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Proceedings of the XIIIth IAGA Workshop on 

Geomagnetic Observatory Instruments, 

Data Acquisition, and Processing 

Edited by Jeffrey J. Love 

Open-File Report 2009–1226

U.S. Department of the Interior 

U.S. Geological Survey 

U.S. Geological Survey Colorado School of Mine

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ience

XIIIth Geomagnetic Observatory Workshop

International Association of Geomagnetism and Aeronomy

Boulder and Golden, Colorado, U.S.A.

June 9–18, 2008
U.S. Department of the Interior 

KEN SALAZAR, Secretary 

U.S. Geological Survey 

Suzette M. Kimball, Acting Director 

U.S. Geological Survey, Reston, Virginia: 2009 

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Love, J.J., ed., 2009, Proceedings of the XIIIth IAGA Workshop on geomagnetic observatory instruments,

data acquisition, and processing: U.S. Geological Survey Open-File Report 2009–1226, 271 p. 

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Contents

Table of Contents .......................................................................................................................................................... iii

Measurement Session ...............................................................................................................................................iii

Scientific Session.......................................................................................................................................................iii

Preface ...........................................................................................................................................................................vi

Group photos of attendees ............................................................................................................................................ix

Measurement Session

Berarducci, A., Woods, A., Absolute Measurement Session XIII IAGA Workshop Boulder Magnetic Observatory ....... 1 

White, T.C., Total Field Sensor Comparison.................................................................................................................. 9

Scientific Session

Adhikari, S., Chandrasekhar, E., Rao, V.E., Pandey, V.K., On the Wavelet Analysis of Geomagnetic Jerks of Alibag 

Magnetic Observatory Data, India ............................................................................................................................... 14

Auster, V., Kroth, R., Hillenmaier, O., Wiedemann, M., Automated Absolute Measurement based on Rotation of a 

Proton Vector Magnetometer....................................................................................................................................... 24

Chau, H.D., Le Mouël, J.L., Truong, F., Telali, K., Models of Magnetic Secular Variations Obtained from 

Vietnamese Magnetic Observatories and Repeat Station Network Surveys from 1997-2003 ..................................... 33

Chulliat, A., Lalanne, X., Gaya-Piqué, L.R., Truong, F., Savary, J., The New Easter Island Magnetic Observatory.... 47 

Chulliat, A., Savary, J., Telali, K., Lalanne, X., Acquisition of 1-second Data in IPGP Magnetic Observatories .......... 54 

Curto, J.J., Marsal, S., Torta, J.M., Sanclement, E., Removing Spikes from Magnetic Disturbances Caused by

Trains at Ebro Observatory.......................................................................................................................................... 60

iii
Delipetrov, M., Delipetrov, T., Panovska, S., Delipetrov, B., Geomagnetic Measurements in 2004 in the Republic of 

Macedonia ................................................................................................................................................................... 67 

Di Mauro, D., Ramdani, F., Fois, M., Alfonsi, L., Preliminary Results from the first Geomagnetic Deep Sounding in 

the Western Sector of the Anti Atlas Region, Southern Morocco ................................................................................. 73 

Fischman, D., Denig, W.F., Herzog, D., A Proposed Metadata Implementation for Magnetic Observatories .............. 82 

Fouassier, D., Chulliat, A., Extending Backwards to 1883 the French Magnetic Hourly Data Series ........................... 86 

Hegymegi, L., Csontos, A., Can we use the dIdD Magnetometer in the Field? ........................................................... 95 

Hemshorn, A., Pulz, E., Mandea, M., GAUSS: Improvements to the Geomagnetic AUtomated SyStem .................. 100 

Hemshorn, A., Auster, H.U., Fredow, M., DI3: Improving DI-Flux measurements with the Three-Component Sensor104 

Hernández-Quintero, E., Cifuentes-Nava, G., Caccavari-Garza, A., Flores-Soto, X., Some Considerations on 

Secular Variation of the Magnetic Field in Mexico ..................................................................................................... 108 

Herzog, D.C., The Effects of Missing Data on Mean Hourly Values .......................................................................... 116 

Kotzé, P.B., Mandea, M., Korte, M., Modelling Southern African Secular Variation Using 2005-2007 Field Survey 

Data ........................................................................................................................................................................... 127 

Linthe, H., Mandea, M., Korte, M., Installing a Geomagnetic Observatory on St. Helena Island – a Special 

Challenge .................................................................................................................................................................. 133 

Madeeha, A., Murtaza, G., Rasson, J.L., Turbitt, C., Geomagnetic Activities in Pakistan since 2006 – to date ........ 146 

Marsal, S., Curto, J.J., Riddick, J.C., Torta, J.M., Cid, O., Ibañez, M., Livingston Island Observatory Upgrade: First 

Results ....................................................................................................................................................................... 154 

Matzka, J., Pedersen, L.W., Maule, C.F., Neska, A., Reda, J., Nilsson, A., Linthe, H., The Effect of High-Voltage 

DC Power Lines on the Geomagnetic Measurements at BFE ................................................................................... 162 

McPherron, R.L., The Utilization of Ground Magnetometer Data in Magnetospheric Physics ................................... 171 

Minamoto, Y., Ongoing Geomagnetic Field 1-second Value Measurement by JMA .................................................. 190 

Newitt, L.R., The Effects of Missing Data on the Computation of Hourly Mean Values and Ranges ......................... 194 

iv 
Nosé, M., Iyemori, T., Takeda, M., Toh, H., Ookawa, T., Cifuentes-Nava, G., Matzka, J., Love, J.J., McCreadie, H., 

Tunçer, M.K., Curto, J.J., New Substorm Index Derived from High-Resolution Geomagnetic Field Data at Low 

Latitude and its Comparison with AE and ASY Indices .............................................................................................. 202 

Panovska, S., Delipetrov, T., Delipetrov, M., Delipetrov, B., Models of the Geomagnetic Field on the Territory of the 

Republic of Macedonia .............................................................................................................................................. 208 

Pulz, E., Jäckel, K.-H., Bronkalla, O., A Quasi Absolute Optically Pumped Magnetometer for the Permanent 

Recording of the Earth’s Magnetic Field Vector ......................................................................................................... 216 

Rasson, J.L., van Loo, S., Berrami, N., Automatic DIflux Measurements with AUTODIF .......................................... 220 

Rasson, J.L., Testing the Time-Stamp Accuracy of a Digital Variometer and its Data Logger ................................... 225 

Riddick, J.C., Rasson, J.L., Turbitt, C.W., Flower, S.M., INDIGO Digital Observatory Project, 2004 – 2008 ............. 232 

Shanahan, T.J.G., Turbitt, C.W., Evaluating the Noise for a Commonly Used Fluxgate Magnetometer – for 1-

second Data .............................................................................................................................................................. 239 

Svalgaard, L., Observatory Data: a 170-year Sun-Earth Connection ........................................................................ 246 

Truong, F., Lalanne, X., Chulliat, A., MAGIS: The Information System of IPGP Magnetic Observatories ................. 258 

Worthington, E.W., Sauter, E.A., Love, J.J., Analysis of USGS One-Second Data ................................................... 262 

Abstracts by Author ................................................................................................................................................ 267 

v 
Preface 

The thirteenth biennial International Association of Geomagnetism and Aeronomy (IAGA) 

Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing was held in the 

United States for the first time on June 9-18, 2008. Hosted by the U.S. Geological Survey’s (USGS) 

Geomagnetism Program, the workshop’s measurement session was held at the Boulder Observatory and 

the scientific session was held on the campus of the Colorado School of Mines in Golden, Colorado. 

More than 100 participants came from 36 countries and 6 continents. 

Preparation for the workshop began when the USGS Geomagnetism Program agreed, at the close of 

the twelfth workshop in Belsk Poland in 2006, to host the next workshop. Working under the leadership 

of Alan Berarducci, who served as the chairman of the local organizing committee, and Tim White, who 

served as co-chairman, preparations began in 2007. The Boulder Observatory was extensively renovated 

and additional observation piers were installed. Meeting space on the Colorado School of Mines campus 

was arranged, and considerable planning was devoted to managing the many large and small issues that 

accompany an international meeting. Without the devoted efforts of both Alan and Tim, other 

Geomagnetism Program staff, and our partners at the Colorado School of Mines, the workshop simply 

would not have occurred. 

Commencing with an outdoor reception on Monday, June 9, workshop participants enjoyed a 

traditional American-style barbeque along with wine contributed by the Townshend Cellar. Guests 

included Suzette Kimball, who was then the Associate Director for Geology but who is now the acting 

Director of the USGS; Robert Doyle, Deputy Director of the USGS; and William Leith, Associate 

Program Coordinator for Geomagnetism. The next day, the measurement session began and continued 

until Friday, June 13. Alan gave a seminar on solar observations; Bill Worthington ran training sessions 

vi 

Proceedings of the XIIIth IAGA Workshop on 

Geomagnetic Observatory Instruments, 

Data Acquisition, and Processing 
for making absolute measurements; Paul Hattori and Andy Woods processed all the measurement data; 

Tim ran the total field comparison sessions; Duff Stewart and Eddie McWhirter demonstrated the USGS 

data acquisition system; Jill Caldwell and Duff ran training sessions for data processing; Tim and Ed 

Sauter gave a tour of the USGS coil calibration system; Jean Rasson demonstrated an automated 

absolute measurement system (p. 220); Jeffrey Love, Jean, and Tim conducted a discussion session on 

1-second data acquisition issues; and William Denig, Sue McLean, and Chris Balch hosted workshop 

participants for a tour of the National Geophysical Data Center and the Space Weather Prediction 

Center at NOAA. The results of side-by-side comparisons of absolute field measurements, made using 

DI Flux (theodolite) and Overhauser and proton-precession magnetometers, are given on p. 1 and 9 of 

these proceedings. The workshop’s measurement session was a busy time, and those involved worked 

hard in making it a success. This part of the workshop is important for ensuring that observatories 

around the world are operated according to a consistent set of modern standards. 

After a weekend that included a mountain tour led by USGS geologists, another barbeque, and free 

time, the three-day scientific session began on Monday June 16. Welcoming words were given by David 

Applegate, the USGS Senior Science Advisor for Earthquakes and Geologic Hazards. The session’s 

first invited speaker, Robert McPherron, professor of space physics at UCLA, spoke on magnetospheric 

physics and observatory data (p. 171). This was followed by contributed oral presentations on 

magnetometer networks, individual observatories, and testing of 1-second data. That evening, a banquet 

dinner was held at a downtown Denver restaurant. On Tuesday, Leif Svalgaard gave an invited talk on 

the analysis of historical observatory data and the Sun-Earth connection (p. 246). Professor John 

Cowdrey, Colorado School of Mines, gave a presentation on the history of electricity distribution in the 

United States, and Tom Lawson gave a presentation on analog to digital technology. Contributed oral 

presentations were given on data management and long-time series, and there was some animated 

discussion about the errors in hourly mean data when one or more minutes of data are missing. We then 

had an extended period of time for poster presentations, which was accompanied by drinks and hors 

d’oeuvres. On Wednesday, Manoj Nair gave an invited talk on oceanic magnetic induction; we had 

contributed oral presentations on the internal field, absolute measurement methods, repeat surveys and 

regional analyses; and Jeff, Chris, and Arnaud Chulliat led a discussion session on the importance of 

real-time dissemination of preliminary magnetic observatory data and the prompt publication of 

definitive data. Madeeha Ashfaque (p. 146) gave a presentation on geomagnetic observatories in 

vii 
Pakistan, and she was later awarded the IAGA Young Scientist Presentation Award, to which we add 

our congratulations! 

The proceedings that follow are a partial record of the workshop. The first two papers record the 

results of the measurement session, and the rest of the papers apply to the scientific session. Many 

presentations, both oral and poster, formed an important part of the workshop, and the Conference 

Program and Abstracts, distributed at the time of the workshop, is record of these. But for reference, a 

list of the abstracts delivered at the workshop is given at the end of these proceedings. As has been the 

case with previous observatory workshops, much fruitful conversation and exchange occurred during 

the measurement session, the field trips, and, even, over the breakfasts, lunches, dinners, and coffee 

breaks that participants shared together. Indeed, the congeniality of the community of people within the 

international community of observatories is impressive. And so, we express special appreciation to 

everyone who participated in the workshop. In particular, we hope that our foreign guests felt welcome 

in the United States. 

We express our thanks to Jill McCarthy, the USGS Central Region Geologic Hazards Team Chief 

Scientist; Carol A. Finn, the Group Leader of the USGS Geomagnetism Program; the USGS 

International Office; and Melody Francisco of the Office of Special Programs and Continuing Education 

of the Colorado School of Mines. We also thank the student employees that the Geomagnetism Program 

has had over the years and leading up to the time of the workshop. For preparation of the proceedings, 

thanks go to Eddie and Tim. And, finally, we thank our sponsors, the USGS, IAGA, and the Colorado 

School of Mines. 

Jeffrey J. Love, Editor 

viii 
Group Photos of Attendees 

IAGA XIII participants on Colorado School of Mines campus in front of Guggenheim building. 

ix 
IAGA XIII participants at Boulder Observatory. 

x 
During IAGA XIII workshop after the tour of NOAA in Boulder, CO. 

xi 
Weekend trip to St. Mary’s Glacier during IAGA XIII. 

xii 
Absolute Measurement Session 

XIII IAGA Workshop 

Boulder Magnetic Observatory 

Alan Berarducci and Andy Woods 

U.S. Geological Survey 

geomag.usgs.gov 

Introduction 

The absolute measurement session of the XIII IAGA Workshop was held at the Boulder 

Magnetic Observatory June 10-13, 2008. Approximately 85 people attended this session. 

The main focus of the session was for observers to make and compare measurements 

using DIFlux magnetometers. The session also included absolute measurement training, 

with lectures and practical training. Also included were data processing training, an 

introduction to solar observations, and a discussion concerning timing for one-second data 

collection. 

Testing and demonstration of three instruments under development was also carried out 

during the absolute measurement session. The auto DI Flux was demonstrated by Jean 

Rasson. A triaxial DI Flux was demonstrated by Uli Auster and Anne Hemshorn. A fast 

delta Declination/delta Inclination (dIdD) magnetometer was tested by Laszlo Hegymegi. 

Results from the Auto DI Flux and triaxial DI Flux are included in the workshop results 

presented below. 

DI Flux Comparison 

Absolute measurements of declination and inclination were made at pillars 1, 2, 3, 4, and 

5. Declination (D), inclination (I) and total field (F) were used to calculate D (declination), H 

(horizontal intensity), and Z (vertical intensity). The location of pillars 1 through 5 (see figure 

1) was chosen after extensive total field measurements to determine the lowest gradients. 

The results from pillars 1 through 5, the auxiliary pillar, and the main pillar were used to 

compute the session baseline and averages. 

 

 Figure 1. Boulder Observatory. 

1
Approximately 27 magnetometers and 47 observers (including 11 USGS) participated in 

the absolute measurement session. The DI Flux magnetometers and observers that 

participated in the absolute measurement session are listed in table 1 below. 

 

No.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

Table 1. List of DIFlux instruments and observers.

Zeiss 010

Auster

Zeiss 020 -616061

Observer

Calp Ziess 010 - 37834 Bartington

Cifuentes

Zeiss 020

Country Instrument

3-axis Fluxgate/ Zeiss

Zeiss 020

Belgium

Australia

Hemshorn

Zeiss 015B

Crosthwaite-Lewis

3-axis Fluxgate/ Zeiss

Deshmukh

Zeiss - 160459 / B0610H

Zeiss 020 - 614379

Rasson 

Camacho

Auto DI

Horacek

Zeiss 020 - 614231

Rosales

Mucusette

Zeiss 010B

Kampine

Zeiss 020 - 614379

Hernandez

Zeiss 020 - 359137

Pedersen

Kadek

Zeiss 020

Lim Zeiss -15963

Kuvshinov

United States

Mozambique

Switzerland

China

Indonesia

Tavistock - V042717

Reda-Neska

Zeiss - 151713 (DMI)

Ruska

Yusuf

Zeiss 010

Minamoto

Zeiss-Elsec 253101

United Kingdom

United States

United States

Zeiss 010

Marsal

Pajunpaa-Tero

United States

Peru

Colombia

Linthe

Matzka

Zeiss 010

Shanahan

Zeiss Theo 010 (DMI)

Zeiss 010 

Schiermeier

Zeiss 010B - 151571

Turbit

Zeiss 020

Mozambique

Orihuela

Zeiss 010

Zeiss Theo 010B / Elsec 810

Pacheco

Zeiss 010

Wu

Zeiss 010

Poland

Peru

United States

Venezuela

United Kingdom

 Serra

Zeiss - 109266

Pakistan

Bayer 

Zeiss 020

United States

Spain

Denmark

Japan

Murtaza

Denmark

Ruska

Zeiss 020 - 359137

 Brown

Zeiss 010

Odell 

Czech Republic

Indonesia

Korea

Germany

 Davie 

CTM-DI 108

United States

India

Germany

Mexico

Uchima 

Bartington/ WildT16

Kaisan

 White 

Germany

Czech Republic

United States

Canada

Venezuela

Mexico

Lopez 

USGS Team United States Zeiss 010 (USGS Main Pillar)

Zeiss 020 - 614379

Finland

Extensive measurements were made to determine the pillar differences in D, H, Z, and 

F. Measurements were made simultaneously on the main pillar, the auxiliary pillars, and 

pillars 1-5 before, during and after the workshop. The adopted D, H, Z, and F differences 

are shown in table 2. 

 Table 2 : D, H , Z and F Differences for Measurement Pillars

Dcorr to Main (min) Hcorr to Main (nT) Zcorr to Main (nT) Fcorr to Main (nT) 

AUX -1.18 -12.64 2.83 -20.70 

Pillar 1 -1.89 -16.52 -11.22 -6.30 

Pillar 2 .42 -9.41 -7.34 -12.70 

Pillar 3 -3.52 -13.66 -6.50 -11.80 

Pillar 4 -3.48 14.64 -14.78 -15.30 

Pillar 5 -.42 -18.76 -4.50 -11.60 

2
The observatory baselines were determined on the main pillar in the Absolute Building. 

Reduction of the measurements was made using the main observatory Narod fluxgate 

magnetometer and GEM Systems GSM-19, USGS model. The stability of these 

magnetometers is approximately 9-12 nT/year. During the month of the workshop, June, 

the stability was between 1 and 3 nT. See figure 2 below. 

 Figure 2. Observed and adopted baseline values for Boulder Observatory, 2008. 

The reference levels (zero level) for D, H, and Z are adopted from the mean value of all 

the measurements made by the session’s participants, excluding measurements outside two 

standard deviations. The reference level for D is 585.85 minutes; H is 30.98nT; Z is 551.87 

nT. Two standard deviations for D is 6.84 minutes; for H is 5.73 nT; for Z is 6.84 nT. 

Conclusions 

The results of the absolute measurement session of D, H, and Z are shown graphically 

in figures 3, 4, and 5 respectively. The results are shown in tabular form in tables 3, 4, and 

5. 

The gray cells in tables 3, 4, and 5 show values which were excluded from the session 

average and individual averages. These values are outside of two standard deviations and 

are shown for information only. 

3
-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Observer

Difference (minutes)

Figure 3. Individual observer difference from workshop average (declination). 

All measurements outside of two standard deviations have been removed. 

 

-7

-6.5

-6

-5.5

-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Observer

Difference (nT)

Figure 4. Individual observer difference from workshop average (horizontal 

intensity). All measurements outside of two standard deviations have been 

removed. 

4
-5.5

-5

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

3.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

Observer

Difference (nT)

Figure 5. Individual observer difference from workshop average (vertical 

intensity). All measurements outside of two standard deviations have been 

removed. 

5
Table 3. Individual difference from workshop average - declination (Min). 

Shaded measurements fall outside of 2 standard deviations and were not used in calculating workshop averages.

Obs 1 Obs 2 Obs 3 Obs 4 Obs 5 Obs 6 Obs 7 Obs 8 Obs 9 Obs 10 Average 

No Obs 11 Obs 12 Obs 13 Obs 14 Obs 15 Obs 16 Obs 17 Obs 18 Obs 19 Obs 20 

1 -0.12 0.11 -0.02 -0.01 0 -0.05 -0.03 -0.09 -0.08 -0.05 -0.037 

-.03 -.09

2 -1.2 -.61 -1.13 -.84 .42 -1.44 -.55 -1.08 -.17 -.61 -.479 

.89 .88 -.85 -.62 -.38 -.81 -.25 -.27 

3 -.15 -.06 -.04 -.083 

4 -.9 -.900 

5 -.62 -.62 -.17 -.07 -.18 -.05 -.28 -.284 

6 .24 -.06 .13 .18 .03 -.06 .13 .18 .096 

7 .26 .260 

8 -.64 -.49 -.26 -.48 -.15 -.06 -.16 -.18 -.303 

9 -.66 -.41 3.26 -.52 .17 .22 .18 .21 -.116 

10 .24 .42 .71 .82 .43 .29 0 .09 .375 

11 -.11 .03 -.040 

12 -.86 -.33 .42 -1 .62 0 -.14 -.09 -.173 

13 .14 .21 -.61 -.43 .04 -.130 

14 1.46 1.15 2.12 .64 1.343 

15 -.38 -.06 -.31 -.01 -.190 

16 .14 .82 -.66 -.34 -.010 

17 -.57 -.570 

18 -.84 -.6 -.720 

19 -.03 2.54 -.030 

20 -24.08 -23.98 -23.82 -23.97 -.09 -.090 

21 .2 .2 .09 .09 .145 

22 -.15 .09 -.030 

23 -.14 -.12 -.13 -.45 -.31 -.24 -.38 -.253 

24 4.22 2.15 .05 -1.1 1.330 

25 -.01 -.13 -.08 .14 .7 .88 .59 .299 

26 -.33 -.33 -.35 -.14 -.288 

27 -.01 .09 .09 .05 -.06 -.09 .08 .021 

28 11.56 3.05 -.35 -.94 .587 

29 .15 1.76 .955 

30 -.06 .03 .18 -.06 .03 -.04 -.060 

31 -.35 -.09 -.12 .14 .27 -.030 

32 -.01 .28 -.1 -.12 .2 .14 .28 .096 

33 .12 .08 .100 

34 -.01 .01 -.96 .1 -.215 

35 -.07 -.14 -.23 -.147 

36 -.27 0 -.14 -.137 

37 -.48 -.2 -.03 -.19 -.225 

38 -.3 -.32 -.07 .03 .11 .28 .08 .24 .006 

39 .94 -.44 -.1 -2.41 1.31 .71 .5 .82 .166 

40 .17 .09 .36 -.14 -10.49 .120 

41 -.03 -.15 -.05 -.04 0 -.06 -.16 -.12 -.12 -.07 -.080 

6
Table 4. Individual difference from workshop average - horizontal intensity (nT). 

Shaded measurements fall outside of 2 standard deviations and were not used in calculating workshop averages.

Obs 1 Obs 2 Obs 3 Obs 4 Obs 5 Obs 6 Obs 7 Obs 8 Obs 9 Obs 10 Aver

No Obs 11 Obs 12 Obs 13 Obs 14 Obs 15 

 -0.55

Obs 16 

 -0.39

Obs 17 

0.01 

Obs 18 Obs 19 

 -0.14

Obs 20 

1 -0.89 -0.8 -1.23 -1.13 -0.41 -0.39 -0.5

.01 

 -.63 -.88 1.05 .4 

 -6.78 -2.12 -3.13 

3 4 .37 .37

 .95 .01 1.85 .22 .13 .22 .13 

-.41

2 -.81 -1.97 -2.56 -3.15 -1.57 -.24 -2.1

-1.33 -3.44 -2.61 -5.34 -3.78

-2.05 -1.92 -2.29 -2.0

5 .76 -1.14 -.35 -.52 

6 1.05 -.88 -.84 -.88 -.84 -.237 18.28

1.13 

 .00

8 -.56 -2.92 -.11 1.68 .63 .78 .02

-.47 

9 2.26 -22.17 -21.71 24.68 1.92 1.37 1.95 1.72 

1.84

10 3.03 3.81 -130.86 3.21 3.58 3.94 3.89 4.1 

11 .85 1.42 

 1.13 3.06 

3.651.13

12 .1 -.62 -1.48 -17.73 -.54 

13 .81 .84 1.3 1.79 1.67 14 1.37 .86 1.39 .56

-.75 .12

1.28

-1.38 

15 -136.62 .65 1 -2.71 -.35

16 75.29 .61 10.28 4.5 

.14 .14

18 1.35 1.65 19 .95 20 .95 .7321 .84 2.58 .91 .95 22 .04 .73 .3823 -.05 .53 .09 .53 .86 

2.55

17 

1.50

-1.18 -.11

.57 .69 .67 .79

1.32

-.02 -.81 .16

24 -6.44 -6.42 2.56 6.71 

25 -3.93 

2.56

-78.52 -.71 -3.33 

26 

27 .86 .15 .95 .80

.46 

-1.39 -4.88 -3.19 -2.9

2.93 1.96 2.75 2.62 2.561.03 .61 .49 1.55 

28 1.64 2.74 851.62

29 .68 .1730 .13 .01 31 .83 .06 1.43 32 -.19 .09 .2 .03 33 .03 .68 .35

1.61

-.33

-.3 -.11 -.41 -.04 -.12

-.55 -2.43 -.13

.26 -.77 .28 -.01

34 -133.51 12.35 .58 .03

.93 .42 .55 .63

36 1.03 .2 .4037 .74 2.24 4 3.33 

-.51

35 

-.02

2.57

38 -19.66 2.01 2.31 1.59 2.3 1.32 .89 .65 

39 -1.21 -4.73 -2.77 40 5.7 3.16 2.12 1.64 41 .54 .8 1.85 .35 .91 .95

1.58

-.02 -.53 -.82 -1.09 -1.63 -1.6

-2.96 1.93

.75 1.77 -.08 .78 1.92 

7
Table 5. Individual difference from workshop average - vertical intensity (nT). 

Shaded measurements fall outside of 2 standard deviations and were not used in calculating work shop averages.

Obs 1 Obs 2 Obs 3 Obs 4 Obs 5 Obs 6 Obs 7 Obs 8 Obs 9 Obs 10 Avera

No Obs 11 Obs 12 Obs 13 Obs 14 Obs 15 Obs 16 Obs 17 Obs 18 Obs 19 Obs 20 

1 0.25 0.8 1.01 0.96 0.2 0.16 0.04 0.22 0.12 0.16 0.36

.04 .22 

2 .53 .46 .42 -.38 1.02 1.27 1.48 .8 -.01 .26 1.06

.68 3.01 .65 1.08 1.7 1.34 2.79 2.12 

3 .76 .53 .54 .610 

4 -.13 -.13

5 19.65 30.45 30.11 -.34 .08 .33 -.65 -.14

6 -.43 .27 .29 -.08 -.24 .27 .29 -.08 .036 

7 -7.49 .000 

8 -.33 .4 1.42 .21 -.65 -.16 -.24 .28 .116 9 -1.18 9.29 9.09 -10.84 -.46 -.24 -.5 -.42 -.56

10 -.82 1.2 56.18 -1.03 -1.74 -1.87 -1.84 -1.95 -1.15

11 -.21 -.59 -.40

12 -.25 .07 -.68 -1.51 .62 7.5 .15 .2 -.20

13 -.56 -.58 -.1 -.39 -.88 -.50

14 1 -.19 .02 -.21 .155 

15 58.2 -.39 1.08 -.48 .070 

16 -32.16 .05 -4.31 -1.79 -2.01

17 -.06 -.06

18 -.51 -.69 -.60

19 -.71 .43 -.14

20 -.74 -.74 -.66 -.73 -.56 -.68

21 -.26 -1.02 -.28 -.27 -.45

22 .05 -.2 -.07

23 .15 .19 -.07 -.23 -.21 -.32 -.41 -.12

24 3.09 3.04 -1.29 -2.79 .513 

25 1.86 33.76 .69 2.54 .72 1.77 1.68 1.54

26 -1.39 -.87 -1.18 -1.07 -1.12

27 -.64 -.48 -.38 -.83 -.3 0 -.35 -.42

28 -.6 .14 -.79 -373.64 -.41

29 -.24 .18 -.03

30 .04 -.07 -.21 .47 .7 .57 .250 31 .16 .95 -.48 .03 -.6 .012 32 -.11 -.3 -.21 .15 -.06 -.01 .01 -.07

33 -.44 -.63 -.53

34 57.4 -4.84 .63 .15 -1.35

35 -.58 -.37 -.43 -.46

36 -.49 -.12 .03 -.19

37 .16 -.48 -1.28 -1.02 -.65

38 8.25 -1 -1.14 -.81 -.67 -.29 -.15 -.07 -.59

39 -.13 .07 .17 .26 .38 1.93 .63 1.16 .559 

40 -2 -.86 -.41 -.24 .11 -.68

41 -.34 -.11 -1.07 -.69 -.46 -.97 -.16 -.15 -.86 -.09 -.49

8
F R O N T M A T T E R

Total Field Sensor Comparison

Timothy C. White

U.S. Geological Survey

geomag.usgs.gov 

B O D Y

Introduction

During the XIIIth IAGA Workshop (hereafter referred to as “the workshop"), several total field comparison measurements were conducted at the Boulder Magnetic Observatory (BOU). The purpose of these tests was to look for errors

within the total field instruments which are considered “absolutes” instruments. The total field measurement is a critical

component of the absolute measurement computation. Samples between two total field sensors, corresponding in time,

were directly compared. Other methods for calibration exist for these sensors, including the use of frequency generators

(Jankowski and Sucksdorff, 1996). This test was not utilized at this workshop. 

Method

The participants were David Kalp of Canada, Hans-Joachim Linthe of Germany, Santiago Marsal of Spain, Kari

Pajunpaa of Finland, and Tim White of the United States. The instruments used were primarily Gem Systems Overhausers (GSM-19). Kari Pajunpaa’s instrument was a Russian Proton magnetometer (PMP-7), and was manually sampled

approximately every 10 seconds. David Kalp’s and Tim White’s instruments were tested after the workshop because of

logistical issues. All tests were performed in July and August 2008. These test systems were compared against the U.S.

Geological Survey BOU Gem Systems GSM-19 Overhauser. Table 1 summarizes the testing specifications for each

participant’s system. 

TABLE 1. Instrument and testing specifications.

Participants Instrument 

make/model Date of test Start time(UTC)

Duration of 

test (s)

Sampling 

rate

(

Samples

ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ Second )

Number of 

samples

David Kalp, Canada Gem Systems,

GSM-19 July 10, 2008 14:25:11 17,788 1 17,788

Kari Pajunpaa,

Finland PMP-7 June 11, 2008 12:57:50 480 1

ÅÅÅÅÅ 10 48

Hans-Joachim 

Linthe, Germany

Gem Systems,

GSM-19 June 11, 2008 13:51:56 36,141 1

ÅÅÅÅ 3

12,046

Santiago Marsal,

Spain

Gem Systems,

GSM-19 June 12, 2008 12:45:37 40,460 1

ÅÅÅÅ 5

8,091

Tim White, United

States

Gem Systems,

GSM-19

August 

15, 2008 00:00:00 86,400 1 86,400

The BOU system that was used in the comparisons as a baseline utilizes a Gem System GSM-19 Overhauser and is

located in an enclosure 25 meters from the Variations Building, on a mechanically stable pier. There is no temperature

control for the sensor enclosure. Ten meters of the sensor cable are located within an underground conduit. The other 15

meters of the sensor cable are located within the Variations Building mounted on the wall. The Variations Building has

9
electric heaters to control the temperature. The thermostat within this building is set to maintain a temperature of approximately 23 °C. Given the high summer temperatures, it is unlikely that the heaters were used during the testing. The BOU

Overhauser is sampled every second, pulse per second (PPS) triggered, and the resulting data was UTC (coordinated

universal time) time stamped.

The participant’s test systems were located at the Coil Calibration facility. The Coil Calibration Facility Buildings are

located about 450 meters from the BOU system, see figure 1. The test sensors were located on a mechanically stable pier

within the Coil Building. The electronics for the test systems were located in the Coil Control Building, with the exception of Kari Pajunpaa ’s system, which was located entirely within the Coil Building. The Coil Control Building is located

~23 meters north of the Coil Building. The Coil Building had some temperature control, but the thermostat for the heating

and cooling system was located within the Control Building and air was ducted from the Control Building to the Coil

Building. All test sensors were placed in the same physical location (±5 cm). Sampling on the GSM-19-test systems was

computer triggered but had varying sampling frequencies, see table 1. Kari Pajunpaa ’s PMP-7 was manually triggered

every 10 seconds.

Figure 1: Aerial view of Boulder Magnetic Observatory.

Data collected by the test systems were directly compared to the corresponding samples collected by the BOU system.

The test systems and BOU system were never accurately time synchronized; therefore, the data streams were shifted about

the time axis to minimize the time synchronization error. Equation 1 models the analysis that was conducted. 

DFHtiL = FbouHti+dL - FtestHtiL (1)

Fbou is the total field measurement from the BOU system. d is the time offset required to sync the BOU system and the

test system. Note that each data set from the different test systems has different values for d. Ftest is the total field measurement from the test system. The pier difference (DF) was plotted and averaged for each system, see Data section. 

10
Data

On initial analysis of the data, several disturbances were noticed. The disturbance levels were -0.1 nT, and lasted about

5-15 minutes. The disturbances were not periodic and had not been observed during magnetometer calibration in the past.

It is believed that the cycling of the heating and air conditioning unit, located at the Coil Control Building, was the source

of the disturbances. A 200-point moving average filter was applied to the test data sets to eliminate the contaminated data

before plotting. Figures 2-6 correspond to the pier difference measurement, in nT, as a function of sample number for

each of the five test systems after the averaging filter was applied. A “zoomed” in view of the data also is presented for

the GSM-19 test systems to present the sample-to-sample chatter characteristic of the test system. For simplicity, only

12,000 data points were displayed for David Kalp’s and Tim White’s test systems. The plots display a slight drift for all

GSM-19 sensors. It should be noted that Santiago Marsal’s system displays a drift characteristic unlike the other GSM-19

test systems. The drift will be discussed in more detail later. Table 2 presents the mean values and standard deviations for

each system. 

Figure 2: All pier difference results for Kari Pajunpaa’s test sensor.

Figure 3: Pier difference results for David Kalp’s test sensor, left: first 12,000 filtered samples, right: 60 samples of data

from start of test.

11
Figure 4: Pier difference results for Hans-Joachim Linthe’s test sensor, left: filtered samples, right: 60 samples of data from

start of test.

Figure 5: Pier difference results for Santiago Marsal’s test sensor, left: filtered samples, right: 60 samples of data from start

of test.

Figure 6: Pier difference results for Tim White’s test sensor, left: first 12,000 filtered samples, right: 60 samples of data from

start of test.

TABLE 2. Pier difference (DF) results.

Participants Mean DF Standard Deviation DF

David Kalp 13.87 nT .032 nT

Kari Pajunpaa 15.32 nT .067 nT

Hans - JoachimLinthe 14.69 nT .024 nT

Santiago Marsal 16.06 nT .062 nT

Tim White 13.58 nT .020 nT

Range in DF 2.48 nT

12
Observations

The total field measurements during the workshop revealed a 2.5 nT variation between the maximum DF and the

minimum DF. Similar measurements were conducted to establish the DF for the absolutes piers used during the workshop.

These measurements were conducted over three months in 2006, using seven different GSM-19 sensor systems. The mean

variation of those measurements was 1-2 nT, which is slightly lower than the 2.5 nT measured for this comparative test. A

gradient survey was conducted at the testing location using a GSM-19 gradiometer. The vertical gradients about the

sensor location were small, 1.16 nT

ÅÅÅÅÅÅÅÅÅÅÅÅ meter . However, the horizontal gradients were very high, as much as 19.8 nTÅÅÅÅÅÅÅÅÅÅÅÅ meter . This

high gradient helps explain the differences observed during the workshop testing as the sensors were only roughly placed

in the same location (±5 cm). Equation 2 relates the positional error to our DF measurement error using the gradient

measurements and the worst possible scenario for sensor placement. This calculation helps explain the observed DF

spread for this test. 

DF (2) error = 0.10 meter * 19.8

nT

ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ

meter

= 1.98 nT

At present, the source of the drift observed by the GSM-19 instruments is unknown, but is being explored in collaboration with Gem Systems. It should be noted that the stated absolute accuracy of the GSM-19 is ±0.1 nT[2]. The internal

heating of the sensor components and electric console components are suspected. Santiago’s instrument is older than the

rest of the test instruments and is suspected to have some electrical components that have a higher temperature coefficient.

This in turn is the suspected cause of the unique drift characteristic displayed by his system. 

Conclusions

Collecting a longer time series of data was very beneficial for this comparative measurement. The results that were

attained had a large spread because of the high gradients but remain useful and will help improve the quality of observatory data. The test also identifies a substantial drift in Santiago Marsal’s instrument. Participation in this test at future

workshops should be encouraged, given the importance of the total field measurement as it relates to the absolutes measurement. 

 References

[1] Jankowski, J., and Sucksdorff, C., 1996, Guide for magnetic measurements and observatory practice: 

International Association of Geomagnetism and Aeronomy, Warsaw 

[2]Gem Systems, Inc., Overhauser Magnetometer, gemsys.ca/PDFDocs/GSM-19%20Overhauser%20v7.0.pdf 

(Accessed Online: October 15, 2008)

13
 

ON THE WAVELET ANALYSIS OF GEOMAGNETIC JERKS OF ALIBAG MAGNETIC 

OBSERVATORY DATA, INDIA 

S. Adhikari1, E. Chandrasekhar1, V. Eswara Rao1 and Vinod K. Pandey2 1

Department of Earth Sciences, Indian Institute of Technology Bombay, Powai, Mumbai-400076, INDIA. 2

BME Group, Bio School, Indian Institute of Technology Bombay, Powai, Mumbai 400076, INDIA. 

ABSTRACT 

Occurrences of Geomagnetic Jerks (GJs) in the decadal 

secular variation data have been understood to be random 

phenomena and show not only a varied spatio-temporal 

behaviour (sometimes occurring as a global feature and 

sometimes as a local one), but also a varied timefrequency localization (i.e., irregular occurrences of these 

high-frequency GJs in time). As a result, conventional 

signal processing techniques fail to facilitate a better 

understanding of the over all characteristics of GJs. 

Wavelet analysis is an effective mathematical tool to 

achieve the time-frequency localization of GJs. In the 

present study, we have applied continuous wavelet 

transformation to the uninterrupted time-series data of 

geomagnetic variations recorded at the Indian magnetic 

observatory, Alibag (ABG), over a period of 70 years 

(1930-2000). We have used different wavelet-families to 

address the following: (i) To identify optimal wavelet(s) 

that are suitable to characterize GJs and (ii) To check the 

presence of global jerks in ABG data that have been 

recorded at most of the worldwide observatories and also 

check the occurrences of local GJs (if any). Our results 

show that the 1968 global jerk could not be resolved well 

in ABG data by most wavelets, while the other global 

jerks that occurred in 1978 and 1992 were fairly well 

resolved. Among the variety of wavelets used, a set of 4 

wavelets viz., Gaus3, Coif1, Coif2, and Sym4 could 

detect the global jerks and also some local jerks that 

occurred in 1943, 1951 and 1960. A set of three other 

wavelets, viz., Meyer, db8 and Morlet, could not detect 

any of the jerks, global or local. 

Key words: Geomagnetic Jerks, Continuous Wavelet 

Transform, Alibag (India). 

1. INTRODUCTION 

Long period geomagnetic variations having periods of 

about one-year and above (except the 11-year solar cycle 

variations) owe their origin to the convection currents 

generated by the dynamo processes taking place within 

the Earth’s liquid outer core. The first time-derivative of 

this field is known as geomagnetic secular variation. 

Examination of past records of geomagnetic data of 

several decades showed some sudden changes (or 

impulses) in the slope of the secular variations, having 

periods of about one year. Such features are known as 

Geomagnetic jerks (GJs). They are conspicuous in the 

East-West component of the magnetic field and are 

believed to intermittently occur sometimes globally 

(observed at most of the world magnetic observatories) 

and sometimes locally (observed at only a few 

observatories). The global jerks are reported to have 

occurred during the years, 1969-70, 1978-79 and 1991-92 

and the local jerks during 1901-02, 1913-14, 1925-26, 

1932-33, 1942-43, 1949-50 and 1999-2000. It is 

important to note here that the terms ‘global’ and ‘local’ 

are purely relative and it is not clear; how global is 

global? And how local is local? Such irregularly 

occurring and spatially varying geomagnetic phenomena 

have motivated a lot of interest in further understanding 

of their overall characteristics. 

Origin of GJs has been the subject of a lot of debate. 

Employing Spherical Harmonic Analysis (SHA) 

technique, Malin and Hodder (1982) reported that the 

GJs are of internal origin. Later, claiming some loopholes 

in the SHA of Malin and Hodder, Alldredge (1984) 

argued that the GJs are of external origin. However, 

further studies by Gavoret et al. (1986), Nagao et al. 

(2002) and Bloxham et al (2002) showed the pronounced 

internal origin of GJs. Gavoret et al. (1986) separated the 

internal and external field components using 

geomagnetic indices and found a negligible influence of 

external sources on GJs. Nagao et al. (2002) applied 

statistical time series modeling to the monthly means of 

geomagnetic data and discussed the non-influence of 

external currents (Field Aligned Current and Ring 

Current) on GJs. Bloxham et al. (2002) explained that the 

GJs arise due to the combination of a steady flow and a 

simple time-varying, axisymmetric, equatorially 

symmetric, toroidal zonal flow of the core fluid. They 

also explained that the short duration of GJs is due to the 

differential fluid flow at the surface of the Earth’s outer 

core, strongly signifying their internal origin. Studies of 

internal origin of GJs have led to understanding of their 

role on the lower mantle conductivity (Acache et al, 

1980; Ducruix et al, 1980; Backus, 1983; Alexandrescu 

et al, 1999). 

Most above studies required a priori assumption of the 

presence of GJs in the data. The GJs noted above clearly 

indicate that their occurrences are not periodic and that 

they occur both globally and locally. This implies that the 

conventional signal processing techniques fail to 

determine their time–frequency localization. Wavelet 

analysis on the other hand is a useful mathematical tool, 

which can determine the frequency of occurrence of 

14
these geomagnetic impulses as a function of time. In 

other words, it helps to determine the time-frequency 

localization of GJs without any prior assumption of their 

presence in the signal. Exploiting this concept, 

Alexandrescu et al. (1995) first detected GJs in European 

magnetic observatory data. They designed their own 

wavelet, which is an approximate of the third derivative 

of Gaussian function. In the present study, we have done 

two independent exercises: First, we have attempted to 

determine optimum wavelet(s) that can best characterize 

global GJs. Second, we have examined the data of Indian 

magnetic observatory, Alibag (ABG), for the presence of 

world-wide reported jerks as well as local GJs (if any). In 

these two exercises, while the former requires an a priori

assumption of occurrence of GJs in the analyzing signal, 

the latter does not. Detection of GJs depends on the 

degree of closeness of the shape of the analyzing wavelet 

(also known as mother wavelet) to that of the GJ itself. 

Different wavelets have different shapes and thus not all 

wavelets can identify GJs. We have used different 

wavelet-families to find out the optimal wavelet(s) that 

may best characterize the global and local geomagnetic 

jerks. The present study is first of its kind in Indian 

geomagnetic research. In the following sections, we 

sequentially describe the data and their initial processing, 

details of wavelet analysis, results and conclusions. 

2. DATA AND PROCESSING 

Geomagnetic jerks are clearly evident in the East-West 

(EW) component of the magnetic field. Perhaps this is 

because, the influence of extra-terrestrial currents is 

relatively less on the EW component compared to that on 

the North-South (NS) and vertical components (McLeod, 

1992). Hourly mean values of EW component of the 

magnetic field variations recorded during 1930-2000 at 

ABG (see Fig.1a for geographical location) have been 

obtained from WDC-C2 for Geomagnetism, Kyoto 

University, Japan. Data available in units of minutes 

were first converted to nT. From these, the daily means 

and then the monthly means were calculated by 

averaging the available number of days’ data with equal 

number of days in that month. Monthly mean values have 

been used for the present study. At some places, 

wherever the whole month’s data were missing, they 

were interpolated using the average of the previous and 

next month’s values. Also occasional instrument noise 

and base line shifts (that usually result in the form of 

box-like jumps or step-like jumps) in the data were 

manually corrected prior to further analysis. However, 

such errors were found to be only a few in the whole 

length of data sequence. Thanks to the concerned staff at 

ABG for producing such a high quality data. Fully 

processed data are shown in Fig 1b. 

3. WAVELET ANALYSIS 

The wavelet function, ψ(t), signifying the ‘timefrequency’ localization is defined by (Mallat, 1999) 

⎟

⎠

⎞ ⎜

⎝

⎛ − = s

t

s

t s

τ

ψ τ ψ

1 )( , . 

where s > 0 indicates the scale and τ indicates the 

translation parameter. Here, s is analogous to frequency, 

in the sense that higher scales (low frequencies) provide 

details of long-wavelength features of the signal and 

lower scales (high frequencies) provide details of the 

short-wavelength features of the signal. The translation 

parameter, τ, refers to time information in the 

transformed domain. The function ψ(t) is called 

‘analyzing wavelet’ or ‘mother wavelet’. More details 

about the fundamentals of wavelet theory can be found in 

Daubechies (1992), Mallat (1989; 1999) and references 

therein. 

In wavelet transformation, the signal to be transformed is 

multiplied with the mother wavelet and the 

transformation is computed for different segments of the 

data by varying τ and s. The wavelet transformation in 

which, τ and s will be continuously varied is called 

Continuous Wavelet Transformation (CWT) and the 

transformation in which, both τ and s will be varied as 

power of an integer ‘n’ (i.e., n

j

, j = 1, 2, 3,..., k) is called 

Discrete Wavelet Transformation (DWT). Generally in 

DWT, dyadic scales and translations are used, in which 

case, n = 2. The distinction between CWT and DWT is 

that the former is linearly translational invariant. That 

means an amount of ‘τ’ time shift in the wavelet function 

results in the same amount of shift in the transformed 

signal. As a result, transients can be effectively picked up 

by performing the computations at chosen variations in 

translations and scales. Whereas DWT is not linearly 

translational invariant. That means, in DWT, the 

translation in the input signal produces a translation in 

the output, only if the former is a multiple of the 

corresponding dyadic translation and scales of wavelet 

function1

. So, if we apply DWT in the present study, 

there is a danger of skipping the whole signature of the 

jerk in such dyadic steps of translation and scale. 

Therefore, DWT is not suitable for the present study. 

In analogy with the definition of inner product of two 

functions f(t) and g(t), given by 

∫ ⋅=〉〈 R )()()(),( dttgtftgtf *

 

1

In such a case, that becomes ‘dyadic wavelet transformation’, 

which is always translational invariant. 

15
Fig 1 : (a) Geographical location of the Indian magnetic observatory, Alibag (ABG) and (b) The geomagnetic E-W 

component data (in nT) from 1930 to 2000. 

Fig 2: Contour plot of CWT coefficients (scalograms) computed using the data shown in Fig. 1b for different wavelets. In 

the scalogram plots, the dark (light) colour represents low (high) coefficient values. The semi-vertical lines beneath 

each scalogram plot designate the associated lines of maxima (LoM) curves corresponding to the respective wavelet. 

16
where )()(),( 2 ∈ RLtgtf . The CWT of the wavelet 

function ψ(t) and signal to be transformed, f(t), can be 

defined as 

dt

s

t tf

s

sCWT ∫ ⎟

⎠

⎞ ⎜

⎝

⎛ − ⋅= τ

τ )( ψ

1 ),( (1) 

Equation (1) explains that the wavelet transformation 

gives a measure of the similarity between the signal and 

the wavelet function. Such a measure at any particular 

scale s0 and translation τ0, is identified by wavelet 

coefficient. The larger the value of this coefficient, the 

higher the similarity between the signal and the wavelet 

at (τ0, s0) and vice-versa. 

CWT was performed on the fully processed data using a 

variety of wavelets. Fig. 2 shows the contour plot of 

absolute CWT coefficients (called scalogram), 

corresponding to Gaus3, Coif1, Coif2 and Sym4 

wavelets. 

In the scalogram plots, the dark (light) colour represents 

low (high) coefficient values. In the plates beneath each 

scalogram, the semi-vertical lines designate Lines of 

Maxima (LoM). They are the curves formed by joining 

the points of local maxima at different scales. Meignen et 

al (2005) explained that the time localization of transients 

is better estimated with the LoM data than with the 

wavelet transform coefficients. These LoM signify 

discontinuities present in the data and that their origin at 

the lowest scale designates the time location of the 

occurrence of the discontinuity in the data. The paradox 

is, the geomagnetic jerks are represented by 

discontinuities, but not all discontinuities may represent 

jerks. Mallat and Hwang (1992) first described the 

wavelet transform modulus maxima method to detect the 

singularities in data. Alexandrescu et al (1995) by 

considering GJs as singularities and defining them as 

some α th derivative of the signal (α being the 

regularity of the singularity), detected GJs from long 

period geomagnetic data by computing the coefficient of 

a liner regression between the logarithm of the absolute 

value of the CWT coefficient along the LoM and the 

logarithm of the scale, by modifying Mallat and Hwang’s 

method. In the present study, we have followed 

Alexandrescu et al’s approach. We give below a brief 

description of the same. 

Since GJs manifest the second-time derivative of the 

geomagnetic field, they can be represented as some α th

derivative of the signal. So, the time-varying magnetic 

field, f(t) can be expressed as 

 (2) 

Where h(t) is the long-period harmonic component, n(t) 

is the noise term as a function of time. The factor A(t+ t0) 

with the condition 

defines the discontinuity in the signal centered around t0

having an amplitude A. Since the discontinuity is 

included in the signal (equation (2)), wavelet 

transformation can detect it and can also tell about the 

time of its occurrence. The above condition explains the 

causal nature of the occurrences of GJs, since each GJ is 

treated as an independent event. 

Performing wavelet transformation on equation (2) and 

doing some subsequent simple mathematical 

calculations, we can obtain a linear regression between 

the logarithm of the absolute value of wavelet transform 

coefficients along the LoM and the logarithm of scale. 

Mathematically it is expressed in a simple form as 

2 τ sCWT =α 2 s)(log|)),((|log + Constant (3) 

where s > 0 is the dilation (scale) parameter. Equation (3) 

defines the regularity,α of the geomagnetic jerk. Since 

the GJs manifest the second-time derivative of the 

geomagnetic field, the computed α values should be 

close to 2. For full mathematical details of derivation of 

equation (3), the reader is refereed to Alexandrescu et al 

(1995) and the references therein. 

Equation (3) implies that the log-log plot of absolute 

value of the wavelet transform coefficient and the 

dilation should be a straight line with the slopeα . 

However, in practice, because of the noise present in the 

data, the curves slightly deviate from their straight line 

behaviour. We have evaluated equation (3) using 

individual LoM data of Fig. 2, corresponding to each 

wavelet and determined whether the LoM under study 

represented a GJ or not. For a LoM to designate a GJ, 

typically, the α values should be close to 2. According 

to Mallat and Hwang (1992), the essential condition for a 

wavelet to detect a singularity in a given data set is that 

the number of its vanishing moments should be greater 

thanα . A brief description of all the wavelets used in the 

present study and their properties are given in Appendix -

A. 

Results of the present study show that among the variety 

of wavelets used, a set of four wavelets, viz., Gaus3, 

Coif1, Coif2 and Sym4 showed a close linear 

relationship between the logarithms of the absolute value 

of the wavelet transform coefficient and the scale 

corresponding to 1968, 1978 and 1992 jerk events (Fig. 

3). These four wavelets have also detected a few local 

jerks in ABG data that occurred in 1943, 1951 and 1960.

)()()()( 0 +++= tnthttAtf α

⎩

⎨

⎧

+

=+ α

α

)(

0

)(

0

0 tt

tt

0

0

tt

tt

>

≤

17
Fig 3: Plots depicting the linear relationship between the logarithm of the absolute value of the CWT coefficient and the 

logarithm of the scale corresponding to the global jerks of 1968, 1978 and 1992 computed using the respective LoM 

data of each wavelet using equation (3). The linearity in the curves appears to be fairly good beyond the dilation 

values ranging between 24

 and 25. Below this dilation range, the data are believed to have been affected by external 

noise. Note the poor resolution of the 1968 global jerk by Coif1, Coif2 and Sym4 wavelets. This is a clear 

manifestation of the fact that a wavelet that can detect a discontinuity in a given data, may not be able to detect 

others (if present) in the same data. See Appendix-A for shapes of these wavelets and their properties. 

Fig 4: Same as Fig. 3, but, for the local jerks that occurred in 1943, 1951 and 1960.

18
Fig 5: (a) Scalograms and lines of maxima plots for ABG data determined for the three wavelets, viz., Meyer, Db8 and Morlet, which d

global or local. (b) Curves generated using equation (3) for all the global and local jerks using the above three wavelets. See Apwavelets and their properties. 

19
The nature of these jerks together with their estimated α

values is shown in Fig. 4. We have found another set of 

three wavelets, which could not detect any of the jerks, 

global or local. They are Meyer, db8 and Morlet 

wavelets. The scalograms and the respective LoM of 

each of these wavelets are shown in Fig. 5a and the 

behaviour of these jerks in accordance with equation (3) 

is shown in Fig. 5b. The α values shown in each plate 

in Figs. 3, 4, and 5b are calculated by fitting a straight 

line to the linear portion of the respective curves. 

4. RESULTS AND DISCUSSION 

We discuss below two main issues concerning our study, 

viz., (i). The choice of mother wavelet and (ii) the 

detection of global and local jerks in ABG data. 

In the first place, one can question the crude method of 

applying many wavelets to the data to determine the 

optimum ones that can best characterize the jerks. Isn’t 

there any refined procedure to achieve the desired 

objective? The answer is a straightforward no. 

Although, there are a number of wavelets that have 

different properties and yet can satisfy the essential 

mathematical criteria (admissibility2

 and regularity3

conditions) for them to be defined as wavelets, there are 

only a few, which can detect singularities in a given data 

set. In other words, not all wavelets can detect GJs. 

Further, even among those few that can detect 

singularities, it is possible that a wavelet, which can 

detect a singularity in the given data, may fail to detect 

another one (if exists) in the same data (Fig. 3). 

Secondly, by observing the shape of the wavelet and its 

properties, it is difficult to ascertain whether a chosen 

wavelet will be useful for the intended study or not. For 

example, the Morlet wavelet, which is found to be very 

effective in delineating the depths to the top of the 

hydrocarbon zones when applied to geophysical well-log 

data (Choudhury et al., 2007) is found to be not suitable 

to identify the jerks in the present study (Fig. 5). 

Therefore, we suggest that unless one is designing one’s 

own wavelet, it is advised to test all the available 

wavelets to choose an optimum wavelet suitable for 

one’s analysis. Such a formidable exercise is 

unavoidable, if we have to obtain a good time-frequency 

resolution for the singularity under investigation. 

Although various workers have discussed several 

methods of choosing optimum wavelets to analyze 

signals of their interest (see for e.g. Ahuja et al., 1995; 

Kumar and Foufoula-Georgiou, 1997; Nenadic and 

 

2

Admissibility condition implies that the wavelet must be 

oscillatory and that its average value in time domain must be 

zero. 

3

Regularity condition explains that the wavelet must be 

localized in time and thus it must be finite in length. 

Burdick, 2005), their applicability needs to be tested on 

individual data sets and thus an apt choice of wavelets 

could be made only on case-by-case basis. In other 

words, the choice of wavelet is dictated by the objectives 

of the study. 

In the LoM plots of Fig. 2, the origin of the LoM at the 

lowest scale designates the times of occurrences of the 

discontinuities in the data. It is interesting to observe that 

the LoM corresponding to the identified global and local 

jerks are distinctly separate and that they do not overlap 

with the adjacent ones except at the ends, which is due to 

edge effects of the signal. 

At a first glance, one can observe from Fig. 3 that, of the 

three global jerks occurred in 1968, 1978 and 1992, only 

the latter two could be resolved fairly well by a set of 

four wavelets, Gaus3, Coif1, Coif2 and Sym4, while the 

former is resolved only by the Gaus3 wavelet. This is a 

clear manifestation of the fact that a wavelet that can 

detect a singularity in a given data, may fail to detect 

others (if present) in the same data. The ability for a 

wavelet to detect a singularity in a given data depends on 

the degree of closeness of the shape of the wavelet to that 

of the singularity under investigation. The fact that a 

wavelet could detect a singularity in a given data and 

fails to detect another in the same data implies that the 

shapes of the different singularities present in the data are 

different. This poses a question: Do the naturally 

occurring geomagnetic jerks possess different shapes at 

different times of their occurrences? We attempt to 

answer this question in our further study on GJs. Further, 

as shown in Fig. 4, the above set of four wavelets could 

well resolve the local jerks occurred in 1943, 1951 and 

1960 and that their α values, on the whole, 

corresponding to these local jerks have been consistent. 

The curves shown in Figs. 3 and 4 tend to be linear 

(except those of the 1968 global jerk in Fig. 3 

corresponding to Coif1, Coif2 and Sym4 wavelets) 

beyond the dilation (S) value of 16-32 (i.e., 24

 to 25) and 

below this value the curves are not linear. This marks the 

transition between the part of the curve dominated by 

external noise (corresponding to s < 24

) and the part 

dominated by the GJ (s > 24

). If the external noise is 

dominant, then the transition value on the scale axis will 

be high and the resultant regularity will be less accurate. 

However, for a couple of curves in Fig. 4 these transition 

values on the dilation axis are less than 24

. This may 

possibly suggest that the wavelet may be averaging out 

the external noise during the CWT process. Further, in 

Figs. 3 and 4 the linear nature of the curves at higher 

dilations is less perfect, and thus some kinks are observed 

in the linear portion of the curves in the dilation range 24

to 25

. We are not sure, if that could be due to the presence 

of strong long-period external noise in the data. Further, 

it is also important to note that the α values estimated 

20
from equation (3) are not the intrinsic property of the jerk 

itself, but also depend on the wavelet used. 

The curves corresponding to the LoM originating at the 

years 1943, 1951 and 1960 clearly show a well-defined 

log-linear nature (Fig. 4). Since global jerks have not 

been reported to have occurred in these years, we believe 

that these may be local jerks. In 1949, a local jerk has 

been reported to have occurred in Pacific and American 

regions (Michelis and Tozzi, 2005). Based on the results 

of the present study, we are unable to ascertain, weather 

the 1951 jerk seen in ABG data, which occurred after a 

time lag of two years, is a continuation of the 1949 jerk 

seen elsewhere, or is a completely independent one. If the 

former were to be true, then such late appearances of 

jerks have earlier been reported to be due to mantle 

conductivity filtering effect (Backus, 1983). However, 

further studies are currently in progress to have a correct 

understanding of the nature of these local jerks. 

Interestingly, we have found another set of three 

wavelets, viz., Meyer, db8 and Morlet, which did not 

detect any of the global or local jerks. The plots 

generated using equation (3) corresponding to these 

wavelets for the above reported global and local jerks are 

shown in Fig. 5b. At present we only believe that the 

reason for failure of these wavelets to detect the jerks 

could be due to the poor coherency between the shapes of 

these wavelets and that of the global and local jerks. 

Further studies are currently being pursued to quantify 

such observations. 

5. CONCLUSIONS 

Wavelet analysis is a powerful tool to identify 

discontinuities like geomagnetic jerks in the decadal 

variations of geomagnetic data. It is very effective in 

determining the spatial and temporal characteristics of 

geomagnetic jerks. Its uniqueness also lies in its ability to 

determine the time-frequency localization of 

geomagnetic jerks without any prior assumption of their 

presence in the signal. However, the difficult task in 

wavelet analysis is to determine the choice of mother 

wavelet. Most studies adopt the trial and error method to 

decide on the choice of wavelet (Ahuja et al., 1995). Our 

investigations confirm that the choice of the wavelet is 

governed by the objectives of study. Also, for the first 

time, we have identified the presence of local jerks in 

ABG data that occurred during the years 1943, 1951 and 

1960 and these are the first results on geomagnetic jerk 

phenomena using Indian data. Among the limited number 

of wavelets used for the present study, we found that 

Gaus3, Coif1, Coif2 and Sym4 wavelets could fairly 

resolve the presence of global jerks (occurred in 1968, 

1978 and 1992). The same set of wavelets could also well 

resolve the local jerks present in the ABG data. We also 

have found a set of three other wavelets, viz., Meyer, db8 

and Morlet, which could not detect any of the jerks, 

global or local. Further investigations are currently in 

progress to address some of the important issues 

concerning geomagnetic jerk phenomena discussed 

above and understand them in a broader perspective. In 

the envisaged study, we propose to use more wavelets 

and use global and other Indian magnetic observatory 

data sets to characterize and help improve the 

understanding of the GJ Phenomena both on global and 

local scales. A long-term goal of these ongoing studies is 

to estimate the constraints on the lower mantle 

conductivity below the Indian plate through the analysis 

of geomagnetic jerks. 

ACKNOWLEDGMENTS 

The present work forms a part of the ongoing study, 

concerning the multi-objective wavelet analysis of global 

magnetic observatory data. EC thanks Indian Space 

Research Organization (ISRO) for providing the financial 

support through a research grant through their 

RESPOND programme. SA and VER thank IIT Bombay 

for the post-graduate fellowship. The authors thank the 

anonymous referee for his/her meticulous review and 

providing some valuable inputs to improve the quality of 

the paper. EC thanks Ms. Trupti Gurav for her help in 

type-setting the manuscript. 

REFERENCES 

Acache, J., Courtillot, V., Ducruix, J. and Le Mouël, J.-

L., The late 1960's secular variation impulse: 

Further constrains on deep mantle conductivity, 

Phys. Earth Planet. Inter., 23, 72-75, 1980. 

Ahuja, N., Lertrattanapanich, S., and Bose, N.K., 

Properties determining choice of mother wavelet, 

IIE Proc.-Vis. Image Process., 152, 5, 1995. 

Alexandrescu, M., M., Gibert, D., Hulot, G., Le Mou ̈el, 

J. L. and Saracco, G., Detection of geomagnetic 

jerks using wavelet analysis, J. Geophys. Res., 

100, 12557–12572, 1995. 

Alexandrescu, M, Gibert, D, Le Mouël, J.L., Hulot, H 

and Saracco, G., An estimate of average lower 

mantle conductivity by wavelet analysis of 

geomagnetic jerks, J. geophys. Res., 104, 17735-

17745, doi: 10.1029/1999JB900135, 1999. 

Alldredge, L. R., A discussion of impulses and jerks in 

the geomagnetic field, J. Geophys. Res., 89, pp. 

4403–4412, 1984. 

21
Backus, G.E., Application of mantle filter theory to the 

magnetic jerk of 1969, Geophys. J. R. Astr. Soc., 

74, 713–746, 1983. 

Bloxham, J., Zatman, S., Dumberry, M., The origin of 

geomagnetic jerks, Nature, 420, 65-68, 2002. 

Choudhury, S., Chandrasekhar, E., Pandey, V.K. and 

Prasad, M., Use of wavelet transformation for 

geophysical well-log data analysis, IEEE Xplore, 

Proc. of the 15th Int. Conf. on Digital Signal 

Processing, 647-650, doi 

10.1109/ICDSP.2007.4288665, 2007. 

Daubechies, I., Ten Lectures on Wavelets, Society for 

Industrial and Applied Mathematics, (SIAM) 

Philadelphia, 1992. 

Ducruix, J., Courtillot,V., and Le Mouël, J.-L., The late 

1060's secular variation impulse, the eleven year 

magnetic variation and the electrical conductivity 

of deep mantle, Geophys. J. Roy. Astron. Soc., 61, 

73-94, 1980. 

Gavoret, J., Gilbert, D., Menvielle, M., and Le Mouël, J. 

L., Long-term variations of the external and 

internal components of the Earth’s magnetic field, 

J. Geophys. Res., 91, 4787-4796, 1986. 

Kumar, P. and Foufoula-Georgiou, E., 1994, Wavelet 

analysis in Geophysics: An introduction, in 

“Wavelets in Geophysics”, Eds: Foufoula 

Georgiou, E., and Kumar, P., Academic Press. 

 Malin, S. R. C. and B. M. Hodder, Was the 1970 

geomagnetic jerk of internal or external origin?, 

Nature, 296, 726–728, 1982. 

Mallat, S., A theory for multiresolution signal 

decomposition: The wavelet representation, IEEE 

Transition on Pattern analysis and machine 

intelligence, 11, 7, 674-693, 1989. 

Mallat, S., A wavelet tour of signal processing, 2nd Ed., 

Academic Press, Elsevier Publication, 1999. 

Mallat, S., and Hwang, W.L., Singularity detection and 

processing using wavelets. IEEE Trans. Inf. 

Theory, 38, 617-643, 1992. 

McLeod, M. G., Signals and noise in the magnetic 

observatory annual means: Mantle conductivity 

and jerks, J. Geophy. Res., 97, 17261-17290, 

1992. 

Meignen, S., Achard, S. and Gumery, P.Y., Time 

localization of transients with wavelet maxima 

lines, IEEE Trans. Sig. Proc., 53, 2251-2258, 

2005. 

Michelis, P.D. and Tozzi, R., A Local Intermittency 

Measure (LIM) approach to the detection of 

geomagnetic jerks, Earth Planet. Sci. Lett. 235, 

261–272, 2005. 

Nagao, H., Iyemori, T., Higuchi, T., Nakano, S., and 

Araki, T., Local time features of geomagnetic 

jerks, Earth Planets Space, 54, 119-131, 2002. 

Nenadic, Z. and Burdick, J.W., 2005, Spike detection 

using the continuous wavelet transform, IEEE 

Trans. Biomedical Engg., 52, 1, doi: 

10.1109/TBME.2004.839800. 

APPENDIX – A 

Basic definitions of some properties of the wavelets used 

in the present study – A quick reckoner. For more 

mathematical details of these and other wavelets, the 

reader is referred to Mallat (1999), Kumar and Foufoula 

Georgiou (1994) to cite a few 

Symmetry: The symmetry property of wavelets explains 

that the wavelet transform (WT) of the mirror (m) of a 

signal is mirror of the wavelet transform of the signal. 

i.e., { [ )( ]} = { [ ] tfWTmtfmWT )( }. 

Compact support: This property explains that the wavelet 

vanishes outside a finite interval. The shorter the interval 

is, the compact the wavelet is. 

Orthogonality: This property implies that if it is possible 

to construct some wavelets ψ(t), such that )( , t ψ vu are 

orthonormal, i.e., ∫ ',', = '' )()( vuvu vvuu ψψ dttt δδ , 

whereδ , ji is the delta function defined as 

then, this implies that such wavelets are orthogonal to 

their “translates” and “dilates”. 

Vanishing Moments: A wavelet, ψ(t) has “N” vanishing 

moments, if the Fourier transform of the wavelet at the 

origin, is k times continuously differentiable. i.e., 

ωψ == 0)0(

ωk

k

d

d

, for k=0, 1,... , N-1. 

22
* The horizontal and vertical axes for the wavelet shapes are in arbitrary units.

 

Name of the 

Wavelet 

Shape of the 

Wavelet * 

 

Symmetry 

Compact 

Support Orthogonality 

Vanishing 

Moments 

Gaussian 3 Anti-Symmetric No No Arbitrary 

Coiflet 1 

Near 

Symmetric Yes Yes 2 

Coiflet 2 

Near 

Symmetric 

Yes Yes 

4 

Symlet 4 

Near 

Symmetric Yes Yes 4 

Meyer Symmetric No Yes Arbitrary 

Daubechies 8 No Yes Yes 8 

Morlet Symmetric No No Arbitrary 

23
Automated Absolute Measurement based on Rotation 

of a Proton Vector Magnetometer 

Volker Auster, Ronald Kroth, Olaf Hillenmaier, Markus Wiedemann 

Magson GmbH, Carl Scheele Strasse 14, 12489 Berlin, Germany, email: office@magson.de

Abstract 

In order to perform an absolute measurement, the coil system of the vector proton 

magnetometer (PVM) has been equipped with telephoto lens and CCD camera. The 

orientation of the PVM axis is determined by image processing which identifies the target. 

Collimation errors between coil axis and optical axis are eliminated by rotation of the coil 

during services. To separate between horizontal components and vertical component the 

PVM is rotatable about its vertical axis. Measurements are taken each 30° which allows a 

harmonic analysis of the readings to eliminate alignment errors versus vertical axis. The 

angular position of the worm gear driven PVM is measured by a rotary encoder. Compared to 

automation approaches presented at the previous IAGA workshop in Belsk, our system is 

based on one rotation only. This makes automation of the absolute measurement less 

complex. Design, limitations and test results will be presented. 

1. Introduction 

Absolute measurements have to provide magnetic field data in terms of absolute 

physical basic units or universal physical constants and in a geophysical reference frame. All 

systematic instrument errors have to be eliminated by the measurement procedure. Today, 

absolute measurements are done by the DI-flux method (Kring Lauridsen, 1985). A single 

axis fluxgate magnetometer mounted on a non-magnetic theodolite is used to measure the 

direction of the geomagnetic field in a reference system given by the vertical and one 

azimuthal direction. The field magnitude is measured by a proton magnetometer. The DI-flux 

measurement is carried out by hand and requires well trained personnel. To reduce intervals 

between absolute measurements, to become independent on personnel and to take absolutes at 

remote station, various attempts are done to automate the absolute measurement. 

Jean Rasson was the first one to automate the DI Flux procedure [Van Loo and Rasson, 

2007]. The orientation of two axis perpendicular to the total field are determined by using a 

single axis fluxgate magnetometer as zero indicator. Angles are measured by incremental 

encoders, the azimuth direction is fixed by a laser-mirror setup and the inclination reference is 

given by a tilt meter. Piezo-motors move the single axis fluxgate magnetometer, similar to the 

DI-Flux procedure, into 8 positions (4 in the horizontal plane to determine D and 4 in the 

plane normal to D) in order to eliminate the deviation between sensor and reference axis and 

the sensor offset. Beside the realization of a non magnetic drive unit, the technical challenge 

of this automation approach is the development of a two axis turn table inclusive angle 

measurement, which have the same precision as the non-magnetic theodolites used for the 

manually performed DI-Flux measurements. 

The automation approach of the GFZ/TU-BS team is done by rotating a three 

component fluxgate magnetometer about two reference axes [H.U. Auster et al, 2007]. The 

procedure requires a set of sensor rotations to determine 9 instrument errors (offset, scale 

value and non orthogonality) but has the advantage that the rotation angles have to be known 

only with low precision and that the orientation of the sensor can be arbitrary. The direction of 

the two measurement axes is determined by a laser beam which requires two azimuth marks 

equipped with light sensitive detectors. Due to the small operating distance of laser beams this 

automated system is very sensitive to the stability of the location of both, instrument and 

reference marks. 

In our automation approach we combine the vector proton magnetometer with a 

telescope which shall be used to determine the measurement direction with respect to an 

24
azimuth mark. The usage of a vector proton magnetometer instead of a fluxgate 

magnetometer has the advantage to measure components along an axis defined by a solenoid 

free from offset and scale value errors and to provide the total field exactly at the position of 

the absolute measurement. The telescope equipped with CCD and image processing is able to 

detect arbitrary azimuth marks also at large distances. The field components H,Z and D are 

derived by a motor-driven rotation about the vertical axis. The rotation is further used to 

eliminate level errors. Instrument characteristics which are not subject to thermal and 

temporal drift as the alignment of coil and optical axes can be eliminated by rotation of the 

bias coil during service intervals. 

Fundamentals, error sources and technical realization of this automation approach are 

described in the following chapters in detail. 

2. Basic Principle 

The use of a vector proton magnetometer for absolute measurement is based on long 

term experiences in Germany. Between 1980 and 1990 a PVM with a special cylindrical bias 

field coil was developed at Niemegk Observatory (see Figure 1a). To perform a rotating about 

the vertical axis for the measurement of declination D, horizontal field H and vertical field Z, 

the cylindrical coil was fastened on the top of a non-magnetic ZEISS-theodolite. The Zeiss 

theodolite allows a rotation about the vertical and one horizontal axis with a precision of arcseconds. The misalignment δ between magnetic coil axis and horizontal plane can be assumed 

as small, because the Zeiss theodolite supports (in a narrow range) leveling by suspension. 

The field measured by the PVM can be expressed by: 

() ( ) α cos α −= + ZDHH sinδ (1) 

whereas α is the horizontal angle versus magnetic east and δ the inclination of the coil system 

versus the horizontal plane. The observer turns the PVM about the vertical axis and takes 

measurements at maximum (Hmax) and minimum (Hmin) field values. The total field F is 

measured by the PVM if no bias field is applied. With this input the field components H and Z

as well as the inclination of the coil axis can be calculated by the following three equations: 

H HH = +

2

max min , sinδ

2

max min Z HH = − , (2a-c) 222 =+ FZH

Similar to the DI-Flux method, D is determined by two PVM readings (α0, α180) at 180° 

turned senor positions close to the east-west direction to eliminate the deviation between coil 

axis and telescope axes in the horizontal plane: 

D

H

Z ⎟ −=⎠

⎞ ⎜

⎝

⎛

− 0

sin

arccos α

δ (3) 

A setup without Zeiss theodolite has been developed by Magson GmbH. The theodolite has 

been replaced by a worm gear driven turn table equipped with an incremental encoder to read 

the angular position (see Figure 1b). Due to the fact that only one rotation capability remains, 

the PVM bias coil has to be inclined to bring the azimuth mark into the focus of the telescope. 

Therefore δ can not be assumed as small anymore and equation 1 has to be extended as 

follow: 

( ) () α HH δ coscos (α ) ZD sin(δ ) n = n +− (4) 

Measurements are taken at several rotation angle positions αn (e.g. in 30° angle steps). Each 

measurement has to fulfill the following equation: 

( ) () α HH δ coscos (α ) ZD sin(δ ) n = n +− (5) 

By fitting the measurement values to a cosine function we get the following estimates for 

amplitude A, phase φ and offset A0: 

cosδ = AH , D = φ , 0 sinδ = AZ (6) 

25
As described in [V.Auster et al, 2007] both field components, Z and H can be derived from 

equation (6a and 6c) by an iteration procedure. 

The whole procedure needs about 30 minutes which requires the reduction of field variation 

by using a three-component fluxgate magnetometer. 

Fig. 1a. Absolute measurement at 

repeat station with a PVM mounted 

on a non-magnetic theodolite 

Fig. 1b. Absolute measurement using a PVM on a worm gear driven 

turn table, model 2006, The bias coil is pivoted in two rings 

Measurement errors 

The measurement procedure provides magnetic field values in direction of the PVM 

bias coil axis. The transformation of these values into the magnetic field value in a geographic 

reference system contains the following errors which have to be considered: 

• the alignment of measurement axis and mechanical coil axis 

• the alignment of measurement axis and optical axis 

• the inclination of the rotation plane vs. horizontal plane 

• the eccentricity of the rotary encoder 

• the precision of the rotary encoder steps 

• magnetic contamination of various mechanical parts. 

Mechanical references for the bias coil are two rings at both ends of the coil (see 

Figure 1b). The coil is pivoted by these rings so that the bias coil can be rotated about its 

mechanical axis. 

The misalignment of the measurement axis versus coil mechanical axis can be 

determined by taking measurements at various coil angular positions. It is an instrument 

specific error which depends on coil machining. The error is much less than 1 arcmin and 

shall not depend on aging and temperature variation. If the coil has to be accommodated in the 

manner that the angular deviation between both axes is oriented in the vertical direction, the 

error will be eliminated in the identical way as the coil inclination has been considered by the 

measurement procedure. 

In a next step the optical detector has to be aligned with respect to the measurement 

axis. This is done by observing a distant target. The target has to be at the same coordinates 

upon making a 180° rotation of the bias coil. If we suppose that an alignment accuracy (and 

stability) of 0.01mm shall correspond with an accuracy of 1arcsec, and if we use a 1/3 inch 

chip (4.51mm x 2.88mm) as detector, the view angle shall be less than 0.2°. The practicable 

26
field of view depends on the optics. A single lens optics is recommended to save costs and to 

avoid magnetic materials close to the sensor. The maximum focal length is limited by the 

instrument dimension (bias coil length of about 300mm). The ratio of CCD dimension and 

focal length leads to a minimum field of view of 0.8°. So this parameter is critical and limits 

the accuracy in D measurement to some arcsec. Therefore accommodation and adjustment of 

the detector at the bias coil has to be done highly symmetric to minimize a thermal influence 

on the alignment of the optical axis. This and the alignment of the measurement axis itself are 

done at maintenance intervals only. 

As described in chapter 2, the PVM sensor has to be rotated about the vertical axes to 

determine H, Z and D. The misalignment between rotation axis and true vertical axis depends 

on instrument leveling and stability of mounting platform. Both error sources can not be 

assumed as constant over a long period. Therefore this error has to be watched at each 

measurement by a tilt sensor. Tilt sensors with sufficient accuracy are available due to the fact 

that only a narrow range has to be covered, that measurements are taken after long enough 

setting times, and that the offset error of the tilt sensor can be eliminated by the 360° rotation. 

For the automated system a glass narrow angle tilt sensor from Fredericks has been used 

which has a resolution of less than 1arcsec. 

The rotation about the vertical axis is monitored by a rotary encoder system. The 

measurement accuracy of the encoder has a systematic and a random error. The systematic 

error depends on the eccentricity of the graduation relative to the axis of rotation (sum of 

radial eccentricity of bearing and grating disk mounting). The angular error is proportional to 

the ratio of eccentricity and diameter. For that reason the bearing diameter (240mm) as well 

as the diameter of the grating disk (192mm) shall be as large as possible. Nevertheless the 

eccentricity error can not be neglected and has to be determined as an instrument 

characteristic which has to be corrected. It can be determined by using a standard magnetic 

theodolite with an one order of magnitude higher accuracy. Instead of the PVM sensor, the 

magnetic theodolite can be placed on the rotation table to verify the rotation angle. Figure 2 

shows the measurement setup. Due to identical interfaces of PVM and magnetic theodolite, 

the verification of the eccentricity can be done at each service interval. 

The random error of the rotation 

angle measurements depends on the 

graduation error of the grating disk. The 

grating disk has 24000 lines with an 

angular resolution of about 1arcmin. 

Although the resolution after interpolation 

and phase evaluation is better than 1arcsec, 

the accuracy guaranteed by the provider 

(Numerik Jena) is about 10arcsec. 

Assuming the error is random distributed, 

it can be reduced by an increasing number 

of measurements. Furthermore the error 

becomes smaller, if the azimuth mark is 

located close to magnetic East-West. In 

this case the measurement directly related 

to the mechanical reference system (free of 

angular error) is located in an anglesensitive area. 

Fig. 2. Turn table with magnetic theodolite to determine the eccentricity error of bearing and grating 

disk 

27
Finally the influence of magnetic parts shall be excluded. The advantage of a PVM is 

that each measurement provides the total force F. If the rotating part of the turn table is free of 

magnetic material F, should be independent on the rotation angle. In this way it can be 

checked that the major part of the facility including encoder, telescope, CCD camera, tilt 

sensor and PVM itself is magnetically clean. Figure 3 shows the residuals of the total field 

measurement at each angular position, corrected by the variometer. The influence of the 

remaining magnetism of the rotating part is less than 0.5nT. 

Fig. 3. Residue Hmeas -Hfit (red): standard deviation 0.87nT 

Residue F -Fmean (blue): standard deviation 0.29nT 

Concluding we summarize in Table 1 the nature and way of eliminating the discussed error 

source: 

Error Source Determined by Eliminated by Done at 

alignm. of measurement 

and mechanical axis 

measurement during rotation 

of bias coil 

turning angle deviation 

into vertical direction 

service 

intervals 

alignm. of measurement 

and optical axis 

180° turning of bias coil and 

taking pictures 

adjustment of optical 

detector 

service 

intervals 

inclination of rotation axis 

vs. vertical axis 

tilt sensor measurement data processing each 

measurement

eccentricity of the rotary 

encoder 

replacement of PVM by 

precise magnetic theodolite 

data processing service 

intervals 

precision of the rotary 

encoder steps 

standard deviation of 

residuals (H sinαn) 

error bar definition each 

measurement

magnetic contamination standard deviation of 

residuals (F) 

error bar definition each 

measurement

The red curve in Figure 3 shows the result after elimination of all errors. The standard 

deviation of the residuals – the difference to an ideal sine function – is in the order of 1nT of 

magnitude. 

3. Technical Realization 

From the 2006 proto type, only the PVM itself has been used for the instrument 

presented in Boulder 2008. All other units are reworked in order to increase the level of 

automation and to improve the measurement accuracy. 

28
Figure 4 shows the absolute measurement system with the PVM coil, including the 

telescope and CCD camera, mounted on the turntable (front). The turntable is driven by a DC 

motor which is linked to the worm gear of the turntable by expansible rods. The electronic 

boxes of PVM electronics and the microprocessor as well as the PC can be seen in the back. 

Fig. 4. PVM Absolute Measurement System presented at the 

XIIIth IAGA Workshop 

The turntable has been 

redesigned to come closer to the 

precision of a Zeiss theodolite. 

The slide bearing (see yellow 

surface area in Figure 5) has a 

bearing clearance of 0.01mm! 

Both parts of the turn table are 

manufactured on the same 4 

axes CNC machine. To avoid 

clamping due to different 

thermal expansion coefficients 

both sides are made from the 

same material (AlZnMgCu1.5). 

The surface of the bearing is 

anodized with Teflon additive to 

reduce the friction. 

The worm gear is configured as 

globoid to keep the contact area 

as large as possible in order to 

provide a smooth power 

transmission. The worm is 

driven by a Faulhaber 50mNm 

DC motor (5000rpm) equipped 

with a planetary gearhead 

(246:1) and shielded by a softmagnetic cover. 

The motor is controlled by an 

absolute angle encoder 

(resolution 13bit single turn & 

12bit multi turn). The motor has 

been selected because its 

magnetic properties have been 

extensively tested for the ESA 

space mission Rosetta. 

Fig. 5. Rotatable part of turn table with worm gear and slide 

bearing (yellow) 

 The magnetic moment is less than 10mAm2 which requires a minimum distance to the 

PVM of 2m. The transmission is done via joint rods, a robust solution which can be simply 

adapted to various installations. The absolute decoder, mounted on the output of the gear, is 

used to control the rotation. Due to the transmission slip of joint rods and worm gear the 

angular position of the turn table can only be adjusted roughly, but is sufficient precise to 

bring the azimuth mark into the field of view of the optical system. The precise angle 

measurement is done by the relative angle decoder mounted between upper and lower parts of 

the turn table. 

 The optical detector is a 752x480 pixel CMOS monochrome sensor with an USB 

interface. The telescope is projecting a field of view of 0.8° on the chip. Thus, one pixel in 

29
horizontal direction corresponds with an angle resolution of 3.8arcsec. Controlled by the 

absolute angle encoder the turntable stops at several positions before and after the nominal 

azimuth mark direction. Pictures are taken at each of these positions and the image processing 

software correlates picture windows with a reference picture of the azimuth mark. At each 

stop the position of the azimuth mark and the counter of the rotary decoder of the turntable are 

saved. Using a set of azimuth mark measurements, the resolution can be increased to about 

1arcsec. Alternatively CCD’s with higher pixel density can be used to increase the resolution. 

Figure 6 shows a picture of the azimuth mark taken in our test facility in Jeserigerhuetten. In 

the bottom part the correlation coefficients between reference picture and a horizontal scan is 

drawn. The position of the azimuth mark is reached if the correlation factor is in its 

maximum. Taking a set of pictures before and after the nominal azimuth mark allows a linear 

interpolation to increase the accuracy of determination of the azimuth mark position. 

Fig. 6. Picture of the azimuth mark as seen by the CMOS chip and result of the correlation analysis 

(lower panel). At the right side the status of the absolute measurement is highlighted 

With respect to the azimuth position, the coil is rotated to 12 equidistant angles and in 

each position a PVM measurement is carried out automatically. When the last measurement is 

finished, the measurement is analyzed. The PC software gives access to the final result as well 

as to various calculation steps. So, figure 7 shows a snapshot of the PC screen, showing the 

measured total field with variation reduction, while figure 8 shows the calculated horizontal 

field with variation reduction. Figure 9 shows the horizontal fit error and figure 10 the final 

result of the absolute measurement. 

30
Fig. 7. Total Field with Variation Reduction Fig. 8. Horizontal Field with Variation Reduction 

Fig. 9. Horizontal Field Fit Error Fig. 10. Final Measurement Result 

A control unit based on a microcomputer coordinates all elements of the automated 

system (see block diagram in Figure 11). Motor, PVM and Camera are controlled by this unit. 

The time is synchronized by a GPS receiver. The data from variometer and tilt meter are 

sampled at each PVM measurement. An industrial PC is used to show the status of the 

measurement and to visualize the results (example see Figure 6). All parts of the measuring 

station are supplied by 12V, which allows to buffer the absolute measurement by a battery. 

Fig. 11. Blockdiagram of the automated system 

31
4. Summary 

An instrument to perform an absolute measurement automatically based on a PVM has 

been presented. Manually performed absolute measurements by various PVM configurations 

on top of Zeiss Theodolites serve as role models. The Zeiss theodolite has been replaced by a 

turntable, manufactured in a high precision mechanical workshop (Steingross Feinmechanik, 

GER) and an optical system consisting of telescope, CMOS detector and image processing 

software. Precision close to that of the Zeiss theodolite, high reliability and comparable low 

costs are the design goals. This could be achieved by usage of well tested commercial parts: 

Industrial PC, Industrial Camera, Tilt sensor (Fredericks, USA), DC Motor (Faulhaber, GER), 

Incremental endecoder (Kuebler, Numerik Jena, GER). 

The presented system is an alternative automation approach to DIF and GAUSS. The 

PVM measurement excludes inherent offset and scale value errors. All other error sources can 

be eliminated. Error sources related to instrument mechanics are checked at maintenance 

intervals. All error sources which might depend on time (installation, environmental 

conditions) are determined at each absolute measurement. Tests with this automated system 

have been performed since 2006. The achieved overall accuracy is better than 2nT. 

Acknowledgement 

For all the support we got for the design and manufacturing of the mechanics we thank Bernd 

Chares and the Company Steingross. 

References

Auster, H.U., M. Mandea, A. Hemshorn, E. Pulz, M. Korte, Automation of absolute 

measurement of the geomagnetic field, Earth Planets Space, 59, 1007\u20131014, 2007. 

Auster, V., O. Hillenmaier, R. Kroth and M. Wiedemann, Advanced Proton Magnetometer 

Design and its Application for Absolute Measurement, Publs. Inst. Geophys. Pol. Acad. Sc. 

C-99, 60-68, 2007. 

Kring Lauridsen, E., Experience with DI fluxgate magnetometer inclusive theory of the 

instrument and comparison with other methods, Danish Meteorological Institute, 

Geophysical Papers, V2-71, 1985. 

Van Loo, S.A., and J.L. Rasson, Presentation of the prototype of an automatic DI-Flux., 

Publs. Inst. Geophys. Pol. Acad. Sc. C-99, 77-86, 2007. 

32
*: Corresponding author 

 Address: Hanoi Institute of Geophysics, 18 Hoang Quoc Viet, Hanoi, Vietnam 

 Tel. : Office: 84 437 566 893; Home: 84 437 567 757 

 Fax. : 84 438 364 696 

 Email : hdchauigp@yahoo.com

MODELS OF MAGNETIC SECULAR VARIATIONS OBTAINED FROM 

VIETNAMESE MAGNETIC OBSERVATORIES AND REPEAT 

STATION NETWORK SURVEYS FROM 1997-2003 

H.D. Chaua

, J.L. Le Mouëlb, F. Truongb, K. Telalib

a: Hanoi Institute of Geophysics, Vietnam 

b

: Paris Institute of Physics of the Earth, France 

Abstract: 

From 1990 – 1991, a magnetic repeat station network in Vietnam was built. The 

network now includes 58 stations distributed on all the territory of Vietnam. 

Measurements of the geomagnetic field were carried out in 1990 (8 stations), 1991 (56 

stations), 1997 (56 stations), and 2003 (58 stations), using the fluxgate, theodolite D-I 

flux, and proton precession magnetometer. This work represents a cooperative effort 

between the Paris Institute of Physics of the Earth and the Vietnam Institute of 

Geophysics. Here we present the results of calculations of the models of magnetic 

secular variation based on data from the repeat surveys and the Vietnamese magnetic 

observatories. The construction of the models is challenging due to the irregular 

geometry of the Vietnamese territory. Computation is based on the values from 

different number of SV stations, sparsely distributed all over the territory of Vietnam, 

and on some weighted values of IGRF outside the territory of Vietnam. We discuss the 

technique and make some inferences about added IGRF values. We show the 

characteristics of the magnetic fields and their secular variations (for 3 magnetic 

elements: Declination D, Horizontal component H and Vertical component Z) from the 

obtained models. 

INTRODUCTION 

 The geomagnetic secular variation data obtained from one repeat station 

network has a big importance not only in the practical problems for every territory, but 

also in the global modeling [2, 3, 10, 12, 13, 14, 16, 17]. In Vietnam, the repeat station 

network was initiated in 1990-1991, including 56 stations distributed over all the 

territory. After this initiation, two measurement campaigns were realized in 1997 [4, 6] 

and 2003 [5, 7] by the Vietnamese researchers at the Hanoi Institute of Geophysics, in 

collaboration with those coming from the Paris Institute of Physics of the Earth, in the 

frame of scientific co-operation between these two Institutes. From measurement 

results of these measurement campaigns, we pose the problem of constructing a 

secular variation model for the period from 1997 - 2003 for the territory of Vietnam, 

issuing from the fact that a model like that is very necessary for scientific studies and 

for practical tasks, such as doing the corrections for the other magnetic measurement 

works on the territory of Vietnam, especially in the present period of its economic 

development. For a geometrically regular territory like France, the construction of a 

secular variation model is rather simple. But for an irregular geometry of the territory 

as Vietnam, the shape of which looks indeed like an elongated "S" oriented in the 

33
North - South direction, one meets with big difficulties due to the irregular distribution 

of the data over the region of model computation. This was already signaled in [1], in 

which one showed that an uneven data distribution has a very big effect on the field 

modeling. For overcoming the difficulties, one thinks to take advantage of the model 

of the International Geomagnetic Reference Field (IGRF), which describes well the 

normal field on the territory of Vietnam, as was showed many times before. One can 

use it, by adding some its values, corrected with the measurements taken at the Earth's 

surface at some points outside the territory of Vietnam to the number of secular 

variation stations in order to obtain a more regular distribution. A problem to pose here 

is: How one does the adding of the points? How many points are enough to add? 

Which weights one must give to these adding points? 

I. BRIEF PRESENTATION OF THE REPEAT NETWORK 

 The distribution of the stations in the Vietnamese repeat network was presented 

in [4, 5]. Here we give only a very brief presentation of it. The Vietnam repeat stations 

network includes 4 magnetic observatories, which have a big role in the network. 

Because the territory of Vietnam is spread in a narrow and long meridian band, from 

8oN to 23oN latitudes, the magnetic variations covering a large range, one has installed 

4 magnetic observatories: Chapa, Phu Thuy in the North, Bac Lieu, Da Lat in the 

South (Figure 1). 

 In the Phu Thuy observatory, from 1993, one has used a numerical recording 

system (GEOMAG) in the frame of INTERMAGNET program, thanks to the scientific 

co-operation between Hanoi Institute of Geophysics and Paris Institute of Physics of 

the Earth. This system is of high resolution: 0.1 nT for the tri-axial magnetometer and 

0.01 nT for the scalar one. For the absolute measurements, from 1995 one has used a 

fluxgate theodolite of DI-MAX 9302 (French), having the resolution of 1", and a 

proton magnetometer . In the Bac Lieu observatory, from 1988 to 1997 one has used a 

Bobrov MBC Russian recording system using the photographic paper, but from 1998, 

one has replaced it by a Japanese fluxgate magnetometer FRG-601, having a 

resolution of 0.01 nT. For the absolute measurements, from 1998 one has used a 

fluxgate magnetometer MAG-01H Bartinghton having a resolution of 1" and a 

Canadian proton magnetometer GSM-9 with the resolution of 1nT. In the Chapa and 

Da Lat observatories, one still uses the Bobrov MBC Russian systems using the 

photographic paper for recording the magnetic field. The distribution of stations is 

presented on the Figure 1. 

 As was slated above, in October - December 1997 and in February – April 

2003, one realized two measurement campaigns all over the Vietnam territory. In these 

campaigns, one has used various proton magnetometers and the fluxgate - 

magnetometers borrowed from Paris and Strasbourg Institutes of Physics of the Earth 

(see [4, 5]). The measurement executions were very carefully made. The problem of 

data reduction to a certain epoch, which is a very complicated because of the long and 

irregular form of the territory of Vietnam (see the references quoted), was very 

minutely realized. 

 

II. DATA 

 From the two campaigns realized in 1997 and 2003 on the Vietnamese repeat 

station network, as was presented in the last paragraph, one obtained two geomagnetic 

data collections, reduced to 1997.5 and 2003.5 epochs (see [4, 5]). From these data, 

34
one deduces the geomagnetic secular variation values (φ,λ) for the period of 1997 - 

2003 in the station of latitude φ and longitude λ, by a simple operation for the element 

E: 

E&

6/)],(),([),( (1) & = EE 2003 − E1997 λφλφλφ

where E1997(φ,λ) is the value of the element E in the station (φ,λ), obtained from the 

1997 campaign and reduced to the 1997.5 epoch; E2003(φ,λ) is the value of the element 

E in the station (φ,λ), obtained from the 2003 campaign and reduced to the 2003.5 

epoch; 6 is the time interval (in years) between the two campaigns. 

 For many reasons, one can't calculate the secular variation in some stations: 

Firstly, there are 13 stations where one hasn't realized the measurement of magnetic 

declination D (in the campaigns 1997 or 2003): PHONG THO, THAT KHE, PHO 

RANG, BAC CAN, TUAN GIAO, THAI NGUYEN, PHU NINH, BAC GIANG , 

BAN MO, MOC CHAU, NAM DINH, SAM SON, YEN BAI). Of cause, one can't 

calculate the secular variation for D, X and Y elements in these stations 

 Secondly, the stations where one had to move the measurement places because 

of the construction of the new buildings: In the campaign 2003, 4 stations have been 

moved to new locations, and renamed: GIA RAY, from XUAN TRUONG, CHAU 

THANH, from TAN AN, RACH TAU, from RACH GOC and LAI CHAU, from 

MUONG LAY, and others 16 were moved in keeping the same names : MONG CAI , 

CAM PHA, VINH, DEO CA, LANG SON, THAI NGUYEN, PHU NINH, NOI BAI , 

BAN MO, PLEI CU, DIEN BIEN PHU, TIEN YEN, GIO LINH, AN KHE, QUI 

NHON and CAN THO. 

 Thirdly, the stations where the measurement values were erroneous or 

containing local anomalies, being suppressed in the calculation of the normal 

geomagnetic fields by the method of least squares: CPA for I, PLC for T in the 

campaign 1997 [6], BLC for Z and I, BLU for D and Y, BMT for H, BNS for Z and I, 

DLT for D and Y, MRK for H, TGO for T, H and Z, VTU for H in the campaign 2003 

[7]. 

35
102 103 104 105 106 107 108 109 110

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

CHINA

LAOS

CAMBODIA

Hai Nan

Tonkin Gulf

Bac Lieu

Da Lat

Phu Thuy

HANOI

Cha pa

Ho Chi Minh

 BGG 

 NDH 

 DBP SLA 

 CPA 

 TNH 

 RCT 

 MXN 

 VTU 

 GRY HMN 

 . 

 . 

 CBG

 PTO TKE PRG LCU BCN

 LSN YBI TGO TNN MCI PNH TYN NBI BMO 

 MCU HPG HBH 

 GKT 

 SSN 

 HMI 

 VIN 

 KAH 

 DHI 

 GLH 

 HUE 

 DNG

 BSN 

 SHH 

 AKE PCU 

 QNN 

 DCA

 BMT MRK

 NTG 

 PNG BLC 

 BBH 

 TSN 

 CTH 

 CTO 

 CMU 

 .

 .

Luang Prabang

Vientiane

Savanakhet

Stueng Traeng

Phnom Penh

Figure 1. Localization and code name of stations from the 

 repeat network of Vietnam, used in the 2003 campaign 

 : Magnetic Observatory 

• : Station 

36
 Finally, one obtained 7 geomagnetic secular variation data sets for 7 elements: 

Table 1. Geomagnetic secular variation values in Vietnam from 1997 - 2003 

Nr. Station Name Code Longit. Latit. D& I

& H& X& Y& Z& T&

(

o

E) (

o

N) (‘/year) (nT/year) 

1 CAO BANG CBG 106.231 22.676 -1.84 4.02 -16.68 -17.08 -20.28 52.23 14.02

2 PHONG THO PTO 103.530 22.394 3.80 -11.10 52.37 18.22

3 CHA PA SPA 103.833 22.336 -1.45 4.40 -17.98 -18.28 -15.86 56.83 14.55

4 BAC CAN BCN 105.854 22.106 4.15 -16.03 54.17 14.53

5 YEN BAI YBI 104.921 21.717 4.23 -14.07 56.23 16.62

6 TUAN GIAO TGO 103.424 21.690 3.74 

7 SON LA SLA 103.942 21.328 -1.45 -13.82 -14.04 -16.22 59.48 17.52

8 BAC GIANG BGG 106.128 21.268 4.14 -13.37 54.57 15.44

9 PHU THUY PTY 105.950 21.029 -1.42 4.10 -13.88 -14.15 -15.89 53.47 14.01

10 HAI PHONG HPG 106.674 20.907 -1.24 4.13 -14.07 -14.30 -13.79 53.40 13.60

11 MOC CHAU MCU 104.670 20.841 3.67 -13.03 47.30 11.39

12 HOA BINH HBH 105.326 20.730 -0.91 4.44 -13.58 -13.74 -10.13 57.92 16.01

13 NAM DINH NDH 106.083 20.444 4.19 -12.03 54.67 15.10

14 GIAN KHUAT GKT 105.928 20.340 -1.49 4.31 -11.92 -12.13 -16.88 56.90 16.00

15 SAM SON SSN 105.889 19.725 3.78 -11.07 48.53 11.83

16 HOANG MAI HMI 105.725 19.266 -1.43 4.46 -11.38 -11.60 -16.35 57.48 14.55

17 KY ANH KAH 106.324 18.048 -1.25 4.35 -10.93 -11.10 -14.44 55.17 11.50

18 DONG HOI DHI 106.625 17.493 -1.32 4.61 -11.17 -11.35 -15.25 58.20 11.26

19 HUE HUE 107.578 16.420 -1.35 4.38 -12.95 -13.12 -15.65 53.08 5.49

20 DA NANG DNG 108.197 16.058 -1.07 3.93 3.88 3.76 -12.59 53.00 20.71

21 BINH SON BNS 108.771 15.274 -0.82 -9.53 -9.62 -9.50 -14.81

22 SA HUYNH SHH 109.065 14.654 -0.93 4.44 -5.20 -5.30 -10.90 55.13 9.82

23 M DRAK MRK 108.748 12.750 -0.32 5.61 -13.12 -3.66 66.58 0.12

24 BUONMETHUAT BMT 108.148 12.698 -0.23 3.10 13.53 -2.78 40.97 21.16

25 NHA TRANG NTG 109.183 12.231 -1.18 4.99 -13.47 -13.51 -13.95 58.62 -3.06

26 DA LAT DLT 108.483 11.944 4.86 -4.60 -4.63 58.57 5.19

27 PHAN RANG PNG 108.934 11.531 -0.02 3.82 -17.38 -17.39 -0.22 43.98 -10.67

28 BAO LOC BLC 107.616 11.410 -0.16 -0.52 -0.52 -1.90 7.90

29 TAY NINH TNH 106.099 11.289 0.49 4.15 -4.65 -4.64 5.83 50.05 2.22

30 BAC BINH BBH 108.334 11.199 0.70 4.87 -7.28 -7.28 8.30 57.93 0.37

31 TANSON NHAT TSN 106.641 10.810 -0.40 5.05 1.12 1.10 -4.81 60.97 7.97

32 VUNG TAU VTU 107.119 10.389 -0.94 4.55 16.65 -11.31 56.67 22.02

33 BAC LIEU BCL 105.711 9.298 7.30 7.14 59.72 10.32

34 CA MAU CMU 105.176 9.176 0.63 4.77 8.37 8.37 7.59 57.77 10.92

*: Magnetic observatory 

III. COMPUTATION METHOD OF THE SECULAR VARIATION MODEL 

 For a certain component of geomagnetic secular variation field in a certain 

point on the Earth's surface having the graphical latitude φ and longitude λ, in the 

neighborhood of a point (φo,λo), for a small territory as Vietnam, one can represent 

the secular variation models by a second degree polynomial with 2 variables (Δφ = φ - 

φo, Δλ = λ - λo ): 

E&

E& φo,λo(φ,λ) = E& o (φo,λo)+ AΔφ + BΔλ + C(Δφ)

2

 + DΔφΔλ + F(Δλ)

2

 (2) 

where E& φo,λo(φ,λ) is the secular variation value at the point having the co-ordinates 

(φ,λ), E& o (φo,λo) is the secular variation value at the point (φo,λo). E& φo,λo(φ,λ) is a 

continuous function on the Earth's surface. The coefficients A, B, C, D, F are 

37
determined by the method of least squares. This method leads to the solving of the 

linear algebraic equations system: 

version method, obta

A X = B (3) 

where A is a symmetric matrix of 6×6 dimensions and B, X are column matrix with 6 

terms. After having resolved the system (3) by the matrix in ining 

the coefficients E& o, A, B, C, D, F and the value of (E φ λ E;,

&&

okkk ),A,B,C,D,F , the 

calculated value of the secular variation field at the measurement station. Comparing it 

with the measured value fk(φk,λk) , one obtains the discard between the calculated and 

measured values εk: 

 (4) 

 Hence one can calculate the standard deviation σ: 

εk = fk(φk,λk) - (E λ ,A,B,C,DE;, okkk φ && ),F

σ = 

n − 6 (5) 

for estimating the data collection quality. In this expression, 6 is the number of 

coefficients to be calculated. One can use the value of σ and the "3σ-principle" for 

making the criterion of filtration of the data which are erroneous or containing the big 

anomalies: If the ratio tk = εk/σ passes 3, the elimination probability of the erroneous 

data is P = 0,99 for n = 20 ÷ 100. After the introduction of all the data for solving the 

system AX = B, obtaining the values of σ and εk, eliminating some data as one said, 

one uses the stations which remain for solving again the system in order to obtain t

1

2

∑=

n

k

k ε

he 

her σ

m

ot and εk values. One repeats as so until there isn’t any ration tk bigger than 3. 

 We have applied this method to the secular variations data in Vietnam fro 

1997 - 2003 (presented in the Table 1) for the declination D& , horizontal component H&

and vertical component Z& in order to compute their secular variation models. In the 

calculations, we have chosen the center of the studied region as the point (φo,λo) : φo = 

16o

40’, λo = 106o10’. Fortunately, for all the elements of the field, one needn't 

suppress any value. Before analyzing the results, we must say at first that we will meet 

with the difficulties in the construction of the maps: The secular variation values are in 

general small, and they aren’t uniformly distributed on the modeling area. As we have 

already warned in [1], the un-uniform distribution of the data has a big influence on 

the field modeling. In our case, we must work with the stations which are distributed

in comprising with the other on the world, 

one has calculated the standard

on a narrow band of “S” form, so one can’t wait good charts from only these stations. 

 We observed that the secular variation (isoporic) charts, constructed for the 

sets of data given in the Table 1 show a very bizarre aspect of isopors system, 

showing a tendency along the narrow band of Vietnam territory, caused by the 

irregular and un-uniform distribution of the data, although the data geomagnetic used 

for calculating the secular variation data were very well measured and treated (as one 

has already said above). One has constructed two sets of normal geomagnetic fields on 

the territory of Vietnam for two epochs 1997.5 [6] and 2003.5 [7], and the sharp of the 

curves systems of these two sets are very similar to those calculated from the IGRF 

models [8, 9]. Furthermore, for confirming the quality of the magnetic values 

measured on the Vietnamese repeat network 

 deviation σ: 

σ = {Σ(Erep – EIGRF)

2

/(n-1)}1/2 (6) 

showing the statistics for differences between field measurements and different models 

38
for 3 repeat station networks (Vietnam, Southern Africa and New Zeeland). On the 

Table 2, one presents the values of this quantity calculated for Vietnamese repeat 

station network and those quoted from [15] for New Zeeland and [11] for Southern 

Afric

e Flight Center (American) 

eological Survey (English) and U.S. Navy 

ll in 

f Y component in Vietnam are very 

mall, so the errors of its estimation are rather big. 

ea epeat 

WORK 

VIETNAM SOUTHERN AFRICA 

(about 80 points) (about 652 points) 

a region, where one has used various main-field models: 

- GS-95 : produced by NASA Goddard Spac

- IZ-95 : produced by IZMIRAN (Russian) 

- UB-95 : produced by British G

- IGRF95 : produced by IAGA 

One sees that the σ values obtained from our repeat station network are rather sma

comparison with those obtained from the other repeat station networks (only σY 

values are bigger, because of the absolutes values o

s

Table 2. M n square discards for the three r station networks 

NET-

(about 

50 pts) 

NEW-ZEELAND 

Model I GRF95 UB-95 GS-95 IZ-95 UB-95 GS-95 IZ-95 IGRF95 

σT (nT) 61,49 133 84 161,57 128,92 , 87 87 103 87 

σX (nT) 66,12 151 140 134 141 

σY (nT) 131,23 118 105 112 109 

σZ (nT) 78,90 133,06 170,99 130,52 85 89 94 86 

 

 One sees that although the geomagnetic data were very well measured at the 

repeat station network during two measure campaigns, the main-field models of which 

are very well constructed, being similar to those of IGRF, the secular variation charts 

constructed show the bizarre characteristics because of narrow elongated form of 

Vietnam territory as one has said above. One needn’t leave these charts as so! And one 

has to have good secular variation models for geomagnetic research and for realizing 

nough, and with which 

eight one must give to these points added. 

the corrections in the magnetic works in Vietnam now. 

 One thinks to make the most of the International Geomagnetic Reference Field 

(IGRF) Secular Variation Models. On Figure 2, one can see the secular variations 

charts calculated from IGRF 1995 models. These models describe rather well the 

general tendencies of the isopors. For having the good models of isopors for our case 

(a territory with a long and irregular form), we must apply these general tendencies to 

the data measured at the Earth’s surface, by making the corrections using the 

geomagnetic values from these IGRF models. Practically, we can add the IGRF 

secular variation values to the places in the region studied, where the data are absent 

for reducing the non-uniformity effect of data on the territory. The problems posed are 

how to make the adding of data, how many points to add are e

w

39
 

102 104 106 108 110

8

10

12

14

16

18

20

22

24

102 104 106 108

CHINA

LAOS

CAMBODIA

Hai Nan Tonkin Gulf

Bac Lieu

Da Lat

Phu Thuy HANOI

Cha pa

Ho Chi Minh

(a) Declination D 

110

8

10

12

14

16

18

20

22

24

CHINA

LAOS

CAMBODIA

Hai Nan Tonkin Gulf

Bac Lieu

Da Lat

Phu Thuy HANOI

Cha pa

Ho Chi Minh

(b) Horizontal Component H 

 

102 104 106 108

8

10

12

14

16

18

20

22

24

110

CHINA

LAOS

CAMBODIA

Hai Nan Tonkin Gulf

Bac Lieu

Da Lat

Phu Thuy HANOI

Cha pa

Ho Chi Minh

(c) Vertical Component Z 

eproduced 

es are in ’/year for D, in 

nT/year for, Z, H)

Fig. 2 (a, b, c). Maps of the magnetic 

secular variation in Vietnam, r

from IGRF 9th generation [9] 

(Values on the curv

40
 Now, we begin with the method of adding data from the IGRF. One knows 

that practically, the values of geomagnetic secular variation field calculated from the 

IGRF in a point at the Earth’s surface don’t coincide to those observed by the 

measurements made in the same point. So for adding a value from IGRF, we must 

know the difference between these two values. For this, we calculate a number, called 

“deviation constant” CE, which shows the mean deviation between the secular 

variation values set of a geomagnetic element E obtained from the measurement at all 

the stations and that calculated from the IGRF for this element at the same station 

positions. 

 After having these values, if one wants to have a secular variation value of a 

component E at a place on the studied territory, comparable to those obtained by the 

measurements made on the Earth’s surface, , one adds the value of these 

constants CE to the secular variation value obtained from IGRF for that component, in 

the same place : 

λφ ),( Eaidded

&

 λφ ),( = + CE (7) Eaidded

& λφ ),( EIGRF&

 So for having a secular variation data set more uniform on the studied territory, 

in order to calculate more correctly the secular variation models, one adds to the 

existent secular variation data a number of secular variations by using the expression 

(7) in the places where there aren’t any secular variation obtained from measurements. 

 Now we pass to study for answering the question: how many points are enough 

to add? The answer to this question is equivalent to find the criteria of data adding. 

For establishing these criteria, one uses the quantity of standard deviation σR : σR = 

{ / (N – 6)}1/2 (similar to that defined in (6), but with εk = -

) as the basis parameter (the notation “R” here (from “Real”) means that the 

standard deviation σ is calculated only for No secular variation real values). From 

now, we will present in detail the study method for the secular variation of the vertical 

component Z. For the other elements (D,H), we use the same method, and we will 

present their results as below. One sees that if one has a secular variation data set (for 

example that of No = 31 secular variation values of the vertical component 

∑=

N

k

k

1

2 ε

E&

IGRF (φ

λφ ),( Emeasured

&

λ),

Z& in the 

Table 1), using the least square method, as presented above, one can obtain a set of 

coefficients o, A, B, C, D, F (by solving the system (3)), which present the secular 

variation model for 

E&

Z& on the studied territory, and besides it the quantity standard 

deviation σR estimating the quality of this Z& data set. Now if we add a number, for 

instance Nadd, of the secular variation defined as in (7) in the places where there aren’t 

any values obtained by measurements, to the data set No existent, we will obtain a new 

secular variation set including N = No+Nadd values. With this, one can calculate again 

the secular variation model, obtaining 6 coefficients and standard deviation σR, 

calculated for No source secular variation values. 

 At first, as in the article [1] on the field modeling from the data of World 

magnetic observatories, one has added various data numbers. We will take the vertical 

component Z for doing the examples. At beginning, we begin to add to the set of 31 

secular variation data of the component Z gradually, one by one to obtain the new data 

set N, with which one obtains a new σRZ (standard deviation for component Z). We 

observe that when the data number increases, the values of σR lightly increases. Until a 

41
moment, the addition of a datum doesn’t affect much anymore on the model quality. 

So it must limit the number of added data. After many tests, we observed that the data 

points at the border of the region where one does the modeling (that is those on the 

lines of 8o

N, 24oN latitudes and 102oE, 110oE longitudes) have the biggest affection on 

the model. So we began therefore to add systematically, gradually the added data 

points at the border of the region to the data set for calculating the models. On the 

Figure 3, one can see the graph of σR depending on the number of data N. 

30 32 34 36 38 40 42 44 46 48 50 52 54 56

5.36

5.40

5.44

5.48

5.52

5.56

5.60

σ Z (nT/year)

N (data number)

R

↓

Figure 3. Graph of σRZ depending on the data number N 

 One can see from this figure that σRZ increases from about 5.40 to about 5.53 

when data number N increases from 31 to 37 (that is Nadd = 1 ÷ 6), after that it remains 

nearly unchangeable (or with very small change) for N=38 - 55. This behavior can be 

seen in the Figure 4, where the quantity δRZ, defined as an increase of the standard 

deviation σRZ (δRZ = σRZ(N) - (σRZ(N - 1))) are rather big This means that when one 

adds gradually 1 to 6 data, these first data added affect much on the models, but if one 

adds more of data, that is Nadd ≥ 6, these data added won’t affect much anymore on the 

models. So one can choose this limit Nadd = 6, that is N = 37 for doing the secular 

variation model. 

30 32 34 36 38 40 42 44 46 48 50 52 54 56

0.00

0.02

0.04

0.06

0.08

0.10

0.12

δRZ (nT/year)

N (data number)

Figure 4. Graph of δRZ depending on the data number N 

 Now we touch upon the problem of the weight of the adding points. For this, 

one gives the different weights, from 0.1 to 1.0, with the interval 0.1 one to the other to 

6 added points in the 31 data set chosen (of course, 31 values of the origin data set of 

Z& are given always the weight 1) and one calculates the δRZ for these data set . The 

42
result is presented on the Figure 5. On the figure, one sees that the values of δRZ 

increase rather strongly from W = 0.1 - 0.5, after that they increase more slowly, with 

small quantity. This shows that with the weight W ≥ 0.5, the added data don’t affect so 

much the SV model. So we take this value as the weight for the added data for 

constructing the secular variation model for the vertical component Z& . 

5.35

5.40

5.45

5.50

5.55

5.60

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 1 .9

σRZ (nT/year)

W (weight)

v

Figure 5. Graph of δRZ depending on the weight of added data 

 So with the techniques presented for overcoming the difficulties due to the 

irregular geometry of the territory in the construction of a geomagnetic secular 

variation model from the Vietnam magnetic repeat station network, we have found for 

the vertical component Z the minimum number of added IGRF values Nadd = 6, with 

the weight W = 0.5. One must emphasize that the criteria chosen here aren’t definitive. 

One can find other criteria. For the moment, one thinks that the SV model obtained is 

already utilizable. 

 For the declination D and horizontal component H , we have realized the same 

procedures as for the vertical component Z, obtaining the following results : 

 - For 24 data set of the SV of the declination D, one adds 11 SV IGRF values 

with the weight 0.4. So the number of data for calculating the SV model of the 

declination D is 35; 

 - For 30 data set of the horizontal component H, one adds 10 values with the 

weight 0.5. So the number of data for calculating the SV model of the horizontal 

component H is 40. 

IV. RESULTS 

 With these data sets, we have constructed the SV models for D, H and Z 

components. The coefficients E& o, A, B, C, D, and F obtained are used for forming the 

equations expressing the magnetic secular variation models. 

 The result charts are presented on the Figure 6. Analyzing these charts, we can 

draw the following characteristics : 

 - Isoporic chart of the declination (Figure 6(a)): Absolute values of on 

the whole territory are small (max ⎢ ⎢= 1.7’/year). Majority of the values of are 

negatives, only in a small part in the southernmost territory they’re positives. The 

more one goes to the North, more absolute values of become bigger. The isolines 

ensemble are lying in the North-West – South-East direction in the North part of 

D& D&

D& D&

D&

43
territory, become South-West – North-East in the southernmost of the territory. The 

more one goes to the South, more the isolines become denser. The zero isoline is a 

nearly straight line laying in the South-West – North-East direction. 

 - Isoporic chart of the horizontal component H& (Figure 6(b)): What’s about 

the magnitude, the values of H& vary in the range of -17 nT/year ≤ H& ≤ 9 nT/year in 

the territory of Vietnam. The isoporic curves are lying in the North-West – South-East 

direction, retaining the concavity to the South-West. They are denser in the South, 

sparser in the North of the country. 

 - Isoporic chart of Vertical component Z (Figure 6(c)): The values of & Z& are 

positives, rather big on whole the territory of Vietnam, varying in the rang of 49 

nT/year ≤ Z& ≤ 59 nT/year. In the North of the country, the isoporic curves tend to lye 

in the North-West – South-East direction, while in the South, they are bent, having the 

concavity toward the West. They are denser in the North of country, much sparser in 

the South. 

V. CONCLUSION 

 In this contribution, we have presented summarily Vietnam repeat stations 

network which includes 56 stations constructed from 1990, 1991 and consolidated in 

1997, 2003, together with 2 absolutes measurements campaigns 1997 and 2003, from 

which one obtained a SV data set of the geomagnetic field on the whole territory of 

Vietnam. From these data, one constructed the SV models of Vietnam. But this met 

with difficulties due to the irregular geometry of the territory: the sharp of this territory 

looks indeed like an elongated “S” oriented in the North-South direction. For 

overcoming these difficulties, one presented a method basing on a analytical 

approximation (a second degree polynomial) of the SV field. The calculation was 

based on the values from different number of SV stations, sparsely distributed on the 

territory of Vietnam, and in addition, on some weighted values of IGRF 2003 (which 

was showed that describes well the normal field on the territory of Vietnam) outside 

the territory of Vietnam. We presented this technique and some criteria to take into 

account the minimum number of added IGRF values. 

In final, we present the isoporic charts D , & H& and . We present also the 

characteristics of the magnetic SV field drawn from these charts. We hope that these 

models can satisfy the requirements of practical geomagnetic works in Vietnam now, 

and can be useful for Worldwide geomagnetic modeling problems. This contribution 

was realized with the help of the Fundamental Research Program Nr. 7 111 06. 

Z&

44
102 104 106 108 110

8

10

12

14

16

18

20

22

24

CHINA

LAOS

CAMBODIA

Hai N an Tonkin Gulf

Bac Lieu

Da Lat

Phu Thuy HANOI

Cha pa

Ho Chi Minh

(a) Declination D 

102 104 106 108 110

8

10

12

14

16

18

20

22

24

102 104 106 108 110

8

10

12

14

16

18

20

22

24

CHINA

LAOS

CAMBODIA

Hai Nan Gulf of Tonkin

Bac Lieu

Da Lat

Phu Thuy H ANOI

Chapa

Ho Chi Minh

(b) Horizontal Component H 

102 104 106 108 110

8

10

12

14

16

18

20

22

24

CHINA

LAOS

CAMBODIA

Hai Nan Gulf of Tonkin

Bac Lieu

Da Lat

Phu Thuy HANOI

Cha Pa

Ho Chi Minh

 (c) Vertical Component Z

Fig. 6 (a, b, c). Definitive isoporic charts 

in Vietnam for the 1997 - 2003 period for 

D, H and Z components. 

(Values on the curves are in ’/year for D, in 

nT/year for H and Z)

45
REFERENCES 

[1]. ALEXANDRESCU M., H.D. CHAU, J.L. LE MOUËL, Geographical distribution of 

magnetic observatories and field modeling. J.G.G, 46 (1994) 891 - 901. 

[2]. ANAD, F.; A. ABTOUT, N. AKACEM, 2005. Algerian repeat station network. The 

IAGA Scientific Assembly in Toulouse, 18-29 July 2005, A-01183. 

[3]. CHAMBODUT A., M. MANDEA, B. LANGLAIS, 2004. Geomagnetic Field models for 

epochs 1995 and 2000. The XXIII General Assembly of the IUGG Sapporo, June 30 - 

July 11, 2003, Abstracts Week B, B254. 

[4]. CHAU H.D., D. GILBERT, J.L. LE MOUËL, N.T.K.THOA, Observations magnétiques 

– Bulletin n° 17 BCMT France. Réseau magnétique de répétition du Vietnam, 

campagne 1997 (2001) 1 - 35. 

[5]. CHAU H.D., D. GILBERT, J.L. LE MOUËL, K. TELALI, F. TRUONG, 2007. 

Observations magnétiques – Bulletin n° 23 BCMT France. Réseau magnétique de 

répétition du Vietnam, campagne 1997, 1 - 40. 

 [6]. CHAU H.D., D. GILBERT, 1999. Normal magnetic maps for the epoch of 1997.5 in 

Vietnam. Journal of Sciences of the Earth, Hanoi, 21(4) (1999) 241 - 253 (in 

Vietnamese). 

[7]. CHAU, H.D., 2007. Normal magnetic models for epoch 2003.5 in Vietnam. Advances in 

Natural Sciences, 20 pgs. (in press). 

[8]. IAGA Working Group V-8, International Geomagnetic Reference Field, 1995 revision 

Geophys. J. Int., 125 (1996) 318 - 321. 

[9]. IAGA Working Group V-MOD, 2003. IGRF (International Geomagnetic Reference 

Field) 9th generation (revised 2003), 

 ( http://www.ngdc.noaa.gov/IAGA/vmod/igrf.html) 

[10]. KORTE, M., E. THEBAULT, M. MANDEA, M. FREDOW, 2005. Benefits from 

European repeat station data in regional modeling. The IAGA Scientific Assembly in 

Toulouse, 18-29 July 2005. 

[11]. KOTZE P.B.,1997. A comparative Evaluation for the Southern African Region of 

DGRF90 and IGRF95 Candidate Models, JGG, 49, No 2,3, (1997) 347 - 352. 

[12]. KOTZE P., M. MANDEA, M. KORTE, 2007. Geomagnetic field models for Southern 

Africa during 2005-2006. The XXIII General Assembly of the IUGG Perugia, Italy, July 

2-13, 2007, 2841. 

[13]. MAKSYMCHUK V., Y. HORODYSKYY, V. TREGUBENKO, M. ORLYUK, 2007. 

The regional secular variations repeat stations network in Ukraine: the new project. The 

XXIII General Assembly of the IUGG Perugia, Italy, July 2-13, 2007, 2852. 

[14]. MATZKA, J., S. MACMILLAN, 2005. Magnetic repeat station results 2004 for Tristan 

da Cunha, South Atlantic Ocean The IAGA Scientific Assembly in Toulouse, 18-29 

July 2005, A-01022. 

[15]. MCKNIGHT J.D., 1997. An Evaluation over the New Zealand Region of Candidate and 

Adopted Models for IGRF 1995, JGG 49, No 2,3, (1997) 353 -358. 

[16]. SOCIAS I., V.M. MARTIN, F.J. PAVON, 2007. Isoporic modeling of the geomagnetic 

field components at the epoch 2005.0. Iberian Peninsula and Balearic Islands. The 

XXIII General Assembly of the IUGG Perugia, Italy, July 2-13, 2007, 7350. 

[17]. UTSUGI M., H. SHIRAI, H. HAMASAKI, T. NISHIKI, S. FUJIWARA, 2004. 

Regional model of the Geomagnetic annual changes in and around Japan island based 

on Noc and Sch analysis. The XXIII General Assembly of the IUGG Sapporo, June 30 - 

July 11, 2003, Abstracts Week B, B255. 

 

46
The new Easter Island magnetic observatory 

Arnaud Chulliat1

, Xavier Lalanne1, Luis R. Gaya-Piqué2, François Truong3, Jean 

Savary3

1

Institut de Physique du Globe de Paris, 4 place Jussieu, 75005 Paris, France 

2

CTBTO PrepCom, Vienna International Centre, PO Box 1200, 1400 Vienna, Austria 

3

Institut de Physique du Globe de Paris – Observatoire Magnétique, carrefour des huit 

routes, 45340 Chambon la Forêt, France 

Abstract 

 A new magnetic observatory has been installed in 2008 in Easter Island, as part of a 

collaboration project between Direccion Meteorologica de Chile (DMC) and Institut de 

Physique du Globe de Paris (IPGP). The new observatory is located on the premises of 

Mataveri airport, at enough distance from the planes but still within the secured area. It is 

designed to meet INTERMAGNET’s operational standards and data quality requirements and 

to provide one-second data. Variation data are provided on a continuous basis since August 

2008 and absolute measurements are expected to begin before the end of 2008. This new 

observatory is extremely isolated, at about 3900 km from the closest INTERMAGNET 

observatory in Huancayo, and will thus significantly improve the global distribution of 

magnetic observatories at the Earth’s surface. Data from the new Easter Island magnetic 

observatory will be particularly useful for main field and secular variation modeling, and for 

global studies of magnetic variations of external origin, such as geomagnetic pulsations and 

the Sq daily variation. 

1. Introduction 

 Considering its geographical location, Easter Island is an ideal spot for installing a 

new magnetic observatory. It is located in the Southern Pacific Ocean, about 3700 km west of 

continental Chile, and actually is one of the world’s remotest inhabited island. The Huancayo 

observatory (Peru), which is currently the most isolated INTERMAGNET observatory (IMO), 

is located 3153 km from Kourou (French Guyana), its nearest neighbor. An IMO in Easter 

Island would become the most isolated of the network, about 3927 km from Huancayo in the 

East direction and 4249 km from Pamatai (Tahiti) in the West direction (Fig. 1). It would be 

one of the very few IMOs in the Pacific Ocean, with Pamataï, Honolulu (Hawaï) and Guam 

(Marianna Islands), and one of the few IMOs in the Southern hemisphere. It would thus 

significantly improve the global geographical distribution of INTERMAGNET, which would 

make it useful for a wide range of scientific studies (Love, 2008). An observatory in Easter 

Island would provide useful data for main field and secular variation modeling, particularly at 

times when no satellite data are available (for example in 2010), for investigating 

geomagnetic jerks (e.g., Olsen and Mandea, 2007), for validating global models of the midlatitude Sq field (Sabaka et al., 2002) and of the quiet-time magnetospheric field (Maus and 

Lühr, 2005), and for studying other external variations such as magnetic pulsations (e.g., 

Uozumi et al., 2000) and solar flare effects (Gaya-Piqué et al., 2008). 

47
Figure 1: Geographical situation of the new Easter Island magnetic observatory (temporary code IPC) 

with respect to the nearest INTERMAGNET observatories. The represented observatories are (in distance 

increasing order) Huancayo (HUA), Pamataï (PPT), Trelew (TRW), Port Stanley (PST), Vernadsky 

(AIA), Teoloyucan (TEO). 

 Based on this motivation, INTERMAGNET recommended about ten years ago the 

installation of a magnetic observatory in Easter Island. IPGP volunteered for this installation, 

but difficulties with a first Chilean partner slowed down the project for several years. The 

project restarted in 2006 with a new partner, the Direccion Meteorologica de Chile (DMC), 

with whom a formal agreement was signed in November 2007. The installation began soon 

afterwards and was completed in August 2008. Absolute measurements are set to begin in 

Spring 2009. This paper presents the layout and instrumentation of this new observatory, as 

well as its first data. 

 Note that it is not the first time a magnetic observatory is installed in Easter Island. 

According to the Data Catalogue of the WDC for Geomagnetism, Kyoto, an observatory (of 

IAGA code EIC) was in operation in Easter Island from March 1958 to January 1964. This 

observatory seems to have experienced several interruptions and problems and only one year 

of definitive hourly values (from February 1963 to January 1964) is available. 

2. Layout and instrumentation 

 The new Easter Island observatory is located on the premises of the Mataveri airport, 

at the end of the runway opposite from the terminal and other airport buildings (Fig. 2). The 

airport single runway is 3.3 km long and was once designed as an emergency landing site for 

NASA’s Space Shuttle. There are usually between one and two flights everyday from and to 

Easter Island. Most take-offs and landings occur on the western side of the runway. 

Occasionally a plane will pass in front of the observatory site (at about 100 m) but the 

perturbation is of short duration (up to a few min) and easily detectable in the recordings. 

Another source of perturbations is a stone quarry nearby with a few trucks coming and going 

and producing disturbances of a few nT for a few sec. 

48
Figure 2: Google Earth view of the Mataveri airport area on Easter Island. The new magnetic observatory 

is indicated by a yellow marker. 

 These perturbations would certainly be considered unacceptable in a less remote 

observatory. However, in the case of Easter Island it was decided to live with them, as the 

choice of the observatory site was heavily constrained by several factors. The site had to: (a) 

be within short distance and easily accessible from the meteorological station (Fig. 3), where 

the team in charge of absolute measurements is based, in order to reduce the amount of time 

needed to travel to and from the observatory site; (b) be within the gated airport area, in order 

to avoid vandalism by human beings and degradations and / or perturbations by wild horses 

(there are several thousand wild horses on Easter Island, many of them with steel horseshoes); 

(c) be far enough from the airport terminal, from the planned location of a second runway to 

be constructed in the coming years, and from the road exiting from the city of Hanga Roa in 

the North; (d) comply with safety regulations imposed by the airport authorities. 

Figure 3: View of the Easter Island meteorological station. 

 A local survey of the observatory site was carried out using an Overhauser proton 

magnetometer in order to avoid large magnetic anomalies. As Easter Island is the summit of a 

large volcanic mountain, the magnetic gradient is high everywhere and there are a lot of 

intense anomalies. This was confirmed by quick surveys in other sites on the airport. We 

selected an area where the gradient is about 20 nT / m in all directions, which was considered 

suitable for the installation of the observatory instruments. 

49
 The observatory was built in two steps. In February 2008, two reinforced fiberglass 

pillars were built at about 20 m from each other. Each pillar was rooted in a 80 cm thick nonmagnetic fiber-reinforced concrete basement buried in the soil. A 2 m x 2 m platform made of 

non-magnetic fiber-reinforced concrete was then built around each pillar, but dissociated from 

it. In May 2008, the absolute hut and the variometer container were installed (Fig. 4). The 

walls of the 2 m x 2 m x 2.20 m absolute hut are made of aluminum and honeycomb 

fiberglass panels, and its roof is a transparent plastic dome enabling the sun light to enter the 

hut from above. There is a sliding door on one face of the hut, and a small hole on another 

face enabling the observer to target the azimuth mark with the theodolite. In order to reduce 

the daily temperature variation in the variometer container, a 14-layer thermal insulation 

cover was wrapped around it and dozens of bottles filled with water (450 l) were placed inside 

of it to increase thermal inertia. As a result, the daily temperature variation within the 

container is less than 1°C. 

Figure 4: View of the absolute hut (front) and variometer container (back) of the new magnetic 

observatory in May 2008. 

 The magnetic variations are continuously recorded by two instruments located within 

the variometer container and distant by about 2 m from each other: a 3-axis homocentric 

fluxgate magnetometer IPGP VM391 (Fig. 5) and an Overhauser-type scalar magnetometer 

GEOMAG SM90. The IPGP VM391 has been developed by one of us (X. Lalanne) at IPGP 

since the mid-1990s and is installed in all IPGP magnetic observatories and in several other 

magnetic observatories throughout the world (Courtillot and Chulliat, 2008). It is 

homocentric, that is, it measures the three components of the magnetic field at the same point. 

This property is particularly useful in volcanic islands like Easter Island, where the magnetic 

gradient is large. The fluxgate magnetometer samples the magnetic field at 5 Hz and produces 

one-second data using a Gaussian digital filter (see Chulliat et al., 2009). The scalar 

magnetometer samples the field every five seconds. 

50
Figure 5: The IPGP VM391 fluxgate magnetometer. 

 Electric power is supplied by the airport and is available from a nearby meteorological 

station. Magnetic data and environmental station parameters are transmitted via an Ethernet 

radio link to the meteorological station office, where the data logging computer is installed1. 

This method was preferred to a fiber-optic cable because of the large distance between the 

container and the office and the impossibility to cross the runway. Data are then automatically 

transmitted to a server in Paris via ftp. Due to an unexpected delay in the customs of some 

pieces of the radio link, the data transmission system started operating in August 2008. 

 Absolute measurements will be performed by the staff of the airport meteorological 

station, using a DI-flux constituted of a thedolite Zeiss 010 and a fluxgate magnetometer 

Bartington MagO1H. Pillar differences (between the absolute measurement pillar and the 

scalar magnetometer pillar in the variometer container) will be regularly measured using a 

scalar magnetometer Geometrix G856. These measurements have not started yet, in part 

because of the lack of data transmission which hampered the training sessions during our visit 

to the island in May 2008. They are planned to start in Spring 2009, once additional on-site 

training has been carried out. The observatory site is easily accessible by airport vehicles 

having permission to drive on the runway. The airport VOR (VHF Omnidirectional Range 

navigation system) antenna, located about 1 km from the absolute measurement pillar, is used 

as azimuth mark. Its precise azimuth was determined using two geodetic GPS receivers to 

within an accuracy of ±10”. The pillar position (in WGS84 geodetic coordinates) is: Latitude 

= 27°10’16.69656” S, Longitude = 109°24’35.53131” W, Elevation = 82.834 m, with an 

accuracy estimated at ±0.0002” (latitude, longitude) and ±0.01 m (elevation). 

3. First data 

 The magnetometers and data acquisition system have been working without 

interruption since their start in mid-August 2008. As an illustration, the variation data for 

August 30, 2008 are displayed on Fig. 6, with a vector magnetometer oriented in HEZ. A 

daily variation is visible on both horizontal components and on the scalar field, with 

amplitude of about 20 nT on the H component and 50 nT on the E component. A very similar 

daily variation was observed at the Pamataï observatory (Tahiti) a few hours later (Fig. 7), 

thus confirming the satisfying operation of the variation recordings in Easter Island. Note that 

the amplitude of the scalar residual is slightly larger in Easter Island, where filtered onesecond data are recorded, than in Pamataï, where filtered one-minute data are recorded; this is 

not surprising since the amount of noise increases with the cut-off frequency. The amplitude 

of the scalar residual daily variation is much smaller than 1 nT, and seems even slightly 

smaller than in Tahiti. 

 

1

 We recently found preferable to install the data logging system near the instruments and to transmit the data to 

an internet node in the station office via the radio link. This upgrade will be implemented in Spring 2009. 

51
Figure 6: Variation data recorded at Easter Island observatory on August 30, 2008. H, E and Z (sampled 

every second) are the three components of the vector magnetometer, approximately oriented in the North, 

East and downward vertical directions. A constant, approximate baseline is applied to the data. Fs 

(sampled every five seconds) is the measured scalar field. Fv is the scalar field calculated from the vector 

components. 

Figure 7: Variation data recorded at Pamataï observatory (PPT) on August 30, 2008. Same conventions as 

in Fig. 6, except that vector data are one-minute data. 

52
 The main problem with the data is the existence of disturbances produced by trucks 

circulating on the road to the nearby stone quarry. These disturbances last a few seconds and 

their amplitude never exceeds a few nT. They would remain unnoticed if only one-minute 

data were recorded. They have a typical signature that cannot be mixed with real sub-minute 

geomagnetic events and have to be removed by hand, leaving a few gaps every day (on 

average) in one-second data. 

4. Summary 

 A new magnetic observatory has been installed in Easter Island, on the premises of the 

Mataveri airport, on the base of a cooperation between Direccion Meteorologica de Chile 

(DMC) and Institut de Physique du Globe de Paris (IPGP). The instruments and data 

acquisition system satisfy INTERMAGNET requirements and are able to produce filtered, 

accurately dated one-second data. The continuous variometer recordings started in August 

2008 and are working properly, except some transmission problems that remain to be fixed. 

Absolute measurements will begin before the end of 2008, once the training of the local staff 

has been completed. This new observatory is designed to become an INTERMAGNET 

observatory after the starting phase. 

 The Easter Island observatory will provide high-quality magnetic data from an 

extremely isolated location, at about 3900 km from the nearest INTERMAGNET observatory. 

This unique geographical location should make it very useful in the future, for internal field 

studies as well as external field studies. 

Acknowledgements 

 Initial contacts with the Chilean partner (DMC) were made by Jean-Jacques Schott, 

Stuart Gilder and Denis Legrand. Funding for the equipment is provided by CNRS / INSU. 

INTERMAGNET is acknowledged for its endorsement and support of this project. This is 

IPGP contribution number 2475. 

References 

Chulliat, A., J. Savary, K. Telali and X. Lalanne (2009), Acquisition of 1-second data in IPGP magnetic 

observatories, Proceedings of the XIIIth IAGA Workshop on Geomagnetic Observatory Instruments, 

Data Acquisition and Processing, June 9-18 2008, Golden, USA. 

Courtillot, V. and A. Chulliat, Eds. (2008), Magnetic results 2005, 264 pp., Bureau Central de Magnétisme 

Terrestre, Paris. 

Gaya-Piqué, L. R., J. J. Curto, J. M. Torta and A. Chulliat (2008), Equivalent ionospheric currents for the 5 

December 2006 solar flare effect determined from spherical cap harmonic analysis, J. Geophys. R., 113, 

A07304, doi:10.1029/2007JA012934. 

Love, J. J. (2008), Magnetic monitoring of Earth and space, Physics Today, 61, 31-37. 

Maus, S. and H. Lühr (2005), Signature of the quiet-time magnetospheric magnetic field and its electromagnetic 

induction in the rotating Earth, Geophys. J. Int., 162, 755-763. 

Olsen, N. and M. Mandea (2007), Investigation of a secular variation impulse using satellite data: The 2003 

geomagnetic jerk, Earth Planet. Sci. Lett., 255, 94-105. 

Sabaka, T. J., N. Olsen and R. A. Langel (2002), A comprehensive model of the quiet-time, near-Earth magnetic 

field: phase 3, Geophys. J. Int., 151, 32-68. 

Uozumi, T., K. Yumoto, H. Kawano, A. Yoshikawa, J. V. Olson, S. I. Solovyev, and E. F. Vershinin (2000), 

Characteristics of energy transfer of Pi 2 magnetic pulsations: Latitudinal dependence, Geophys. Res. 

Lett., 27, 1619-1622. 

53
Acquisition of 1-second data in IPGP magnetic observatories 

Arnaud Chulliat1

, Jean Savary2, Kader Telali2, Xavier Lalanne1

1

Institut de Physique du Globe de Paris, 4 place Jussieu, 75005 Paris, France 

2

Institut de Physique du Globe de Paris – Observatoire Magnétique, carrefour des huit 

routes, 45340 Chambon la Forêt, France 

Abstract 

 Since 2003, INTERMAGNET has been recommending that magnetic observatories 

produce vector data sampled every second, instead of every minute. The scientific 

motivations for this upgrade are mainly that: (a) there is a growing demand from the space 

physics community of one-second magnetic data at the global scale for studying ULF waves 

in the ionosphere and the magnetosphere; (b) observatory data need to be synchronized with 

magnetic data produced by low-Earth orbiting satellites such as Oersted and CHAMP, which 

are sampled at 1 Hz. A user survey lead by Jeff Love (USGS) in 2005 helped define the 

scientific requirements for observatory one-second data, such as data resolution and accuracy, 

time-stamp accuracy and filtering specifications. Following this survey and the operational 

standards issued by INTERMAGNET, IPGP has been developing a new version of its VM391 

3-axis, fluxgate magnetometer and a new acquisition system in order to meet these standards. 

These new instrument and system are now installed in Chambon la Forêt (CLF) magnetic 

observatory and will be installed in most IPGP magnetic observatories in the coming years, 

before the launch of the upcoming ESA Swarm mission. 

1. Introduction

 INTERMAGNET magnetic observatories currently produce and distribute digital, 

one-minute data. Twenty years ago, when INTERMAGNET was born, distributing this type 

of data represented a significant improvement with respect to the analog, hourly data that 

were still the only data product for many observatories. However, it was realized a few years 

ago (at the INTERMAGNET Dourbes meeting, 2003) that observatories needed to move 

towards one-second data if they wanted to catch up with magnetometer networks (such as 

IMAGE, CANOPUS, GMC, etc.) and magnetic satellites (such as Ørsted and CHAMP), 

which were already routinely producing and distributing one-second data. A user survey 

carried out by Jeff Love in 2005 helped assess users’ needs before the observatory community 

took action. 

 The biggest demand comes from the space physicists studying rapid variations of 

external origin, such as ULF waves (e.g., Uozumi et al., 2000) and sudden impulses (e.g., Chi 

and Russell, 2005). For example, Pi 2 pulsations can be used to time and locate substorm 

onset (e.g., Kennel, 1996; see also R. McPherron’s response to the survey). Due to its global 

geographical distribution (Love, 2008), INTERMAGNET would usefully complement 

regional magnetometer networks by providing one-second data in isolated locations not yet 

covered by magnetometer networks. In addition, it is not unreasonable to expect new insights 

into the long-term evolution of the ionosphere and the magnetosphere from observatory onesecond data, since observatories are sustainable infrastructures aimed at operating over 

decades. Observatory one-second data would also be useful to the geomagnetism community 

studying ionospheric magnetic fields such as the Sq field and the equatorial electrojet (e.g., 

Manoj et al., 2006). A CHAMP-like magnetic satellite flying in quasi-polar orbit at about 7 

km/s covers around 420 km between two successive one-minute data at nearby observatories. 

This is to be compared with the width of 600 km of the equatorial electrojet. Synchronized 

54
ground and satellite data would help improving the spatio-temporal description of the 

ionospheric currents such as the equatorial electrojet. 

 Today, more and more observatories are producing one-second data and 

INTERMAGNET is setting up the capability to distribute these data in quasi-real time. Here 

we report recent efforts to produce one-second data in IPGP observatories. These involve an 

upgrade of IPGP’s 3-axis fluxgate magnetometer and the development of a new data 

acquisition system able to properly handle one-second data. 

2. Requirements 

 Jeff Love’s user survey in 2005 lead to the following consensus requirements 

regarding one-second data: 

 Data resolution should be 10 pT; data absolute accuracy is unimportant. 

 The data should be centered on UT second with a time accuracy of about 10 ms. 

Some users expressed the need to have a better (1 ms) time accuracy for the 

purpose of studying wave propagation. 

 One-second data should be obtained by filtering data sampled at higher frequency 

using a digital filter, for example a Gaussian filter with cut-off frequency between 

0.3 and 0.5 Hz. 

These requirements were later adopted by INTERMAGNET. The data resolution requirement 

was increased to 1 pT at the Golden meeting in 2008. 

 Another requirement that emerged from this survey and follow-up discussions was 

that the magnetometer noise spectrum should not exceed the geomagnetic signal spectrum at 

frequencies between 0 and 1 Hz. However, the geomagnetic signal spectrum steeply decreases 

with frequency and its typical value is 1 pT/sqrt(Hz) at 1 Hz. This makes such a requirement 

unrealistic for most (if not all) existing fluxgate magnetometers, which have a larger intrinsic 

noise at 1 Hz. New, low-noise fluxgate magnetometers would have to be developed if one 

wants to fully satisfy the noise requirement. 

3. Upgraded fluxgate magnetometer 

 The VM391 vector magnetometer currently in operation in IPGP magnetic 

observatories (including those in cooperation with other institutions, see Courtillot and 

Chulliat, 2008) is a ring-core, 3-axis and homocentric fluxgate magnetometer (Fig. 1). It was 

originally developed in the 1990s, and its electronics was subsequently upgraded several 

times. In order to satisfy the above requirements for the production of 1-second data, two 

improvements were made to this instrument: 

 Some changes were made in the analog part of the electronic (feedback integrator, 

filters), in order to have a cutoff frequency at 2 Hz for 5 Hz sampling, and a time 

accuracy for the time lag for all three components of about 10 ms. 

 A new 24-bits ADC board, in order to have a 10 pT resolution, was designed and 

built with one ADC per component and all components sampled at the same time. 

A synoptic view of the upgraded magnetometer is represented in Fig. 2. 

 

Figure 1: The IPGP VM391 fluxgate magnetometer 

55
Figure 2: Synoptic view of the upgraded VM391 magnetometer. 

 The frequency response of the analog part of the VM391 was measured by placing the 

sensor in a magnetic shield including a cylindrical coil and submitting it to a sinusoidal 

magnetic field at various frequencies between 0.001 and 10 Hz. The results are displayed for 

each component in Figs. 3 (amplitude) and 4 (phase, converted to time delay). As expected, 

the cutoff frequency for the analog part is 2 Hz. Note that the effects of the 50 Hz and 60 Hz 

mains effects are filtered out by a digital notch filter in the ADC. 

Figure 3: Frequency response of the upgraded magnetometer: amplitude (in dB) vs. frequency (in Hz). 

Figure 4: Frequency response of the upgraded magnetometer: time delay (in ms) vs. frequency (in Hz). 

56
 The time lag for all components is around 130 ms for the tested instrument. This 

average value is used as a parameter by the data logger, which adds it to the other sources of 

time delay in the acquisition chain in order to correct them (see next section). The difference 

in time delay between the three components is smaller than 10 ms. Therefore, using the same 

value for the three components does not violate the above requirement on time accuracy. 

 The intrinsic noise of the VM391 magnetometer was determined by putting it into a 60 

cm long mu-metal 3-layer cylinder, which was itself put into a 2.3 m x 1.7 m x 1.7 m nonmagnetic room located on the premises of the Chambon la Forêt observatory (Fig. 5). This 

room is made of two layers of mu-metal reinforced by an aluminium structure. The static 

magnetic field in the room is less than 100 nT and less than a few nT at the sensor level. The 

results of the tests are represented in Fig. 6 for the X component (they are very similar for the 

other components). The magnetometer noise is about 10 pT/sqrt(Hz) at 1 Hz, which is ten 

times the requirement on noise mentioned in section 2. However, this is not far from the noise 

of the less noisy fluxgate magnetometers, around 7-8 nT. (One such magnetometer, a 

Thomson VFO, has actually been used to test that the non-magnetic chamber filters out 

natural variations down to a lower noise level than that of the VM391.) The intersection 

between the magnetometer noise spectrum and the spectrum of geomagnetic variations in 

Chambon la Forêt occurs around 0.3 Hz, where both signals have an amplitude near 20 

pT/√Hz. We concluded from this test that the cut-off frequency of the digital filter (see Fig. 2) 

should be less than or equal to 0.3 Hz. 

 A Gaussian digital filter was designed accordingly. We decided to use a sampling 

frequency of 5 Hz and to have a smallest filter coefficient of 0.1 and a cutoff frequency of 0.3 

Hz, thus obtaining the following 15-point filter, centered on UT second: 

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ −=

2

2

1 exp)(

τ

t tf , 

where τ = 442.0 . The filter is represented in the time domain and frequency domain in Fig. 7. 

This filter was implemented in the ADC of the upgraded VM391 electronics. 

Figure 5: Non-magnetic room in Chambon la Forêt observatory. 

57
Figure 6: Instrumental noise (X axis) recorded in the non-magnetic room. 

Figure 7: Time (left) and frequency (right) domain plots of the selected Gaussian digital filter. 

4. New data acquisition system 

 A new data logger was developed for the purpose of acquiring 1-second data 

satisfying the requirements listed in section 2. This system, named “ENO3” (“ENO” is an 

acronym for “Enregistreur Numérique d’Observatoire”) includes the following functionalities: 

 GPS time synchronization 

 Time lag correction 

 Configuration through a web interface 

 Data storage in IAGA2002 format 

 Data transmission through the Internet where available 

 Local or WEB control display (Fig. 8) 

 

Figure 8: View of the control screen of the new “ENO3” data 

acquisition system. 

58
 The time lag correction is obtained by interrogating the vector magnetometer at t0+Δt, 

where t0 is the desired measurement time and Δt is the total time delay to be corrected for the 

complete chain. For example, in the case of the VM391, Δt is the sum of: (a) the time lag of 

the analog part of the fluxgate magnetometer (which needs to be determined experimentally 

for each individual instrument, as shown in section 3); (b) one sampling time step of 200 ms 

allowing some additional time delays (smaller than 200 ms) introduced by the numerical 

chain; (c) an additional 2000 ms allowing the calculation of the Gaussian digital filtered data. 

As a result, Δt = 2330 ms for the tested instrument, which an uncertainty of about 10 ms, thus 

satisfying the time accuracy requirement of section 2. 

5. Summary and perspectives 

 In order to acquire filtered one-second data satisfying the requirements of 

INTERMAGNET and the scientific community, in particular that investigating ULF waves, 

the IPGP VM391 fluxgate magnetometer was thoroughly upgraded and tested. Its new 

characteristics are as follows: 

 2 Hz cutoff frequency 

 10 ms time accuracy 

 10 pT resolution 

 10 pT/√Hz @ 1 Hz noise 

A new, low-noise sensor will have to be developed if one wants to achieve a lower 

instrumental noise. However, discussions during the 2008 IAGA Workshop in Golden did not 

demonstrate the urgent need to do so. The digital part of the magnetometer applies a Gaussian 

digital filter with cutoff frequency at 0.3 Hz to produce 1-second data. These data are 

retrieved by a newly developed data acquisition system, ENO3. 

 As of February 2009, the new one-second system is installed and running in the 

Chambon la Forêt, Addis Ababa and Easter Island (Chulliat et al., 2009) observatories. It will 

soon be deployed in other IPGP observatories, including those in cooperation with other 

institutions. 

Acknowledgements 

 This is IPGP contribution number 2476. 

References 

Chi, P. J. and C. T. Russell (2005), Travel-time magnetoseismology: Magnetospheric sounding by 

timing the tremors in space, Geophys. Res. Lett., 32, L18108, doi:10.1029/2005GL023441. 

Chulliat, A., X. Lalanne, L. R. Gaya-Piqué, F. Truong and J. Savary (2009), The new Easter Island 

magnetic observatory, Proceedings of the XIIIth IAGA Workshop on Geomagnetic 

Observatory Instruments, Data Acquisition and Processing, June 9-18 2008, Golden, USA.

Courtillot, V. and A. Chulliat, Eds. (2008), Magnetic results 2005, 264 pp., Bureau Central de 

Magnétisme Terrestre, Paris. 

Kennel, C. F. (1996), Convection and Substorms, 432 pp., Oxford University Press, New York. 

Love, J. J. (2008), Magnetic monitoring of Earth and space, Physics Today, 61, 31-37. 

Manoj, C., H. Lühr, S. Maus, N. Nagarajan (2006), Evidence for short spatial correlation lengths of 

noontime equatorial electrojet inferred from a comparison of satellite and ground magnetic 

data, J. Geophys. Res., 111, A11312, doi:10.1029/2006JA011855. 

Uozumi, T., K. Yumoto, H. Kawano, A. Yoshikawa, J. V. Olson, S. I. Solovyev, and E. F. Vershinin 

(2000), Characteristics of energy transfer of Pi 2 magnetic pulsations: Latitudinal dependence, 

Geophys. Res. Lett., 27, 1619-1622. 

59
Removing spikes from magnetic disturbances caused 

by trains at Ebro observatory

Curto, J.J.(1), Marsal, S.(1), Torta, J.M.(1), Sanclement, E.(1)

(1) Observatori de l’Ebre, CSIC – Universitat Ramon Llull

Abstract. 

Since the electrification of the railway between Barcelona and Valencia in 1973, the 

dominant magnetic disturbances at Ebro observatory were those due to the return electric 

currents from trains. Since 1997, a straightening of part of the railway closest to Ebro 

observatory improved the situation significantly. This work analyzes the level of noise of these 

perturbations basically with a spike-like shape and proposes a method to remove these spikes in 

the final data as we did in the latest years in Ebro. 

Introduction. 

Ebro Observatory was created in 1904 to study geophysical phenomena. Geomagnetic 

and telluric variables were in its scope. Ebro Observatory was placed in a quiet zone, a hill in 

the surrounding of the village of Roquetes and far enough from the town of Tortosa. It was 

settled following the good practice manners for magnetic observatories (Jankowski and 

Sucksdorff, 1996). Agriculture was the main income of the village and there were no industries 

to disturb the natural environment. Magnetic and telluric records were very clean. Thus Ebro 

was selected as a reference observatory to produce the national magnetic chart (de Azpiazu and 

Gil, 1925). There was a railway close to the observatory but trains did not affect the 

measurements. However this changed in 1973 when the electrification of this railway (which 

linked Barcelona and Valencia) was established. Return currents from DC power have a large 

amount of leakage which flow into the earth producing artificial magnetic fields (Dupouy, 

1950; Tokumoto and Tsunomura, 1984; Georgescu et al., 2002; Pirjorla et al., 2007) eventually 

affecting both magnetic and telluric records. Due to the proximity of the railway to the 

observatory (0.5 km) (Fig. 1), the telluric and rapid run records had to be stopped and only slow 

run records remained, although they got very compromised. Perturbations were always in the 

same sense (positive for H and negative for Z). Peaks of 3 nT in horizontal intensity, H, and 15 

nT in vertical intensity, Z, were very common. The extent of these perturbations for the case of 

Ebro was computed by Sanclement (1974). He took the geometry of the railway and the 

position of the feeding substations and predicted the values of the disturbances at different 

locations of the region in order to choose an alternative place to settle a variometric station. 

 (a) (b) (c) 

Figure 1: (a) Reference map (b) Railway on its way through Tortosa before 1996 (blue line = A+B) and 

(c) After 1996 (blue line = A+C). (blue line = A+C). 

60
In 1996, RENFE (the railway operator) decided to straighten the railway design. A 

direct line avoiding passing Tortosa was created 12 km far away. It deviated most of the large 

distance traffic. Only regional trains got to Tortosa, which became the end of the line. This 

point is located 3 km from the observatory (Fig. 1). 

The quality of the records improved and disturbances on Z were much reduced whereas 

disturbances on H remained and became the dominant disturbances (Fig. 2). 

Figure 2: (left) Magnetogram from Ebro for a day (February, 3rd 1991) as representative for the first 

epoch (1973-1995). Red circle shows an episode of disturbances. (right) Magnetogram from Ebro for a day 

(August, 24th 2001) as representative for the second epoch (1996- now). Disturbances over Z were 

dominant in the magnetograms during the period 1973-1996 while those from H became dominant with 

the change of the railway geometry in 1997. 

1. Statistics of disturbances due to railway electrification. 

Aiming to determine which variations had natural origin and which had an 

anthropogenic origin, we compared Ebro (40o 49’N, 0o 30’E) records with those from San Pablo 

(SPT) (39o 33’ N, 4o 21’ W) and l’Aquila (AQU)(42o 23’N, 13o 39’ E) observatories. They have 

similar latitude as Ebro so they should present similar magnetic behaviour. Their diurnal 

variations have very small phase lag because their short angular difference in longitude (taking 

EBR as reference, SPT is 20 min. ahead and AQU is 52 min. behind which is very small 

compared with the period of 24 hours of the diurnal wave). Thus, a simultaneously visual 

inspection of the magnetograms was performed. After a visual inspection, H was chosen 

because of its greater disturbance level. Daily regular variation (Sr) and rapid magnetic 

variations (as pulsations) were coincident in the three magnetograms (Fig. 3). However, other 

rapid spike-like variations appeared only in Ebro magnetograms (Fig. 3). They lasted for several 

minutes and their amplitudes got sometimes values exceeding 5 nT. 

Figure 3: Detail of a piece of a 

magnetogram showing the H 

variation of the Toledo, Ebro 

and l’Aquila observatories for 

the day September, 20th 1996 

of the second epoch, after 

straightening of the railway. 

Values have been shifted to a 

common base to facilitate the 

visual comparison. Arrows 

indicate disturbances in Ebro, 

probably produced by trains. 

61
To check if there is a cause-effect relationship between these magnetic disturbances and 

train movements we performed some statistics. We analyzed a month of data: from September, 

15th 1996 to October, 15th 1996 which is representative for the second epoch. In Fig. 4 (left) a 

histogram of disturbance events is shown. We consider a magnetic disturbance having 

anthropogenic origin if it only appears in Ebro while not in the other two observatories. We 

used civil time for synchronisation. Figure 4 (right) shows the histogram of trains circulating 

through the Tortosa station. 

In general, spikes are in good agreement with trains’ movements, although agreement is 

better for the scheduled departures than for scheduled arrivals. This is consistent with the fact 

that trains getting to Tortosa, which is the end of the line, accumulated delays. Most of the 

disturbances in Fig. 4 (left) -611- have a correspondence with a theoretical train circulation in 

Fig. 4 (right) -542- (89%). The remaining 11 % could be attributed to other movements in the 

Tortosa station not scheduled in the official timetable. 

Figure 4: (left) Histogram of anthropogenic magnetic disturbances and (right) movement of trains in 

Tortosa station during the period Sep. 15th

 1996 to Oct. 15th 1996. 

Fig. 5 allows us having an overview of the time distribution of the disturbances for the 

period Sep. 15th

 1996 to Oct. 15th 1996. Circles point out disturbance events and lines indicate 

the scheduled movements of trains at Tortosa station. Departure times are marked with (s) and 

arrival times with (a). Weekends have fewer circulations and Friday have extra trains (e.g. 

23:16). Most of the disturbances can be attributed to train movements. A detailed analysis of 

the shapes of the disturbances shows that they vary according to the type of train and the kind of 

shunting they do. Disturbances are more intense when trains start off, having sharp beginning 

and decay. When trains brake, the growing part is slow and progressive until they get the 

maximum. 

Although spikes disturbances had small influence in the Mean Hourly Values (MHV) – 

the estimated error in disturbed hours was only some tenths of nT while MHV are usually given 

in units of nT- they produce inappropriate values for minute data. 

62
Figure 5: Overview of magnetic disturbances for the period Sep. 15th

 1996 to Oct. 15th 1996. Circles represent 

disturbances and lines scheduled hours of departures (s) and arrivals (a) of trains at the railway station of 

Tortosa. 

2. Spikes removal. 

As it was mentioned before, although a new railway design improved magnetic noise, 

some spikes greater than 5nT were still found. In these cases, magnetic data did not fulfil 

INTERMAGNET specifications. So it was decided to remove these spikes. Direct filtering was 

not acceptable because it would introduce attenuation and phase delays in rapid variations, so a 

more sophisticated method to remove spikes was needed. Its design involved data treatment in 

several steps: 1) Filtering, 2) Thesholding, 3) Detection and finally 4) Removal. 

First, a high pass filter is applied to the real data. Its output provides a collection of spike 

candidates (fig. 6a). In practice, the operative of this filter was produced by a low pass filter and 

a subtraction of its output from its input (raw data) (fig. 6b). The low pass filter was composed 

by a running mean which smoothes down the magnetic curve removing high frequency 

variations as those from the spikes. 

To get redundancy, an alternative method was implemented in the same procedure. 

When data from an another neighbouring observatory as SPT were available, subtracting both 

sets of raw data and filtering provide a list of candidates which can be compared with that 

obtained from EBR raw data alone. Both lists essentially coincide. 

63
A process of thresholding dismisses small picks not related with the anthropogenic 

disturbances. In our case, 2 nT is an appropriate value for the threshold. 

Figure 6: (a) Block diagram showing the steps needed in the process. Two separate methods were applied: 

1) The main method consisting in a high pass filter on Ebro raw data (upper channel) and 2) An alternative 

method consisting in a high pass filter on differential values between Ebro and San Pablo (lower channel). 

Then, thresholding allows spikes detection. After the application of an activity mask, the final list is 

obtained. (b) High pass filter performance was achieved by subtracting the original data from the same 

data after their pass through a low pass filter. 

Figure 7: Both methods: Ebro alone (in the upper plot) and Ebro + San Pablo (in the bottom plot) give 

similar results. Arrows point out peaks being simultaneously detected by both methods. 

64
An activity mask was designed to prevent the algorithm giving false detections and 

removing natural signal as rapid magnetic variations during periods of magnetic activity. This is 

the case of the Strom Commencement, SC, in Fig 7. These periods are determined in advance so 

where the mask is active no data are removed. 

After removing the spikes from the raw data (wrong data are substituted by a missingdata-flag), final values (red line in Fig. 8) are obtained. The final data set is now essentially free 

from anthropogenic disturbances and fulfils INTERMAGNET specifications. 

Figure 8: Ebro H final values after spikes removal (red line) superimposed to Ebro H raw values (black 

line). Upper plot with detected spikes is reproduced again to facilitate visual comparison. 

3. Conclusions. 

. The configuration of the railway since 1997 reduced the level of noise. 

. The remaining noise is still large enough to justify moving the sensors to a remote 

place. 

. Although disturbed periods embrace only the 3-4% of the day, in those moments rapid 

variations of small amplitude (pulsations, SFE, SI o SSC) could be “masked” 

. In general, spikes disturbances produce variations of some tenths of nT (e.g. 0.3 nT) in 

the Mean Hourly Values, which usually are given in units of nT. 

. Filtering and thresholding is a good method to remove spikes. We applied it to the raw 

Ebro magnetic data for the last two years: 2006 and 2007. 

. A remote place in the village of Horta de Sant Joan, 21 km far from Ebro 

observatory, is proposed to locate a new magnetic station. Its location far enough 

from the closest railway line (24 km) accomplishes Wienert (1970) 

recommendations. 

65
References. 

De Azpiazu,U., Gil, R., Estudios de magnetismo terrestre y de electricidad atmosférica en 

España, en Bauer (ed.), Transactions of Madrid Meeting, October 1924, Internacional 

Geodetic and Geophysical Union, Section of Terrestrial Magnetism and Electricity, 

Bulletin no 5, 86-91, 1925. 

Dupouy, G., Perturbation du champ magnétique terrestre et des courrants telluriques par les 

chemins de fer electrifiés, Annales de Géophysique, V6, 18-50, 1950. 

Georgescu, P., Le Mouel, J.L., Mandea, M., Electric and magnetic perturbations generated by 

d.c. electric railway, Geofizica, Bucuresti, Romania, 40, 69–82, 2002. 

Jankowski, J., Sucksdorff, C., Guide for magnetic measurements and observatory practice, 

IAGA publications, Warsaw, 1996. 

Pirjorla, R., Newitt, L., Boteler, D., Trichtchenko, L., McKee, L. Danskin, D., Jansen van Beck, 

G., Modelling the disturbance caused by a dc-electrified railway to geomagnetic 

measurements, Earth Planets Space, 59, 943-949, 2007. 

Sanclement, E., Perturbación del campo geomagnético debida a la electrificación de las líneas 

férreas, Proceedings de la I Asamblea Nacional de Geodesia y Geofísica, 

p. 787-815, Madrid, 1974. 

Tokumoto, T., Tsunomura, S., Calculation of Magnetic Field Disturbance Produced by 

Electric Railway, translated from Memoirs of theKakioka Magnetic Observatory, 20(2), 

33–44, (pages 27–37 in the English translation), 1984. 

Wienert, K.A., Notes on geomagnetic observatory and survey practice, Ed. 

UNESCO, Brussels, 1970. 

66
Geomagnetic measurements in 2004 in the Republic of Macedonia

M. Delipetrov

(1)

, T. Delipetrov

(1)

, S. Panovska

(2)

, B. Delipetrov

(2)

(1)

Faculty of Mining, Geology and Polytechnics, University Goce Delcev, Stip, Macedonia 

(2)

Faculty of Computer Sciences, University Goce Delcev, Stip, Macedonia 

Abstract 

Geomagnetic investigations on the territory of the Republic of Macedonia carried out up to 

now are presented in this paper. A tectonic description of the Eastern Macedonia zone, Vardar 

zone and Western Macedonia zone is given. The central part of the paper is the performed 

geomagnetic measurements during 2004. The network of repeat stations is shown. 

The applied method and the measurements technique include the observations of the 

declination (D), inclination (I) and total vector (F). On the basis of measured data and information 

from neighboring observatories for daily variations, the maps of elements of geomagnetic field 

were compiled and here are shown. 

Introduction

Over the past several years a number of geomagnetic measurements are done on large 

number of locations in the Republic of Macedonia with the goal to define in more detail the 

geomagnetic field on this territory and to develop a new map of geomagnetic anomalies. Also, the 

measurements were a benefit to plan the installation of a net of geomagnetic stations. 

After the year 2000, a cooperation was started with the Royal Meteorological Institute, the 

Geomagnetic observatory in Dourbes, Belgium and the Department of Geology and Geophysics at 

the Faculty of Mining, Geology and Polytechnics in Stip. Intensive studies and research were 

done on the geomagnetic field in the Republic of Macedonia [1]. During that period, a basic net of 

measuring stations (repeat stations) was installed and a location for a geomagnetic observatory 

was determined. 

In this work, the maps of the elements of the normal geomagnetic field are presented which 

were observed during the year 2004. 

Historical data on the geomagnetic field study 

Over the past years the geomagnetic measurements were carried out on the territory of the 

Republic of Macedonia and other territories of former Yugoslavia, leading to the Grocka 

Geomagnetic Observatory in Belgrade. 

After the declaration of independence of the Republic of Macedonia, activities started for 

establishing a geomagnetic observatory in the country. In that regard, the project entitled 

“Establishing a Geomagnetic Observatory on the Territory of the Republic of Macedonia 

According to the INTERMAGNET Standards” started implementation as a joint project between 

the Faculty of Mining and Geology - Department of Geology and Geophysics and the Royal 

Meteorological Institute - the Observatory in Dourbes. Project leaders are Prof. Dr. Todor 

Delipetrov and Dr. Jean Rasson. The paper presents the results obtained during the past 2004-

2007 time interval. The activities extended by Dr. Jean Rasson in the geomagnetic investigations, 

the creation of the net for the repeat stations, and the selection of the site for the geomagnetic 

observatory, particularly the transfer of knowledge and training of educated people in the field of 

geomagnetism are worthy of note. 

Even the territory of Macedonia had a poor coverage of geomagnetic measurements, we 

conclude that a vast portion of the Southeast Europe has been observed in the 19th century. 

During the 60’s, the territory of Yugoslavia was overhauled with a relatively thick terrestrial 

net of stations and a map on the vertical Z component was made with a scale of 1: 500.000 (see 

Fig. 1). 

67
Fig. 1 Map of vertical Z component of Macedonia 

After the year 1997, the project named Europe Magnetic Project (EEMP) was concluded, 

and in that frame, Report 8 dealing with the Macedonia territory, with cooperation with GETECH 

- Geophysical Exploration Technology was published [2]. 

On these maps, the following research data is presented. 

Fig. 2 Total magnetic field anomaly Fig. 3 Derived total magnetic field anomaly map of Macedonia 

based on 1 km grid upward continued to 1 km above 

topography and linked to Albania, Serbia and Greece 

68
Fig. 4 Vertical field (ΔZ) anomaly map of Macedonia based on 1 km grid at original survey elevation (~ 1 m above 

ground level) 

 Tectonic setting of Macedonia

At present, from the known data from numerous authors [3], a tectonic setting of the 

Republic of Macedonia is presented (Fig. 5 and Fig. 6). The Macedonian territory belongs to the 

Dinarian and Rodopian system. In this part of the Alp’s orogen the Macedonian massif represents 

a horstanticlinorium zone, a mid massif that separates the Dinarides and Karpato – Balkanides. In 

the border area with Bulgaria, as a bolt in the old Rodop mass the elements of Karpato – 

Balkanides are marked, which are separated as a Strumian zone (Kraistide). Such elements, 

separated Rodop from Eastern – Macedonian mass. 

 Fig. 5 Regional tectonic setting of the Republic of Macedonia: I - Cukali Krasta zone, II - Western Macedonian 

mass, III - Pelagonian horstanticlinorium, IV - Vardar Zone, V - Serbo Macedonian massif, VI - Kraishtide 

Zone 

 69
For the neotectonic period on the territory of the Republic of Macedonia, three zones are 

highlighted, shown on Fig. 6. 

 

Fig. 6 Neotectonic map of the Republic of Macedonia 

Geomagnetic research

For a complete monitoring of the geomagnetic field on a given territory, a basic net of 

stations for periodical measurements is needed and a geomagnetic observatory which permanently 

measures the time changes of the geomagnetic field must be set up. 

For a definition of the coefficients as a function of the geographic longitude and latitude on 

a given territory, a relatively homogenous net of geomagnetic stations is needed. Extended 

analysis on data collected in the field, during the realization of the project, allowed the choice of 

15 places on the territory of Macedonia, which constitute the net of geomagnetic stations [4]. The 

basic net of geomagnetic stations serves for periodic measurements to be performed every 3-5 

years. 

The polynomial model is defined by the follow equation: 

λφ 6 Δ⋅Δ+Δ⋅+Δ⋅+Δ⋅+Δ⋅+=ΔΔ λφλφλφ 2

5

2

21 3 4 E ),( aaaaaa

where 

E φ ΔΔ λ),( - is the value of normal field at the point with geographic latitude φ1 and 

longitude λ1 ; 

φ0 and λ0 – geographic latitude and longitude of the reference point; 

φ φ −=Δ φ 01 – difference of geographic latitude in minutes; 

λ λ −=Δ λ01 – difference of geographic longitude in minutes; 

аi – coefficients of the interpolated differences in nT/minutes, minutes/minutes or nT and 

minutes. 

For a given time frame the value of any component of the geomagnetic field can be derived for a 

given place with coordinates φ0 and λ0 . Such polynomial model matches the territory size 

of the Republic of Macedonia. 

In August 2004 measurements of D, I and F were carried out on the grid of repeat stations. 

The names and geographic coordinates of the 15 repeat stations [5] are reported in table 1, with 

measured and calculate components of the geomagnetic field in 2004. Locations of the repeat 

stations are shown in Fig. 8. 

70
 Table 1. Measured and calculated components of the geomagnetic field in 2004 

Repeat Station Geographic 

latitude 

Geographic 

longitude 

Elevation D 

(

o

) 

I 

(

o

) 

F 

(nT) 

H 

(nT) 

X 

(nT) 

Y 

(nT) 

Z 

(nT) 

BAILOVCE 42o

 13' 16" 21o 55' 17" 592 m 2,997 59,269 46758,4 23893,9 23861,3 1249,1 40192,4 

CRNA SKALA 41o

 59' 41" 22o 47' 28" 833 m 3,260 58,883 46920,4 24247,8 24208,5 1378,9 40169,2 

EGRI 40o

 57' 56" 21o 26' 54" 626 m 3,097 57,765 46430,1 24765,7 24729,5 1338,1 39273,6 

GALICICA 40o

 57' 23" 20o 48' 51" 1684 m 2,955 57,694 46291,2 24740,2 24707,3 1275,3 39125,4 

GRADOT 41o

 23' 15" 21o 57' 06" 317 m 3,576 58,083 46438,4 24551,3 24503,5 1531,3 39417,7 

LUKA 42o

 20' 39" 22o 16' 29" 1180 m 3,337 59,390 47036,7 23950,4 23909,8 1394,3 40482,4 

MAVROVO 41o

 42' 58" 20o 43' 38" 1418 m 3,058 58,577 46561,4 24275,2 24240,6 1294,9 39732,7 

NIKOLIC 41o

 15' 54" 22o 44' 36" 300 m 3,146 58,203 46594,8 24551,2 24514,2 1347,3 39601,9 

PLACKOVICA 41o

 47' 41" 22o 18' 13" 677 m 3,229 58,619 46679,2 24307,0 24268,4 1369,2 39851,3 

PONIKVA 42o

 01' 35" 22o 21' 29" 1618 m 2,868 58,994 46825,7 24121,1 24090,9 1206,7 40135,0 

PRILEP lake 41o

 24' 11" 21o 36' 32" 870 m 3,101 58,277 46665,8 24537,7 24501,7 1327,5 39693,8 

 

Fig. 7 Maps of measurements carried out in 2004 on the existing network of repeat stations D (upper left panel), I 

(upper right panel), F (bottom left panel) and Z (bottom right panel) 

71
Fig. 8 Map of the repeat stations on the territory of the Republic of Macedonia 

Measurements carried out in 2004 on the existing network of repeat stations have been presented 

in maps (Fig. 7). Each component varies within the ranges reported in table 2 [4]: 

Table 2: Maximum and minimum values in 2004 for all the geomagnetic components. 

2,868o

< D < 3,576o

57,694o < I < 59,390o

46291,2 nT < F < 47036,7 nT 

23893,9 nT < H < 24765,7 nT

23861,3 nT < X < 24729,5 nT

1206,7 nT < Y < 1531,3 nT 

39125,4 nT < Z < 40482,4 nT 

Conclusion

Analysis of the presented maps shows that the declination is most susceptible to the local 

changes in the geological structure of the terrain. Global neotectonic setting seems to have its own 

part in influencing the behavior of the total field F, but not the same on the map of inclination I. 

Contributed maps from the measurements in 2004 are a good basis for detailed analyses of 

the geomagnetic field, since they can serve to a wide spectrum of useful information. 

References 

1. Arsovski M, Tectonics of Macedonia, Faculty of Mining, Geology and Polytechnics, Stip, 1997 

2. GETECH (Geophysical Exploration Technology), Eastern Europe magnetic project (EEMP) Report 8, Leeds, 

United Kingdom, December 1997 

3. Delipetrov T., Basics of geophysics, Faculty of Mining, Geology and Polytechnics, Stip, 2003 

4. Delipetrov M., Delipetrov T., Delipetrov B., Panovska S., Geomagnetic Data for the Republic of Macedonia 

Obtained during the Measurements Carried out in 2004 , XII

th

 IAGA Workshop on geomagnetic observatory 

instruments, data acquisition and processing, Belsk, Poland, June, 2006 

5. Rasson J. L. and Delipetrov M., Magnetic Repeat Station Network Description, 2004. 

6. Rasson J.L. and Delipetrov T., Geomagnetics for Aeronautical Safety, A Case Study in and around the Balkans, 

NATO ARW, Ohrid, 2005. 

72
PRELIMINARY RESULTS FROM THE FIRST GEOMAGNETIC DEEP SOUNDING 

IN THE WESTERN SECTOR OF THE ANTI ATLAS REGION, SOUTHERN 

MOROCCO 

Domenico Di Mauro1, Faiçal Ramdani2, Massimiliano Fois1, Laura Alfonsi1 

1. Istituto Nazionale di Geofisica e Vulcanologia, via di Vigna Murata, 605 – 00143 Rome, ITALY 

(dimauro@ingv.it) 

2. Laboratory of Physics of the Earth, Scientific Institute, Mohamed V-Agdal University, Rabat, MOROCCO

Abstract 

An array of vector magnetometers was temporarily installed in the Western sector of the Anti 

Atlas chain, Morocco in the frame of the Geomagnetic Depth Sounding (GDS) technique. As 

a fruitful collaboration between Italy and Morocco, the joint project “Terremagnet”, funded 

by the Italian Ministry of the Foreign Affair, was aimed to local observations of the Earth’s 

magnetic field in order to define electric conductivity horizontal and vertical contrasts in a 

tectonic active region. The analysis in time and frequency-domain for tests on the induced 

EM field dimension, computations of single site and coupled site Transfer Functions (TFs) 

and induction vectors configuration are shown. Recorded data are compared to Averroes 

geomagnetic observatory (Morocco) (IAGA code: AVE, lat. 33° 17’ 53” N, long. 7° 24’ 48” 

W, altitude: 230 m a.s.l.). The preliminary results from Morocco are really encouraging and 

pointed out good data samplings that will allow shedding up light on the tectonic setting of 

this peculiar region of the Anti-Atlas.

1. Introduction 

This work aims at characterizing the electromagnetic behavior of the western sector of the 

Anti Atlas chain. The investigated area is located in the Anti-Atlas range, in Jbel Siroua region 

that spans between latitudes 31° 03.30 and 30° 27.31 N and between longitudes 7° 06.38 and 

7° 58.04. 

In the past decades several magnetic soundings have been executed in Morocco in order to 

image the deep structure of this complex tectonic area. Aeromagnetic survey of the Moroccan 

area, as well as ground-based soundings, revealed the existence of conductive structures 

beneath the northern Rif chain (Menvielle & Rossignol, 1982), mid-crustal low resistivity 

structures beneath the Middle and High Atlas area have been enhanced using MT techniques 

(Schwarz et al., 1992). The Anti Atlas deep structure was investigated by means of a regional 

seismic refraction sounding (Makris et al., 1985). However, due to the large distance 

separating the seismic stations that were installed along an EW striking line across the Anti 

Atlas, the results were not useful to clear up in a definitive way the characteristic of the deep 

structure of the region. The deep structure, in terms of electrical conductivity at crustal depth, 

is still not well known, and the use of recent techniques in geomagnetic data processing, as 

the one employed in this study, will hopefully shed up light on the deep structure of the study 

area, that is to say the region around Jbel Siroua-Anti Atlas. 

2. Tectonic setting of the Anti Atlas 

The Anti-Atlas range is a 700 km long Pan African fold formed as a result of the collision 

of the African and Eurasian plates about 80 million years ago, exposes the oldest rocks of the 

Atlas domain. Its basement may be seen as a stable not seismic block, assimilated to the 

African stable Craton (Michard, 1976). 

Precambrian rocks cropping out in domes and anticlines are covered by Paleozoic 

sediments. The Anti-Atlas domain is bordered to the North by the South Atlas fault zone 

(SAF). The mountain range itself is subdivided by the main Anti-Atlas fault into two equal 

parts: Southwestern and northeastern domains. To the South, the Anti-Atlas is limited by the 

Paleozoic of the Tindouf and Reggane basins and to the East by the Saharan Platform. 

73
The Anti-Atlas tectonic setting is generally considered as a “high structure” of the 

Northern border of the African platform that generated during the Mesozoic age (Stets and 

Wurster, 1982). Successively the whole area was interested by several phases of major 

tectonic activity mainly expressed through shortening pulses whose principal directions of 

stress are expressed by system of faults whose strike changed from NW–SE to north–south 

and NE–SW through time. This tectonic regime led to the development of the actual dome 

and basin patterns on scales from 100 m to 10 km. (Burkhard et al., 2006, see Fig.1 for a 

general tectonic view of the studied area).

Figure 1. Tectonic overview map of the Anti-Atlas (from Burkhart et al., 2006) with the survey area in a 

highlighted circular area. 

Landsat TM images effectively distinguish rock types and lineaments within the JbelSiroua region, the one object of this study, and also provide evidence for the continuity of the 

suture zone linking the Siroua and Bou Azzer, suture zone not visible directly on the outcrops 

(Conner et al., 2001). 

The tectonic deformations of this domain are predominately located on its western and 

northern margins. The western margin is represented by a set of faults with a SSW-NNE strike 

that constitute a limit between the Atlantic Ocean and the continental platform. The northern 

margin is more or less broken down and cut by a complex fault system. Except for these two 

margins, the tectonics of the Anti-Atlas is modest and limited to normal vertical faults which 

may have induced flexures and deformations within the cover. These faults are exhibited only 

in the North of the Siroua and Sarrho settlements. The basaltic magmatism of the PlioQuaternary is related to recent tectonic activities, and it is cropping out in the range of Jebel 

Siroua. The recent geodynamic evolution of this area is still under debate. Geoelectric data of 

the High and Middle Atlas was related to fluids which is associated with phases 

transformations linked to others data e.g. intermediate earthquakes and volcanism that may be 

explained by a delamination process active in the area (Ramdani, 1998). 

3. GDS Method 

Among the gainful applications of the recordings of the time variations of the Earth 

magnetic field, the geomagnetic deep sounding (GDS) represents an autonomous and 

74
complementary method to detect electrical conductivity contrasts at depth both for planetary 

studies, when long observations from permanent observatories of the magnetic field are used, 

and for regional and local scale studies using short or very short term observations to map 

lateral contrasts. 

Basically, the relationship between external and internal magnetic fields, at a range of 

frequencies, holds information on the radial conductivity distribution. However, the presence 

of differences in lateral electrical conductivity at depth perturbs the flow of induced currents 

and produces frequency-dependent anomalies in the X, Y and Z components. 

The detection of such anomalies can be facilitated by correlating data from closely spaced 

sites, preferably collected by an array of simultaneously operating magnetometers. The 

detection and following interpretation of frequency-dependent anomalies, related to lateral 

electrical inhomogeneities, give the basis and objectives of the GDS technique. 

In an alternative sense, the GDS combines the principle of both, soundings (in depth) and 

profiling (laterally), for imaging lateral conductivity inhomogeneities at varying depths.

A rough guide to the depth reached by a varying field (which is propagating inside a 

conductor (like the Earth can be considered in this frame) is provided by the so-called “skin 

depth” as as δ = 0.5 (ρ/T).5, given in km when ρ, the electrical resistivity is given in Ω.m

and the period T in seconds (Fig. 2). 

Figure 2. Skin depth for a uniform half-space in dependence on electric resistivity ρ (or conductivity σ) 

and period T (from Encyclopedia of Geomagnetism and Paleomagnetism, 2007). 

Short variations of the field are associated to EM transient of external origin, generated by 

changes in the electrical currents flowing in the ionosphere and magnetosphere, and 

penetrating into the Earth. Here, in turn the electrically conducting layers of the Earth produce 

their own surface magnetic fields. 

Restricting our attention to a local scale, this method makes use of time series of the three 

components of the local magnetic field, as recorded by a vectorial magnetometer. Each 

channel is Fourier transformed to yield a series of complex coefficients which are used to 

estimate the transfer functions according to the following: 

Z(f) = A(f) X(f) + B(f) Y(f) + error (1) 

where X(f), Y(f) and Z(f) are the Fourier transformed channels of the correspondent 

components in the time domain, while A(f) and B(f) are the so-called Transfer Functions. 

75
These functions can be displayed as induction arrows (elsewhere known as Parkinson’s 

vectors or Wiese vectors), according to the following formula to calculate them as the 

following, for each frequency: 

Vr,i = [|Ar,i(f)|2 + |Br,i(f)|2]

1/2, 

and Phaser,i= arctan Br,i(f)/Ar,i(f) 

In geomagnetic deep sounding (GDS) the deduction of the internal electrical conductivity 

distribution is based only on three time-varying geomagnetic field components X, Y and Z (or 

H, D, and Z). For extensive descriptions of the method refer to Gough D. I. (1989), Parkinson 

W. D. (1989) and Egbert G. D. (2002) and reference therein. 

4. Location and period of data collection 

During the summer and autumn of 2007 an array of 10 stations covering an area of about 

6500 km2 was set up (Fig. 3). Their geographic locations and the time periods of data 

collection are listed in Table1. 

Figure 3. Google images of the study area. In the small picture the target area is highlighted with respect 

to the Averroes geomagnetic observatory. White marks indicate the location of temporary stations. 

In each station a self powered system consisting of a three-component Lemi 018 fluxgate 

magnetometer (type Lemi 018, manufactured by Lviv Centre of Institute of Space Research, 

Ukraine), with own built-in digital datalogger, was used. The measurement accuracy was 0.1 

nT and the sampling rate was set at 1 Hz. In order to allow a comparative analysis on datasets 

two complete sets of instrument, available during the measurements campaign, were put in 

operation contemporaneously. Moreover, datasets collected on the field were compared with 

data from the geomagnetic observatory of Averroes (Casablanca), about 300 km away from 

the studied area. Data from Averroes observatory were used as reference to smooth out the 

possible anthropogenic noise in the field. 

76
STATION # Latitude (°, N) Longitude (°, W) Elevation (m) Length of sampling

Site 1 30° 55.56 7° 13.91 1296 24 Jun - 02 July 

Site 2 30° 56.39 7° 15.54 1331 23 Jun - 03 July 

Site 3 30° 49.17 7° 19.28 1509 28 Sep – 06 Oct 

Site 4 30° 31.70 7° 52.46 1078 10 Nov – 18 Nov 

Site 5 30° 28.03 7° 42.88 1707 04 Oct – 13 Oct 

Site 6 30° 45.73 7° 25.03 1730 06 Oct – 13 Oct 

Site 7 30° 59.81 7° 06.38 1238 27 Oct – 03 Nov 

Site 8 31° 03.30 7° 40.92 2176 01 Nov – 09 Nov 

Site 9 30° 27.31 7° 27.07 1755 03 Nov – 10 Nov 

Site 10 30° 44.37 7° 58.04 1028 11 Nov – 18 Nov 

Table 1. Location (geographic coordinates and altitudes) with the timing of data collection for each station. 

The magnetic fluxgate sensors were always buried and covered in order to reach a stable 

thermal condition since fluxgate sensors are sensitive to the temperature drift, which in our 

case was of about 0.1 nT/°C. In Fig. 4 an example of a full day recording with the three 

magnetic components, sensor and electronic temperatures is shown. A visual comparison of 

data from different sites yielded preliminary information on the spatial morphology of the 

magnetic component over the investigated area (Gough, 1989). 

Figure 4. Additional variation of 0.2 nT is artificially produced by a thermal drift during a full day since 

the used equipment had a temperature drift of 0.1nT/°C. Note that the dynamic curve for the buried 

sensor is confined in a range of 2-3 °C while the ambient temperature where the electronic was located 

shows a range of about 15° C. 

Figure 5 shows stacked magnetograms from 2 different stations running simultaneously 

and the contemporarily data from the Averroes observatory. 

77
Figure 5. Sample of time variations along 24-hours for the North (X), East (Y) and vertical (Z) magnetic 

field components recorded simultaneously at station 4, station 10 and at the Averroes geomagnetic 

observatory. 

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,0

0,1

0,2

0,3

0,4

0,5

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

V real (a.u.)

# Harmonics

Phase (°)

# Harmonics

V imag (a.u.)

X Axis Title

Phase(°)

X Axis Title

STATION #1

0 20 40 60 80 100

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0 20 40 60 80 100

-200

-150

-100

-50

0

50

100

150

200

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,0

0,1

0,2

0,3

0,4

0,5

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

V real (a.u.)

# Harmonics

Phase (°)

# Harmonics

# Harmonics

V imag (a.u.)

Phase (°)

# Harmonics

STATION #2

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,2

0,3

0,4

0,5

0,6

0,7

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,0

0,1

0,2

0,3

0,4

0,5

0,6

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

V real (a.u.)

# Harmonics # Harmonics Phase (°)

V imag (a.u.)

# Harmonics

Phase (°)

# Harmonics

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,3

0,4

0,5

0,6

0,7

0,8

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

-200

-150

-100

-50

0

50

100

150

200

V real (a.u.)

# Harmonics

Phase (°)

# Harmonics

V real (a.u.)

# Harmonics

Phase (°)

# Harmonics

Figure 6. Real and imaginary vectors (upper and bottom left columns) in arbitrary unit and their 

corresponding phases (upper and bottom right columns) plotted against harmonics for four stations.

78
5. Preliminary data analysis and discussion 

For each site, we selected several 512-minute X, Y, Z data segments at night time, and 

after a Fast-Fourier transformation into the frequency domain, we estimated the single station 

transfer functions A(f) and B(f) as the final step of an algorithm aimed to minimize the error in 

the linear equation given in (1). We applied a standard cross-spectral analysis (Schmucker, 

1970). We show in Fig. 6 a full representation of the induction vectors compiled from the 

calculated transfer functions for four different stations. Each panel is composed by four 

subplots showing real and imaginary vectors and their corresponding phases for the first 100 

harmonics. Real vector magnitudes are five to six times greater than those of the imaginary 

vector and show similar trend along harmonics. 

The azimuth angles (phase in the plots) are very stable at a value of about 150 degrees 

along harmonics. A confirmation of such character is graphically shown in the figure 7, where 

the whole network response is shown in the form of induction arrows (or Parkinson arrows; 

e.g. Parkinson, 1989). Such arrows are reversed as to point toward zones of high 

conductivities (Lilley and Arora, 1982), and shown in two different periods, in relation to 

medium term (T = 64 minutes) and short term characteristics (T = 16 minutes), as a reflection 

of the effect for the class of physical phenomena investigated and the kind of analysis used. 

The global pattern of Parkinson arrows shows that they mainly point to the NNW 

direction, consistently at two different ranges of probed depth. Such results could be related to 

the predominant regional scale for the depths involved, and with respect to the position of the 

station network, a strong uniform pattern of high electrical conductivity anomaly is present. 

Figure 7. Complete set of real vectors in the Wiese convention in arbitrary unit along harmonics for the 

most peripheric four stations in the four geographic directions. The sequence is not decimated nor 

averaged, and for each arrow a couple of colored arrows indicate the corresponding error both in 

magnitude and in phase. 

A different image is obtained using data sampled at 1 second. Applying the same analysis 

as in the case of 1 minute data segments we compiled new induction vectors over longer time 

series. As shown in figure 8, in the Wiese convention which is simply opposite to the 

Parkinson convention for the Northernmost, Westernmost, Easternmost and Southernmost 

stations of the network, real induction vectors reveal interesting changes of directions and 

different magnitudes along the harmonics. 

79
Figure 8. Wiese (real and imaginary) arrows along harmonics for several sites that indicates the variations 

due to local pattern. At Site 9, no change in orientation of arrows is observed along all harmonics. 

By comparing sites 8 and 9, lateral variations of conductive structures and a clear effect of 

important local pattern are emergent over the regional uniform scale. Further analysis over the 

entire network and an appropriate modeling will return a better knowledge of the 

electromagnetic picture of the area, both locally and in relation to the regional scale. 

6. Conclusion 

 Another independent way to probe our planet is offered by the time changes of the Earth’s 

magnetic field which give us inexpensive methods to look into the Earth. Depending on the 

frequency ranges of such variations, we are able to investigate the planet from “below” and 

from “above” when very long or short time geomagnetic variations are considered, 

respectively.

80
In particular, results from Geomagnetic Deep Sounding can give a valuable contribution 

to establish the tectonic frame, from the electromagnetic point of view, at regional and local 

scale. Such a method was applied in the West sector of the Anti ATLAS chain, Morocco, 

within a project named “Terremagnet” funded by the Italian Ministry of Foreign Affair, under 

a special law to sustain the development policy of Northern African countries. A secondary 

aim of the project was to improve the Averroes geomagnetic observatory capability in data 

collecting, storage and dissemination, possibly via Intermagnet network. 

In the studied area a network of ten stations equipped with vector magnetometers was 

devoted to collect data at 1 Hz sampling rate. This allows to investigate the local and regional 

electrical conductivity response. From the analysis of 1minute data we can preliminarily say 

that an evident and persistent indication of an electrical conductivity high contrast is present 

at NNW direction from the network, probably connected to the magnetic anomalies of the 

Atlantic rifting system. The analysis of 1second data applied to some stations reveals the 

presence of local tectonic superficial structures which need further deep investigations and 

appropriate modeling to better define the electromagnetic imaging of the area. 

Acknowledgment 

The Italian Ministry of Foreign Affair is thanked for the partial financial support to the project 

“Terremagnet”. 

References

Burkhard, M., S. Caritg, U. Helg, C. Robert-Charrue and A. Soulaimani. 2006. Tectonics of the AntiAtlas of Morocco, C.R. Geoscience, v. 338,1-2, .pp11-24 

Conner, J., Hefferan, K., Admou, H., 2001, Remote Sensing Analysis of the Siroua Inlier, Anti-Atlas 

Mountains, Morocco, AGU Spring Meeting, Boston, EOS Transactions v. 82, p. S109. 

Egbert G. D. 2002. Processing and interpretation of induction array data. Survey in Geophysics, 

23:207-249 

Encyclopedia of Geomagnetism and Paleomagnetism, 2007, Gubbins, David; Herrero-Bervera, Emilio 

(Eds.), 1054 pp., ISBN: 978-1-4020-3992-8, Springer editor. 

Gough, D.I., 1989. Magnetometer array studies, Earth Structure and tectonic processes. Review of 

Geophysics, 27: 141–157. 

Lilley,F.E.and B.R.Arora, 1982.The sign convention for quadrature Parkinson arrows in Geomagnetic 

induction Studies, Rev. Geophys. Space Phys., 20,513–518. 

Makris, J., Demnati, A., Klussman, J., 1985. Deep seismic soundings in Morocco and a crust and 

upper mantle model deduced from seismic and gravity data. Ann. Geophys. 3 (3)., 369–380. 

Menvielle, M., Rossignol, J.-C.1982. Conséquences tectoniques de l’existence d’une anomalie de 

conductivité au Nord du Maroc. Can. Journ. Of Earth Sc., vol. 19. 

Michard A. 1976. Eléments de géologie marocaine. Notes et Mémoires Service Géologique du Maroc, 

Rabat 252: 408.

Parkinson, W. D., 1989. The analysis of single site induction data, Phys. Earth Planet.Inter.,53,360–

364. 

Ramdani F., 1998. Geodynamic implications of intermediate-depth earthquakes amd volcanism in the 

interpolate Atlas mountains (Morocco), Phys. Earth Plan. Int., vol. 108, pp. 245-260. 

Schmucker U., 1970. Anomalies of geomagnetic variations in the southwestern United States. Bull. 

Scripts Inst. Oceanogr., Univ. Calif, 13, 165. 

Schwarz, G., H. G. Mehl, F. Ramdani and V. Rath. 1992. Electrical resistivity structure of the eastern 

Moroccan Atlas System and its Tectonic implications. International Journal of Earth Sciences , 

vol. 81,1. 

Stets, J. & Wurster, P. 1982. Atlas and Atlantic structural relations. In Geology of the Northwest 

African continental margin (eds Von Rad, U., Hinz, K., Sarntheim, M. & Seibold, E.), pp. 69–

85 (Springer-Verlag, New York). 

81
A Proposed Metadata Implementation for Magnetic Observatories 

David Fischman, W. F. Denig and Don Herzog 

World Data Center for Solar-Terrestrial Physics, Boulder 

NOAA National Geophysical Data Center 

325 Broadway, Boulder, CO 80305 U.S.A. 

Abstract 

Metadata is simply data about data. It is a reasonably new term used by the environmental 

data community to quantify a need to describe the who, what, when, where, how and why of a 

dataset. Most well-meaning producers and users of environmental data have always, to some 

extent, tried to rely on reasonable principles of data sharing. Prior to the so called “digital 

revolution” most environmental datasets were used by small, close-knit communities of scientists 

within which the provenance of the datasets were well known. However, the recent explosive 

increase in the sheer magnitude, accessibility and applicability of environmental data requires a 

commensurate improvement in our techniques and capabilities to describe environmental data so 

that all interested users, from subject area experts to non-experts, can effectively find, understand 

and utilize available datasets. This is the rationale and the challenge for metadata. Metadata 

standards attempt to use a common format and structured vocabulary through which the 

extended user community can communicate effectively. Unfortunately this rigid vernacular can, 

at times, be difficult to reconcile with the jargon of a particular technology area, say 

geomagnetism. This paper identifies a need within the geomagnetic observatory community to 

adopt the principles of metadata and describes a possible tool for geomagnetic data providers to 

satisfy this need. 

1. Introduction 

The concept of metadata5

 is not new. The very basis of civilization rests on mankind’s 

ability to pass on vital information from one generation to the next such that the knowledge and 

experiences gained can be used to protect and improve society. To say that metadata is history 

is, perhaps, too grandiose a statement for the limited context in which metadata is currently used. 

Nevertheless, metadata can be viewed as the “Rosetta Stone1

” for ensuring that knowledge or, 

more to the point here, environmental data can be a self sustaining body of information. Over the 

past hundred years or so the international community has undertaken various data collection 

campaigns to assemble geophysical datasets and related information. An outcome of the 1957-58 

International Geophysical Year (IGY)9

 was a realization that the long-term stewardship of 

environmental data requires a conscientious and organized means of retaining both the data 

products and the supporting information. The IGY also saw the creation of the World Data 

Centers (WDCs) as the official repositories for scientific data. In spite of the significant changes 

that have occurred since the IGY, the WDC structure has remained largely intact and continues 

to be a major collector and provider of environmental data. Thus it is logical to assume that the 

WDCs would take on a leadership role in scientific data stewardship by encouraging the use of 

metadata for environmental datasets. 

To a large extent the geomagnetic observatory community has been aware of the need to 

retain data about data4

. Since the early 1800’s individual observatory data records have been 

published along with the supporting metadata in geomagnetic yearbooks or other similar 

records2

. While useful, these paper records are neither complete nor particularly well suited for 

digital search and discovery. What is needed is a consistent means of assembling digital 

metadata records for geomagnetic observatory datasets which can subsequently be made 

82
available to individual researchers, industrial users, government officials and data 

clearinghouses6

. Clearly, the responsibility for creating and maintaining the observatory 

metadata records belongs to the data providers themselves which, in many cases, places an undue 

burden on the individuals and organizations. We propose herein an initiative to implement a 

means for responsible data providers to enter their metadata information into a database format 

that can be manipulated based on any client’s requirements. For example, a student would like 

to conduct a statistical analysis of data collected with a proton precession magnetometer verses a 

Overhauser magnetometer7. Currently, the student would have to comb through many different 

publications to find which magnetometers the different observatories use. With metadata 

standards in a metadata database, the student can query the database for magnetometer type and 

gather a wide variety of information in seconds. 

The remainder of this paper discusses, with specific reference to metadata for the 

geomagnetic community, the following; the partnerships among data provider, users and archive; 

the rationale for metadata implementation and an overview of metadata entry information. The 

intent of this paper, as discussed in the latter sections, is to present to the geomagnetic 

observatory community a coordinated approach for metadata entry that is being initiated by the 

WDCs. We believe that a comprehensive set of station-level metadata will improve the inherent 

value of geomagnetic observatory data and ensure the long-term viability of the geomagnetic 

datasets. 

2. Partnerships 

There are four groups of partners in the geomagnetic community. The first group is the data 

providers. They collect the data and generate the metadata. The second group is the set of direct 

users. They are highly educated geomagnetic data users who acquire the data directly from the 

data providers and use the data for complex analyses. The third group is the data managers. They 

organize and make available the data and metadata. The fourth group is the general public. They 

can be individuals with geomagnetic knowledge or no experience at all. The relevance of the 

general public's knowledge is nil when providing the data and metadata. The data managers 

must assume the users are not experts and provide the metadata in a usable and understandable 

format. Hence, any published data products should contain the metadata collected by the 

providers and distributed by the data managers. This shifts the burden of data-use to the public 

and thus eliminates all responsibility from the data providers, assuming the metadata is complete 

and accurate. 

3. Standards 

At present there exists an emerging standard for metadata which is based on prior, more ad 

hoc, approaches. The International Organization for Standardization (ISO) [see 

http://www.iso.org/iso/home.htm] has approved the ISO-19115 “Geographic Information – 

Metadata3

” to define the schema required for describing geographic information and services. 

Metadata, when presented to the community at large, also provides a means of data discovery 

when published in a registry of metadata records. Two such information portals are the Global 

Change Master Directory [see http://gcmd.nasa.gov/] and the Geospatial One Stop [see 

http://gos2.geodata.gov/wps/portal/gos]. The creation of an environmental metadata record 

compliant with the ISO-19115 can be a daunting task. It is therefore useful to weight these 

complexities against the benefits of using a well-defined schema that, in turn, can be published 

and exposed to a wide set of potential users. 

83
Metadata must be collected and provided in a standard form that is accepted and used by the 

entire community. Standards provide a common set of terminologies and definitions. Regardless 

of the level of expertise, the users and providers should be able to understand the metadata and, 

in turn, the data. A common standard allows the users to search the data based upon metadata 

criteria for any worldwide observatory. For example, if a user enters a search field value for the 

instrument’s elevation as 1000, is that feet or meters? Standards also preserve the continuity of 

the data over time. If we look at the previous example, suppose that in 100 years the station no 

longer exists and the data collectors are no longer available for comment. Will anyone know 

whether the elevation was 1000 meters or feet? If the metadata followed a known standard then 

the answer would be yes. 

4. Metadata Requirements 

NGDC recommends requiring five sections of the Federal Geographic Data Committee 

Content Standard for Digital Geospatial Metadata [see http://www.fgdc.gov/metadata/csdgm/] 

when collecting metadata. These sections, which are currently under review for applicability to 

the geomagnetic community, are; 1) identification, 2) data quality, 3) entity and attribute, 4) 

distribution, and 5) reference. 

Identification is the basic information about the data. Who collected the data, when was it 

collected, what was used to collect the data and where was it collected. For example, the United 

States Geological Survey [see http://www.usgs.gov/] collected magnetometer data at the Boulder 

Observatory in 2008 using fluxgate and Overhauser magnetometers. The subjects of the 

metadata will be stored in a database and a possible output from the database will be the sentence 

above. Likewise, the output could have just as easily been an Extensible Markup Language 

(XML) [see http://en.wikipedia.org/wiki/XML] document with tags. 

Data quality is an assessment of the data's accuracy and precision. This requirement can 

contain both textual and numerical expressions. For example, data from the Boulder Magnetic 

Observatory meet the requirements for being an INTERMAGNET8 magnetic observatory. While 

another field contains the value 0.1 nT (nanotesla). This gives the community searchability based 

on numerical values or international standards. 

Entity and Attribute is detailed information about the instruments used to collect the data. 

This requirement could contain such information as calibration coefficients, manufacturer 

specifications, processing algorithms, etc. The detailed information can be outlined in the 

database or links to documents on the manufacturer’s website. 

Distribution Information is the relevant information on how to obtain the data and metadata. 

This field contains the Uniform Resource Locator (URL) [see http://en.wikipedia.org/wiki/URL] 

and contact information for the cognizant data manager. Best management practices dictate a 

well organized data management file system using an authoritative database backbone to 

construct a dynamic website capable of Web Map Services (WMS) [see 

http://en.wikipedia.org/wiki/Web_Map_Service] and File Transfer Protocol (FTP) [see 

http://en.wikipedia.org/wiki/File_Transfer_Protocol] accessibilities. 

Reference identifies the responsible parties associated with the dataset and any constraints on 

the use or releasability of the data. It contains information about who reviewed the data, what 

version of the data is represented, and any time stipulations. It could contain any distribution 

restrictions on the data. For example, the NOAA Space Weather Prediction Center [see 

http://www.swpc.noaa.gov/] would like to use real-time observatory data to forecast solar events 

related to diverting polar commercial airlines in flight. The reference section of the metadata 

84
accompanying the data could contain the appropriate information labeling the dataset as 

preliminary and thus avoid inadvertent usage of the data as definitive information. 

5. Magnetic Observatory Metadata Form 

The cognizant WDCs for Geomagnetism have initiated a program to acquire station-level 

metadata for the geomagnetic community. The first phase for populating a geomagnetic 

observatory metadata database was to develop a standard form containing data entry fields that is 

being distributed to the various observatories. While the form does take advantage of the 

Portable Document Format (PDF) [see http://en.wikipedia.org/wiki/Portable_Document_Format] 

to facilitate data entry by the observatory staff members, the form can be filled out manually as 

needed. The document requests basic information about the observatories including contact 

information, data quality, entity and attribute, distribution information, and reference as 

previously discussed. A copy of this document is provided in the appendix to this paper. The 

goal is to get the data providers to fill out the document digitally and email to NGDC or print and 

mail. The information will then be entered into a database. Once the foundation is laid, the next 

stage will be a fully functional easy to navigate website with assigned authoritative managers 

who will enter and edit their own metadata. 

6. Conclusion 

The WDC for Solar-Terrestrial Physics, Boulder, in collaboration with the WDCs for 

Geomagnetism at Edinburgh and Kyoto, is requesting the active participation of the geomagnetic 

observatory community to build a metadata repository of observatory sites and available data. 

Assembling a metadata database with standard values and multiple export capabilities will 

enhance the scientific community’s use of geomagnetic data. The increased visibility within the 

community combined with improved access to geomagnetic data will also benefit our industrial 

users and society, in general, as well as better inform our national sponsors 

References 

1. Brewer, D.J., and E. Teeter, Egypt and the Egyptians, ISBN-13: 9780521616898, 280 pp., Cambridge 

University Press, Cambridge U.K., 2001. 

2. Butchvarov, I.A. and I.V. Cholakov, Field and Observatory Geomagnetic Measurements in Bulgaria, in 

Geomagnetics for Aeronautical Safety, eds J.L. Rasson and T. Delipetrov, pp. 61-82, Springer, Netherlands, 

2006 (doi: 10.1007/978-1-4020-5025-1_5). 

3. ISO 19115 Geographic Information – Metadata, International Organization for Standardization (ISO), Geneva, 

2003. (see 

 http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=26020) 

4. Love, J.J., Magnetic Monitoring of Earth and Space, Physics Today, 61, pp. 31-37, 2008 

(doi:10.1063/1.2883907). 

5. Marco, D., Building and Managing the Meta Data Repository: A Full Lifecycle Guide, ISBN: 978-0-471-

35523-6, 416 pp., Wiley, Hoboken NJ, 2000. 

6. Papitashvili, V., V. Petrov, R. Clauer, A Saxena, and N. Papitashvili, Virtual Global Magnetic Observatory 

VGMO.NET: A Component of the Electronic Geophysical Year, presented at the 2005 Joint Assemble (AGU, 

SEG, NABS and SPD/AAS), New Orleans, LA, 23-27 May 2005. (see 

www.egy.org/files/PapitaVGMO_AGU_2005.ppt) 

7. Ripka, P. (ed), Magnetic Sensors and Magnetometers, ISBN 978-1-58053-057-6, 494 pp., Artech House, 

Norwood MA, 2001. 

8. St-Louis, B., Ed. (2007), INTERMAGNET Technical Reference Manual, Version 4.3, 88 pp., JAGA, 

Edinburgh. (see http://www.intermagnet.org/) 

9. Tuve, M.A., J. Kaplan, H.Odishaw, Report on the U.S. Program for the International Geophysical Year, IGY 

General Report 21, National Academy of Sciences, 907 pp., Washington D.C., 1965. 

85
Extending backwards to 1883 the French magnetic hourly data series 

Danielle Fouassier1

, Arnaud Chulliat2

1

Institut de Physique du Globe de Paris – Observatoire Magnétique, carrefour des huit 

routes, 45340 Chambon la Forêt, France 

2

Institut de Physique du Globe de Paris, 4 place Jussieu, 75005 Paris, France 

Abstract 

 Geomagnetic field variations have been continuously recorded near Paris since 1883: 

first in Parc St Maur (1883-1900), then in Val Joyeux (1901-1935) and eventually in 

Chambon la Forêt (since 1936). Until 1922, the field was recorded every hour, but only four 

instantaneous hourly values per day (every 6 hours) were published in yearbooks. Other 

hourly data, kept in hand-written notebooks, were never made available, even after the 

computer age. It was not until the end of 2006 that a systematic digitization began. Using a 

purposely designed software, 40 years of the 3 components H, D and Z (about 1,000,000 

hourly values) were keyboarded, thoroughly checked and compared with the already digitized 

six-hour samples. These new data represent a significant improvement for external magnetic 

field studies, especially for rapid phenomena such as magnetic storms. To this date, the 

obtained series is the longest digitized, uninterrupted hourly data series to be made public. We 

hope this effort will trigger similar projects wherever old magnetic data are long forgotten in 

dusty observatory attics. 

1. Introduction 

 There is a growing interest in long series of observatory hourly values, for the purpose 

of studying the long-term evolution of regular variations of external magnetic fields (Le 

Mouël et al., 2004; Chulliat et al., 2005) and that of geomagnetic activity and solar wind 

parameters (Svalgaard and Cliver, 2007a), including possible relationships with climate 

variability (Le Mouël et al., 2005). In addition, it has recently been shown that it is possible to 

build mechanically derived indices of geomagnetic activity from hourly values that are very 

well correlated with traditional indices (Svalgaard and Cliver, 2004; Svalgaard and Cliver, 

2007b; Mursula and Martini, 2007). This makes it possible to duplicate and check traditional 

indices such as the aa-index (Mayaud, 1972), which derivation involves some complicated 

calibration, and to use hourly data recorded in the 19th century to calculate new indices that go 

further back in time. 

 The French national magnetic observatory could provide an interesting contribution to 

this project. Founded in 1883 in Parc St Maur (PSM), in the vicinity of Paris, it was moved in 

1901 to Val Joyeux (VLJ), near Versailles, in order to avoid disturbances generated by a 

nearby electrical tramway. As the Parisian conurbation kept growing, the observatory 

eventually moved in 1936 to Chambon la Forêt (CLF), at the heart of the Orleans forest (Fig. 

1). The full PSM-VLJ-CLF data series is now more than 125 years long. Its homogeneity has 

been improved by the recent reevaluation of all data discontinuities since 1883 (Bitterly et al., 

2005; see Bitterly et al., 2008, for an English translation) and by a thorough data check 

(Fouassier and Chulliat, 2007). However, until recently, only 6-hour data (sampled at 6, 12, 

18 and 24 UT) were available in digital format from 1883 to 1922. Hourly values before 1923 

were only available as hand-written tables stored in the archive of the Chambon la Forêt 

observatory. This paper describes how these 40 years of hand-written data were entered into a 

computer and how they were checked. 

86
Figure 1: Geographical location of the Parc St Maur (1883-1900, easternmost triangle), Val Joyeux (1901-

1935, westernmost triangle) and Chambon la Forêt (1936-today, southernmost triangle) magnetic 

observatories. (Geological map from Schrader & Gallouédec, Atlas classique, Hachette, 1952). 

2. Original data 

 The complete hand-written dataset consists of 1440 monthly tables, one for each 

month and each component during 40 years; the total number of hourly values is larger than 

106

. All tables contain baseline-corrected, definitive data, and no data processing and / or 

reprocessing was needed. Hourly values are spot values throughout the whole dataset (the 

switch to mean values occurred in 1972 at the Chambon la Forêt observatory). Variation data, 

(that is, data before the baseline addition), were stored in preliminary sets of hand-written 

tables. A copy of definitive data was taken in hand-written black covered books, one per 

component, which books were lasting several consecutive years, probably for back-up 

purposes. Except for the years 1883 and 1884 for which the original data sources are lost, we 

did not use this back-up dataset in order to avoid copying errors. 

 Except in a few cases, all monthly tables have the same layout as that of the table 

represented in Fig. 2. Each line contains the 24 hourly values for a given day, from 1h UT to 

24h UT, followed by the sum and mean of the 24 values. Similarly, the sum and mean of the 

28 to 31 values recorded at a given hour during the month are noted down at the end of each 

column. As a consistency check, the sum of all hourly values for a given month was 

systematically calculated from both the column sums and the row sums; the result is noted 

down in the lower left corner of the table. Some additional columns contain the minimum and 

maximum values for each day. Note that the base value indicated in the upper left corner (14° 

in the case of the declination table represented in Fig. 2) needs to be added to the table values 

in order to obtain the full hourly values. 

 Yearbooks are another important data source for the years 1883 to 1922. They contain 

6-hour data sampled at 6h, 12h, 18h and 24h UT (on the top of the hour), as well as the daily 

means, calculated from all 24 hourly values for each component, the daily minimum and 

maximum values (and their timing) and the level of geomagnetic activity. This proved to be 

very useful information in cases where hand-written data were difficult to read or dubious. 

87
Figure 2: Original monthly table of hand-written hourly values of declination measured at the Parc St 

Maur observatory in July 1900. 

3. Data entry 

 Since the amount of data to be entered was very large, an automatic procedure was 

first investigated. Several companies were approached, as well as a research laboratory. All of 

them answered that existing scanning devices were unable to read the data with enough 

reliability, for several reasons: 

 the calligraphy is not uniform; 

 some numbers on the same line are too close to each other, introducing some 

ambiguity as to which digit belongs to which number; 

 some digits cross the horizontal and vertical lines, which thus cannot be used to 

delimit the numbers; 

 some lines can fool the recognition of digits; 

 some tables were damaged by transparent adhesive tape put over them after the 

paper tore; 

 some numbers have been underlined and / or annotated. 

Although not impossible in theory, the development of a specific scanning device was not 

considered cost-effective. 

 The chosen solution was to hire typists who would manually enter the data into a 

computer. French and Vietnamese students were hired and the whole digitization took about 

one year and a half. One of us (D. Fouassier) coordinated the project on a day-to-day basis. It 

88
was found necessary to frequently interact with the typists and check the typed data in order 

to avoid accumulating errors. 

 A new, specific software, GETOLD, was developed for the purpose of facilitating the 

work of typist and reducing the amount of typing errors. This software is written in Clipper 

language. For each day and each component, the typist opens a new window in GETOLD in 

which he or she can enter the relevant hourly values in 24 numbered boxes, as well as their 

mean and sum (Fig. 3). The base value and minimum / maximum values for each day are 

predefined on a yearly basis and can be changed for a specific day. An alarm occurs if the 

data entered is out of the predefined minimum / maximum range. Once the data for a given 

day are entered and the page is validated, the program automatically calculates the mean and 

sum and compares them with those entered (Fig. 4). This simple functionality provides a 

straightforward quick-check of the typed data. 

Figure 3: Data entry screen of the GETOLD software. 

Figure 4: Data checking screen of the GETOLD software. 

4. Data checking 

 The general approach for checking the typed data was to track the differences between 

the various datasets at hand (one-hour values, 6-hour values, sums, means, etc). The first level 

of checking was carried out by the typists while using GETOLD, as explained above. The 

sum check proved very useful in avoiding small typing errors, even if it could happen that two 

small errors would compensate each other within a day. In the case where no sum was 

89
available in the hand-written tables, it was deemed necessary to carry out a tedious doublecheck of all values one by one. This was done for ten years of data, from 1913 to 1922. 

 Another systematic test was to track down lines repeating each other using a purposely 

designed tool. This error occurred several times, due to the unavoidable vigilance lowering of 

typists after a certain time. 

 The typed hourly values for 6h, 12h, 18h and 24h UT were all compared to the 6-hour 

values already available in digital format. Several errors were found that way, mostly in the 6-

hour dataset. Conversely, 6-hour values in a yearbook helped us to correct a wrong base value 

in one of the hand-written table. 

5. One-hour data vs. six-hour values 

 The improvement brought by 1-hour data with respect to the description of rapid 

variations of external origin is conspicuous in Fig. 5, which shows both 6-hour and 1-hour 

data series on the same graph over March 1904. The Sq variation, for example, is well 

described by 1-hour data while it cannot be studied from 6-hour data only. However, this 

improvement is still not enough during very large magnetic storms, for which it is still 

necessary to go back to the original magnetograms. For example, the great storm of 

September 25, 1909, during which the variation reached about several hundreds of nT on the 

H and Z component at Val Joyeux (Fig. 6), is not sampled enough by hourly values (Fig. 7). 

 Monthly and annual means for the considered time interval (1883-1922) were all 

recalculated from the newly typed hourly values. Differences of up to a few nT were found in 

monthly means (Fig. 8; the occasional large differences on that figure are due to errors in 6-

hour data). As expected, differences in yearly means are negligible (Fig. 9). 

Figure 5: Comparison of 1-hour values (in color) and 6-hour values (in black) recorded at Val 

Joyeux from March 1 to March 31, 1904. 

90
Figure 6: Original magnetogram recorded at Val Joyeux during the great magnetic storm of September 

25, 1909. 

Figure 7: Comparison of 1-hour values (in black) and 6-hour values (in color) recorded at Val Joyeux 

during the great magnetic storm of September 25, 1909. 

91
Figure 8: Differences between monthly means calculated from 1-hour values and from 6-hour values at 

Parc St Maur and Val Joyeux observatories (in red). Monthly means are also plotted (in blue). 

Figure 9: Differences between yearly means calculated from 1-hour values and from 6-hour values at Parc 

St Maur and Val Joyeux observatories (in red). Yearly means are also plotted (in blue). 

92
6. Conclusion 

 There are only a handful of observatories providing continuous hourly data series 

going back in time before 1923 (Fig. 10). As a result of this work, the 40-year long PSM-VLJ 

hourly data series is to this day the longest such series publicly available in WDC. It 

continues without interruption until today, thus providing more than 125 years of hourly data 

in digital format. 

 The last step of this project will be to digitize PSM hourly data in 1901, that is, at a 

time where both PSM and VLJ were running in parallel, in order to make this data available 

for cross-calibration of the daily variation at both observatories. 

 What was striking during this project is the near absence of data gaps in the considered 

old data. Not considering the first operation year, there are only 16 missing values in the PSM 

series, from 1884 to 1900, and only 96 missing values in the whole VLJ series, from 1901 to 

1935. This is all the more admirable that observatory routine in those days was much more 

demanding than today. For example, it was necessary to change the photographic paper of the 

variometer at a certain time each day. This sustained dedication of observers and institutions 

behind them represent an inspiring example for our times. 

Figure 10: Longest continuous series of hourly values before 1923 available in digital format at WDC-C1. 

Acknowledgements. 

This is IPGP contribution number 2474. 

93
References 

Bitterly, M., A. Chulliat, D. Fouassier, J.-L. Le Mouël, M. Mandea and J.-J. Schott (2005), Analyse 

des séries de données obtenues dans les observatoires magnétiques depuis leur installation, In: 

Observations magnétiques 2002 - Bulletin No 21, pp. 9-19, Eds. J.-L. Le Mouël and M. 

Mandea, Bureau Central de Magnétisme Terrestre, Paris. 

Bitterly, M., A. Chulliat, D. Fouassier, J.-L. Le Mouël, M. Mandea and J.-J. Schott (2008), Analysis of 

data series obtained in BCMT observatories since their installation, In: Magnetic results 2004 

- Bulletin No 24, pp. 265-275, Eds. V. Courtillot and A. Chulliat, Bureau Central de 

Magnétisme Terrestre, Paris. 

Chulliat, A., E. Blanter, J.-L. Le Mouël and M. Shnirman (2005), On the seasonal asymmetry of the 

diurnal and semidiurnal geomagnetic variations, J. Geophys. Res., 110, A05301, 

doi:10.1029/2004JA010551. 

Fouassier, D. and A. Chulliat (2007), The new BCMT magnetic database, Publs. Inst. Geophys. Pol. 

Acad. Sc., C-99 (398), 128-134. 

Le Mouël, E. Blanter, A. Chulliat and M. Shnirman (2004), On the semiannual and annual variations 

of geomagnetic activity and components, Ann. Geophys., 22, 3583-3588. 

Le Mouël, J.-L., V. Kossobokov, V. Courtillot (2005), On long-term variations of simple geomagnetic 

indices and slow changes in magnetospheric currents: the emergence of anthropogenic global 

warming after 1990?, Earth Planet. Sci. Lett., 232, 273-286. 

Mayaud, P. N. (1972), The aa index: a 100-year series characterizing the geomagnetic activity, J. 

Geophys. Res., 77, 6870-6874. 

Mursula, K. and Martini, D. (2007), A new verifiable measure of centennial geomagnetic activity: 

Modifying the K index method for hourly data, Geophys. Res. Lett., 34, L22107, 

doi:10.1029/2007GL031123. 

Svalgaard, L. and E. W. Cliver (2007a), Long-term geomagnetic indices and their use in inferring 

solar wind parameters in the past, Adv. Space Res., 40, 1112-1120, 

doi:10.1016/j.asr.2007.06.066. 

Svalgaard, L. and E. W. Cliver (2007b), Interhourly variability index of geomagnetic activity and its 

use in deriving the long-term variation of solar wind speed, J. Geophys. Res., 112, A10111, 

doi:10.1029/2007JA012437. 

Svalgaard, L., E. W. Cliver and P. Le Sager (2004), IHV: a new long-term geomagnetic index, Adv. 

Space Res., 34, 436-439. 

94
 Can we use the dIdD magnetometer in the field? 

László Hegymegi, András Csontos, Balázs Heilig Eötvös Loránd Geophysical Institute, H-1145 

Budapest, Columbus utca 17-23, Hungary 

Abstract 

To obtain high quality geomagnetic data in the field, it is necessary to use precise

time-variation correction. The precision of the procedure mostly depends on the data used for the 

correction. The time variation can be different in different places due to the geographic location, 

different geological set-up and the actual activity of the geomagnetic field. A proper solution would

be to record the variation at the site, but in this case we have to ensure nearly the same baseline

stability for the variometer in the field as in an observatory. The paper discusses some of the most

important effects and shows on test data, how can a dIdD magnetometer fulfil these requirements. It

gives a proposal on the configuration of a portable recording station. 

Keywords: magnetic survey, time variation correction 

Introduction 

During a magnetic survey or repeat-station measurements, individual observations are

performed in different times. The magnetic field vector changes during the measurements. This

change has to be taken into consideration in data processing. Generally for correction, the variation

data of a nearby observatory is applied. The problem with this procedure is, that the magnetic 

variation can differ even on small distances and sometimes there is no observatory in the vicinity.

Using variation data of a far-away observatory can reduce the accuracy of the repeat-station 

measurements (Korte, 2001). 

A proper solution would be to record the variation at the site. Up-to-date magnetometers generally 

have stable baselines, but in most cases they need temperature control and a stable pier. Decreasing

the temperature sensitivity of the instrument and eliminating or compensating the pier-tilting effect, 

would be an ideal solution to get rid of the above-mentioned disturbing effects. 

Experiences in Hungary 

According to our experiences in Hungary, at a distance of about 300 km the error due to the 

correction can be higher than 2 nT if we use observatory variation data for reduction (Fig. 1). IAGA

recommendations for first order surveys: “Variation of the vector magnetic field in the vicinity of

the repeat station are determined continuously for 3 or more days of low magnetic activity (as 

determined from an on-site variometer, or from suitable reference observatory), and calibrated to an

accuracy of 5 nT, or better, using sets of absolute observations made at the repeat station” (Newitt,

1996). As example, we present the time variation of the difference between Tihany (THY) and

Hurbanovo (HRB) observatory-recorded X and Z components, as well as the X and Z magnetograms

of Tihany for the same period, for quiet and disturbed days, in Figs 2 and 3, respectively. The

distance between the two locations is 110 km. 

Possibilities for field recording 

In the past it was nearly impossible to take a recording variometer into the field. The fluxgate 

instruments, which measure directly the components of the magnetic field vector, are rather

sensitive to temperature variations. Up-to-date constructions are better but still too instable, unless

some additional temperature protection is applied. 

Proton precession magnetometers are excellent for this job but they only measure the

absolute value of the field vector. However, if we build a coil system around their sensor they can be

used as vector instruments. In the past, due to the considerable size of their sensor a large coil

system was needed to compensate the various field vectors. “ The proton magnetometer is an 

95
outstanding instrument for absolute measurements because of its accuracy and simplicity. But the 

output of the measurement is of no direct importance for the control of variometers, and in order to

convert to usually recorded elements rather large coil systems are used in vector proton

magnetometers. This solution works very well, but the whole instrument is clumsy and it can, for

instance, not be used in the field.” (Lauridsen, 1985) 

In our solution we use the delta I–delta D (dIdD) method

which theoretically is not influenced by external effects. In this case, 

the criterion of keeping the whole system − including the size and the 

resistance of the coil, and the deflection current − stable during one 

measurement period is reduced to about 1 second. Any tilting effect is 

compensated by the suspension system. The only disturbing effect that 

remains is due to mechanical deformations, e.g. the coil axes change 

their mutual positions or they have a rotation with reference to the 

geographical coordinate system. This problem cannot be eliminated

perfectly but can be reduced to a very low level. 

MinGeo – GEM Systems suspended dIdD overhauser

magnetometer 

Fig 1: Error due to the correction vs observatory distance 

96
Fig. 2. Time variation of the difference between Tihany (THY) and Hurbanovo (HRB) observatory

recorded in the X and Z components, as well as the X and Z magnetograms of Tihany for the same

period, for quiet days (pay attention to the scales) 

Fig. 3. Time variation of the difference between Tihany (THY) and Hurbanovo (HRB) observatory

recorded in the X and Z components, as well as the X and Z magnetograms of Tihany for the same

period, for disturbed days (pay attention to the scales) 

97
Temperature effect on different magnetometers 

In the last several years we investigated the effects of temperature variations on different

magnetometers. It can be concluded that most fluxgate magnetometers have too large temperature 

coefficients and their behaviour depends very much on the rate of the temperature change. It is well

known, that the effect of temperature variations on readings are different during heating and cooling

thus determination of a temperature coefficient, as a correction factor is impossible. Testing different

magnetometers, we found dIdD magnetometers the best. Their temperature dependence was found

relatively low, and the instrument response similar during heating and cooling. Accordingly, 

determination of a temperature coefficient for each instrument became possible (Csontos et al.

2007). 

Fig. 4 Temperature dependence of a dIdD magnetometer 

In Fig. 4 we present an example of the effect of a changing temperature environment

obtained in the nonmagnetic heat chamber of Tihany Observatory. The amplitude was usually 30−40 

centigrade and the heating–cooling period was 4 days. 

During the magnetic repeat station survey of Hungary in 2005−2006, a suspended dIdD 

instrument was used for on-site recording with satisfactory results. Regarding stability under

significant temperature differences between day and night, even if the instrument was put on the

ground. The results proved that the difference in the correction between on-site recording and far

away observatory can reach several nT (see Fig 1.) 

Conclusions 

To get high-precision survey data, we should use on-site recording magnetometers. We can 

up practically anywhere, using a piece of solid material as underplate (a piece of laminated wood or

a marble slab of 40 by 40 cm). But it is evident that the instrument has to be protected against direct

sunlight or strong wind. According to our experiences only one or two hours are enough to reach

stable operational conditions. 

98
Using this instrument, no total-field magnetometer is needed as dIdD system also provides

the absolute value (F) if a D/I magnetometer is used with it. 

In the suggested configuration (Fig. 5) the whole system can be powered by a 12 V battery

for several days. The variation data (F, D, I) are recorded on an SD card. For time synchronization, a

GPS antenna can be connected to the palmtop recorder. 

Fig 5. Suggested configuration for field use 

References 

Korte, M.: External influences in regional magnetic surveys. Contributions to Geophysics and 

Geodesy Vol. 31. 273-278. 2001. 

L. R. Newitt, C. E. Barton, J. Bitterly: Guide for magnetic repeat station surveys, IAGA, Boulder, 

USA, 1996. 

Krig Lauridsen, E.: Experiences with the DI-fluxgate magnetometer inclusive theory of the 

instrument and comparision with other methods. Geophysical Papers, Danish Metorological 

Institute, Copenhagen. 1985 

Pankratz, L.W., Sauter, E.A., Körmendi A., Hegymegi L.: The US-Hungarian delta I – delta D 

(DIDD) quasi-absolute spherical coil system. Its history, evolution and future, Geophysical 

Transactions, 42, 195-202, 1999. 

Hegymegi, L., Heilig, B, Csontos, A.: New suspended dIdD magnetometer for observatory (and 

field?) use, Proceedings of the XI. IAGA Workshop on geomagnetic observatory instrument, data 

acquisition and processing pp. 28-33, Kakioka, Japan, 2005 

Csontos A., Hegymegi, L., Heilig, B.: Temperature tests on modern magnetometers, Publs. Inst.. 

Geophys. Pol. Acad. Sc. C-99 (398) 171-177, 2007 

Csontos A., Heilig B., Kovács P.: Hungarian repeat station survey, 2006, 3rd MagNetE 

Workshop on European Geomagnetic Repeat Station Survey 2005-2006, Poster, Bucharest, May 

2007. 

99
GAUSS: Improvements to the Geomagnetic AUtomated SyStem 

A.Hemshorn1,2

, E.Pulz1, M.Mandea1 - A.Hemshorn (e-mail: anneh@gfz-potsdam.de) 

1

Helmholtz-Zentrum Potsdam, Deutsches GeoForschungsZentrum -GFZ, Telegrafenberg, 14473 Potsdam, Germany 2 

Institut f ̈ur Geophysik und extraterrestrische Physik, TU Braunschweig, Mendelssohnstrasse 3, 38106 Braunschweig, 

Germany 

Due to the lack of magnetic measurements in scarcely inhabited regions of the Earth, the geomagnetic community is 

strongly interested in automated absolute measurements to replace the manual operations, generally done with a DI-Flux 

theodolite. GAUSS, the Geomagnetic AUtomated SyStem has been developed in a cooperation of GFZ Potsdam and TU 

Braunschweig. This instrument determines the field intensity in two horizontal directions by means of rotations of a three 

axial fluxgate magnetometer. Together with the standard field variation and total intensity recordings, the method allows for a 

full calibration of the rotated fluxgate sensor, providing the absolute determination of all components. In contrast to the DI 

flux measurements, the reading of angles is obsolete, while the exact orientation of the instrument is controlled by an optical 

set-up. The automated instrument was first installed and run at Niemegk observatory in 2006. After some successful 

measurements in fall 2006, several relevant mechanical components had to be modified to guarantee long-term reliability . 

Here, we present the improvements applied to the system and comparisons of a new time series of measurements with the 

standard observatory data. 

Key words: Absolute measurement, Geomagnetic field 

1. Introduction 

Systematic measurements and modeling of the geo-magnetic field go back to the time of Humboldt 

and Gauss in the 1830s. Humboldt and Gausss understood that only standardized measurement 

procedures conducted all over the world allow for comparability of the results and thereafter efficient 

field modeling. They agreed about distinct measurement methods and organized “measurement days", 

when observers all over the world recorded the field simultaneously. 

Nowadays, the fields variations are recorded automatically, whereas absolute measurements 

providing a stable baseline still have to be performed manually. Absolute, in this frame, means that the 

determination of all systematic errors is part of the measurement procedure. Calibration errors as well as 

the orientation of the result towards a geographic reference system have to be evaluated during each 

measurement. 

After several new developments until the end of the last century, the DI-Flux is the standard 

observatory instrument for absolute geomagnetic measurements over the last 30 years. Using a nonmagnetic theodolite equipped with a single axis fluxgate magnetometer, the field declination and 

inclination are measured absolutely, completed by the recording of the field intensity by a proton or 

Overhauser magnetometer. 

Until recently, absolute measurements have been performed only manually and their full 

automatisation has not been achieved, although the geomagnetic community strongly asks for it. As an 

efficient magnetic modeling relies on an equal distribution of data, automated observatories are needed 

to fill the gaps in the geomagnetic network. Observatories lack in remote areas. 

In the last years, several groups attempted automation of absolute measurements. Their approaches 

differ, as shown below: 

• A Belgian group has developed an acrylic theodolite equipped with angle encoders, a target pointing 

laser and an electronic level. Driven by piezo electric motors, AutoDIF performs a standard 

procedure with automated orientation of the whole instrument [Rasson et al., 2008]. 

• During the XIIIth IAGA workshop on Geomagnetic Observatory Instruments, Data Acquisition and 

Processing, [Auster et al., 2008] demonstrated an automated vector proton magnetometer, which also 

measures the Earth's magnetic field absolutely. The orientation towards a reference system is here 

done by means of a webcam. 

100
• The DIDD-magnetometers have continuously improved, so the period between manually absolute 

measurements can increase. However, the manually absolute measurements cannot be abandoned 

because only the total intensity recordings are absolute, but not the directions [Pulz et al., 2008]. 

We present here GAUSS, the Geomagnetic Automatic SyStem. After a short introduction to the 

fundamentals of the method, we describe precursor instruments. We explain the improvements applied 

to the system and show a new time series consisting of about 300 measurements and covering four 

months of measurements. 

2. Fundamentals of the Method 

The method GAUSS relies on has been described 

previously. The reader is referred to [Auster et al., 

2007a] for more detail

2.1. Brief description of the method 

If a three component fluxgate magnetometer is 

rotated about a very well defined, very stable axis, 

and all three components are recorded at three 

different positions, the magnetic field Brot along the 

axis of rotation can be calculated as follows: 

Fig. 1. The instrument in the absolute house in Niemegk. 

On the table and on the pier in the back, one can see the 

new motors. 

where Bm is the matrix of the three single measurement results: 

2.2. Requirements: Magnetical, mechanical, optical 

Because the magnetometer is turned about in the Earth’s magnetic field, it has to be linear covering a 

range of ±64000 nT. Additionally, it should be small enough to fit in a rotating system. Thus we 

decided for a fluxgate originally designed for space application. The instrument consists of a turn table to 

change between the two measurement directions. A pair of support prisms forms the turn axis for the 

three component fluxgate and holds the “basket” accommodating the sensor. The stability of the 

mechanics is crucial, because only a stable axis leads to sufficiently stable results. 

A telescope focuses a laser beam pointing along the measurement direction. While the magnetometer 

is turning, the laser beam describes a circle on a target, a position sensitive device (PSD), acting as 

azimuth mark. As the PSD is two-dimensional, deviations in the horizontal plane can be observed as 

well. 

3. Previous systems 

The first prototype of this instrument was presented during the Xth IAGA workshop 2002 in 

Hermanus, South Africa [Pulz, 2002], [Pulz et al., 2004]. Measurements were manually carried out until 

May 2006 and showed good results encouraging us to automate this method. The first automated 

instrument was presented at the XIIth IAGA workshop 2006 in Belsk, Poland [Auster et al., 2007a], 

[Auster et al., 2007b]. Though the results showed a better agreement to the observatory standard values, 

several mechanical components turned out to be not reliable in the long term. A redesign became 

inevitable. 

101
4. Improvements 

The new instrument (Fig. 1) is a complete new design with one exception - the basket. In contrast to 

the former instrument, all movements are now carried out by piezo motors, provided by a new 

manufacturer. They offer increased force compared to the old ones and a much more solid design. The 

kind of ceramics, crucial for the friction between the stator and the rotor, were exchanged too. While the 

former motors could only roughly be tuned to the necessary force, drag, and duration of the movement, 

the new ones come with an additional control box, where several parameters can be set digitally. 

Together with the new motors we also implemented angle encoders, that offer high precision 

positioning. With an accuracy of better than 1 arcsec the table rotation returns the axis of rotation 

carrying the laser beam to the optical sensor. A mechanical end stop as used for the previous air pressure 

driven turn table caused heavy mechanical stress, is no longer necessary. Another encoder was installed 

for the rotation of the measurement axis. Here, the precision is less crucial so that an accuracy of 1 

arcmin only was chosen. The shifting of the magnetometer inside the basket has not to be exact, but 

stable. So, we also exchanged the motors and introduced switches, that stop the motion close to the 

desired angle of 90°. All new parts have been checked for their magnetic cleanliness and, if necessary, 

magnetic parts have been removed or exchanged by non-magnetic components. The whole instrument 

has now a weight of about 50kg which guarantees high stability. The proton magnetometer placed on 

outside was substituted by the recordings of the observatory optically pumped Potassium magnetometer. 

The rest of the setup remains the same. 

Fig. 2. Baselines for H (top), D (center), and Z (bottom) component derived from GAUSS’s daily measurements 

(green) compared to the official Niemegk baseline (blue). Lines show calculated baselines, crosses single 

measurements. 

102
5. New measurement results 

The new GAUSS has been set up to measure periodically on 2008/04/18 at the Niemegk observatory. 

After a test phase with eight measurements per day in April and May 2008, GAUSS is continuously 

measuring once per day at midnight since June 2008. The magnetic conditions are most quiet at this time 

of the day, and no daylight disturbs the optical sensors. The procedure starts with a recalibration of the 

table and the basket angle encoders. Afterwards, the measurement steps are still the same: The basket 

turns in six steps 360° forward and backward. During this measurement steps the laser beam targets the 

first PSD. The sensor inside the basket is then shifted to its second position and the turning of the basket 

is repeated. The table rotates the entire upper structure towards the second PSD, and the measurement of 

the magnetic field along the second axis is performed as described before. Fig. 2 shows the NGK 

baseline resulting from measurements performed with a DI-flux in comparison with the GAUSS ones. 

The resulting baselines look promising. We have no explanation for the small drift of the D-component 

during a few weeks. We suppose that one of the three pillars involved in measurements is tilting. Though 

the variance is higher than for the standard observatory baseline, the larger number of measurements 

compensates this effect. An improved evaluation software processes all data automatically so 

considering more measurements does not imply extra efforts. 

6. Conclusion 

GAUSS worked reliably for over four months without any problems. During this time period, more 

than 300 single measurements have been performed, which corresponds to almost six years of of 

measurements, if we consider the nowadays observatory standards with a requirement of a weekly 

absolute measurement. These results let us look forward very optimistically. Some couple of years of 

continuous measurements are necessary to assess the long term stability and reliability. Thereafter, 

GAUSS can be seen as a reliable option for observatories situated in remote regions and as a 

contribution to the worldwide efforts to measure the magnetic field. 

Acknowledgments. We thank Carsten Müller, Oliver Bronkalla, Jörg Wummel and all other 

mechanicans, electricians, and informaticians who helped improving GAUSS. 

References 

[Auster et al., 2002] Auster, H. U., Fornacon, K. H., Georgescu, E., Glassmeier, K. H., and Motschmann, U. (2002). Calibration of fluxgate magnetometers using relative motion. Measurement Science and Technology, 13:1124–1131. 

[Auster et al., 2007a] Auster, H. U., Mandea, M., Hemshorn, A., Korte, M., and Pulz, E. (2007a). GAUSS: A Geomagnetic AUtomated

SyStem. In Reda, J., editor, XII IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, pages 49–

59. Publications of the Institute of Geophysics Polish Academy of Sciences. 

[Auster et al., 2007b] Auster, H. U., Mandea, M., Hemshorn, A., Pulz, E., and Korte, M. (2007b). Automation of absolute measurement of 

the geomagnetic field. Earth, Planets, and Space, 59:1007–1014. 

[Auster et al., 2008] Auster, V., Kroth, R., Wiedemann, M., and Hillenmaier, O. (2008). Automation of absolute measurement using a 

vector proton magnetometer. In Proceedings of the XIIIth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition 

and Processing, Golden/Boulder, USA. 

[Pulz, 2002] Pulz, E. (2002). First results of absolute component magnetic field measurements without the use of a theodolite. In 

Proceedings of the Xth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, Hermanus, South 

Africa,. 

[Pulz et al., 2004] Pulz, E., Auster, H. U., Korte, M., and Linthe, H. J. (2004). Experiences with a new method fort he absolute component 

determination of the Earths magnetic field. Journal of Electrical Engineering, 55:53–57. 

[Pulz et al., 2008] Pulz, E., Jäckel, K. H., and Bronkalla, O. (2008). A Quasi Absolute Optically Pumped Magnetometer for the Permanent 

Recording. In XIII Geomagnetic Observatory workshop, Abstract book. 

[Rasson et al., 2008] Rasson, J. L., van Loo, S., and Berrami, N. (2008). Automatic DIflux Measurements with AUTODIF. In Proceedings 

of the XIIIth IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, Golden/Boulder, USA. 

103
DI3: Improving DI-Flux measurements with a three-component sensor 

A.Hemshorn1,2, H.U.Auster2, M.Fredow1 

1

GeoForschungsZentrum Potsdam, Telegrafenberg, 14473 Potsdam, Germany 2 

Institut für Geophysik und extraterrestrische Physik, TU Braunschweig, endelssohnstrasse 3, 38106 Braunschweig, Germany

Abstract 

DI-Flux instruments, used for absolutely determining the Earth’s magnetic field components, consists of a

non-magnetic theodolite usually equipped with a single-axis fluxgate sensor. Because of recent space missions, 

three-component fluxgate sensors are small enough to fit on the telescope of a theodolite and can measure the Earth’s

magnetic field with an adequate linearity. One can presume that the additional magnetic information should contribute to

an improved measurement result. Performing the standard procedure of declination and inclination readings in the four

possible orientations of the sensor allows one to calibrate and align the fluxgate sensor completely. Transformed into the 

theodolite coordinate system, each set of magnetic field and angle readings provides the complete field vector in the 

geographic reference system, which leads to a welcome redundancy. With this redundancy, random errors can be 

minimised, and systematic effects can be separated during data processing. This should ease some of the difficulties of

precision in the measurement procedure with a one-component sensor. Such a simplification of absolute measurements is 

highly desirable for observatories without permanent, well-trained staff where laypersons perform the observations. 

Key words: Absolute measurement, Geomagnetic field 

(see Figure 1), and performed a measurement procedure 

similar to the traditional one. Without exploiting the angle 

readings, we can 

- Calibrate the sensor completely (i.e. determine offsets, 

scale values and orthogonality) by turning it about two 

independent axes (Auster et al., 2002). 

- Calculate the field along the direction of rotation by 

using three measurement positions Auster and Auster 

(2003) 

- Derive the rotation matrix to transform the 

magnetometer readings into the coordinate system defined 

by the theodolite by aligning one magnetometer axis (Z for 

D-rotation and X for I rotation) with the rotation axis. 

Using the angle readings, each vector measured by the three 

component fluxgate magnetometer is absolute. The 

necessary are explained in detail in the following sections. 

2.1 Calibration of the sensor 

A three-axis fluxgate magnetometer features three different types of calibration parameters: In every single 

component, the output is shifted about the sensor offset and

it is multiplied by a scale factor. Furthermore, the three 

components are not necessarily mechanically perpendicular

so that the sine of their non-ortogonality has to be taken into 

account to obtain the right field components. If the 

magnetometer is rotated about two independent axes and the 

magnetic field intensity is available at the same time, all 

nine calibration parameters can be derived. During the 

standard DI procedure, rotations about the vertical and the 

meridional field are performed and can be used for the 

described calibration purposes. 

1. Introduction 

Geomagnetic observatory measurements are nowadays

subdivided into continuous variation recordings and discrete

absolute measurements. The continuous recordings performed by a fluxgate magnetometer are always subject to

drifts originating from sources as temperature changes, the

instability of the mounting and many others. Regular absolute measurements are therefore necessary to provide a

baseline. 

Since the 1980s, the standard instrument for absolute

measurements of the field components is the DI-Fluxmagnetometer (Jankowski and Sucksdorf, 1996). An

iron-free theodolite is equipped with a single-axis 

magnetometer ontop its telescope. Finding the telescope

positions where the magnetometer indicates zero field in the 

horizontal and the meridional plane allows the absolute

determination of the field inclination and declination by a

simple averaging. As the quality of an absolute

measurement depends strongly on the experience and the

skillfulness of the observer, measurements performed by lay

persons sometimes lack the desired accurateness. 

Here, we present an upgraded DI-Flux-magnetometer:

The iron-free theodolite is equipped with a three component

flux-gate sensor. We obviously obtain additional magnetic

information that can be used for simplifying the

measurement procedure and for obtaining a higher

redundancy. Considering the establishment of several

observatories in remote regions where lay persons perform

the absolute measurements, the method presented here can

improve the results by shifting a certain degree of accuracy

from the on site observer to the mathematical calculations. 

2. Instrumentation and Procedure 

We mounted a vector-compensated fluxgate sensor, provided by TU Braunschweig controlled by a Magson digital

fluxgate electronics on top of a Zeiss 020B theodolite 

104
2.3 Rotation towards a geographic reference system 

The magnetometer is now calibrated and the field along 

some specific direction is known (i.e. the vertical and the 

horizontal intensity). With two samples being part of the 

rotation, the rotation matrix from the magnetometer system

towards the theodolite system can be determined. With the 

theodolite’s angle readings, these directions are defined in a 

geographic frame and one can transform all magnetometer 

readings, taken in arbitrary but well known positions, into 

the geographic reference system. The four absolute 

measurements in vertical and in total force direction are 

supplemented by a large number of vectorial measurements, 

all in a well defined reference system, so that they only have 

to be rotated in the geographic reference system. 

2.4 Resulting procedure 

As pointed out, the magnetometer readings can be taken 

at arbitrary angle combinations. The observer does not have 

to search the angle where the sensor is perpendicular to the 

field but can move to discrete, simple angles. We agreed on 

a procedure for measuring at the field at azimuth mark

direction before and after D rotation, at 4 subsequent positions (0.05 gon steps) close to the telescope orientations perpendicular to F, and at one angle between these positions, 

altogether 44 readings. Each and every position gives a calibrated and adjusted magnetic value; additionally, we obtain 

32 more results for X and Z from the D-and I-rotations. 

Figure 2 shows one example of one complete set of measurement results. 

Other procedures are possible and can be optimised with 

regard to the local field conditions and observers’ 

preferences. A controlbox, time synchronized by GPS, 

collects the magnetic data from the sensor on the theodolite 

and from a distant fluxgate variometer that sends the full 

vector set via radio link on request. 

Fig. 1. Zeiss theodolite equipped with a three-component 

fluxgate magnetometer. 

2.2 Calculation of the field along the axis of rotation 

Rotating a three-component magnetometer about a very

well defined axis and taking measurements at three different

positions during this rotation additionally allows one to

derive the field magnitude in direction of the rotation axis.

The calculation of this component is completely independent of the sensor orientation and rotation angles. It can be

expressed by: 

3. Advantages

The use of a three-component magnetometer for a standard 

DI measurement has numerous advantages: 

Reduction of random errors 

The large number of absolute measurements during one 

sequence can help reduce random measurement errors ( e.g., 

angle reading errors). Figure 2 shows the result for all three 

components derived from one measurement procedure 

where variation reduction is already included. With a Zeiss

020B the standard deviation of the measurement set varies 

between 1nT and 5nT for the different components. 

By increasing the number of measurements per

sequence, the confidence for the mean value increases. 

Performing a standard DI-Flux procedure, in contrast,

causes random errors to accumulate because each reading is 

necessary to calculate D and I. 

Detection of fatal errors 

Fatal errors like typos of angle readings can easily be 

detected. As our procedure features a high redundancy, 

those measurement points can be removed without losing

the whole absolute measurement. This makes the procedure 

less vulnerable to errors of less trained observers. 

where Bm is the matrix of the three single measurement 

results: 

in the sensor coordinate system. Thus, we get as a first 

result the magnetic field component along the turn axes 

defined rotation of the theodolite. 

105
Fig. 4. Taking measurements in Boulder observatory during 

the XIIIth IAGA workshop. 

Fig. 3. The four possible positions, where the magnetometer is

subject to different fields because of field gradients. 4. Conclusion and outlook 

Using a three-component instead of a single-axis sensor

increases the redundancy and thus the accuracy of a DIfluxgate measurement. Systematic errors can be detected. 

Wrong values can be dropped and random errors can be 

minimised with increasing number of angle readings. 

The requirements on the observer are relaxed: Time, 

magnetometer output and variometer readings are recorded

automatically. Angle readings are only necessary at discrete 

(rounded) angles. 

A next step should be to simplify the measurement

equipment, for example implement a new bright display (cf

4). Finally, our measurements during the IAGA workshop 

in Boulder can compete with the results of other observers. 

During the field measurements, theodolites are sensitive

to heating from the sun and can tilt. With our procedure, the

tilting can be quantified and all single measurements can be

reduced to the start, when the levelling was still acceptable. 

Elimination of systematic errors 

Perturbing field gradients can be detected easily. The

measurements performed at the observatory in Boulder

during the IAGA workshop were done on pillar 1 and 3. A

noticeably big number of observations on pillar 1 showed

bigger deviances from the baseline than others (cf. Woods

et al. (2008), this issue). With our method, we even could

indicate the field gradient in z-direction. Between upper and

lower sensor position we measured differences of 5nT.

Though strong gradients at the observatory site are well

known to the personel, this fact cannot be discovered by a

standard DI procedure. A more detailed analysis using 

artificial field gradients is discussed in Hemshorn et al.

(2008) 

References 

Auster, H. U. and Auster, V. (2003). A new method for 

performing an absolute measurement of the geomagnetic 

field. Measurement Science and Technology, 14:1013– 

1017. 

Auster, H. U., Fornacon, K. H., Georgescu, E., Glassmeier, 

K. H., and Motschmann, U. (2002). Calibration of 

flux-gate magnetometers using relative motion. 

Measurement Science and Technology, 13:1124–1131. 

 a 

Hemshorn, A., Auster, H. U., and Fredow, M. (2008). 

DI-Flux Measurement of the Geomagnetic Field using

three axial Fluxgate Sensor. submitted to Measurement 

Science and Technology. 

Adjustable preferences 

Preferred angle adjustments can be determined after the

azimuth mark reading automatically because the approximate orientation between the pillar / azimuth mark

system and the magnetic field vector is fixed. Therefore,

measurement angles can be predicted by the sequence

control automatically for each pillar. This is a further step to

make the measurement more error tolerant. 

Jankowski, J. and Sucksdorf, C. (1996). Guide for Magnetic 

Measurements and Observatory Practice. International 

Association of Geomagnetism and Aeronomy, Warsaw. 

Woods, A., Hattori, P., and Berarducci, A. (2008). Absolute 

Measurement Comparison Preliminary Results. In XIII 

IAGA Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing. 

A.Hemshorn (e-mail: anneh@gfz-potsdam.de) 

No disadvantage 

Finally, with implementing a 3D fluxgate sensor we offer no disadvantage to observers who prefer the traditional

method. By using only one axis, the DI flux method can be

applied as usual. 

106
Fig. 2. Measurement results on pillar 1 at Boulder observatory. Green and blue results originate from the turning of the

instrument according to chapter 2.2, red diamonds from the theodolites angle readings (chapter 2.3). The straight lines indicate

the mean values with standard deviation ontop and below. Obviously, there is a difference between upper and lower sensor

position, most prominent in Z and F and originating presumably from local field gradients at the observatory site, see text. 

107
Some Considerations on Secular Variation of the Magnetic Field in Mexico 

Hernández-Quintero E., Cifuentes-Nava G., Caccavari-Garza A., and Flores-Soto X. 

Instituto de Geofísica. Universidad Nacional Autónoma de México. Circuito Institutos s/n. Ciudad Universitaria. 

México D.F. (estebanh@geofisica.unam.mx) 

Foreword 

The secular variations of the Earth’s magnetic field have been a problem with several puzzles 

still under debate. It is generally accepted that features such as the systematic decrease in dipole 

moment and the westward drift of the field are the main features of the secular variation (Rikitake, 

1966). The westward drift of the geomagnetic field has been interpreted to imply that the outer core is 

rotating more slowly than the mantle; resulting in an apparent westward drift of the core produced 

field. Other specific manifestations of secular variation are, for example, changes in local declination 

or inclination, in scales of 100 years or so with changes of 30° or more (Backus et al., 1996). 

The steady drift that can be easily modeled by polynomial equation in time; but present some 

quite sudden changes in the records such as in 1969; when a magnetic jerk (a sudden change of 

more or less one year of duration) took place in Europe (Bloxham et Al., 2002). According with such 

view, it is possible to recognize that if the detailed regional nature of the westward drift could be 

determined, one might hope to be able to form some ideas about the structure of the convective 

motions in the core. Field and observatory work as well, would be a contribution in solving this kind of 

problems as presented. 

Brief Description of Studies and Field Work in Mexico 

The first magnetic observations known in Mexico were made by Cavendish in La Paz, Cabo 

Corrientes, and Manzanillo in 1587. In the Gulf of Mexico the oldest magnetic measurement was 

made by Sir Arthur Dudley in 1625. Those observations were the beginnings of a development led to 

the first magnetic maps in this area. By these years geographically diverse observations were carried 

out, and were useful for the analysis of the field’s morphology and evolution deduced from these 

measurements. Observations since 1830, were made in order to produce magnetic charts, with two 

main desired results: knowledge of the magnetic phenomena (the fact that only in the last few 

decades this information has been used for modeling secular variation and its origin at the core-mantle 

boundary, supports the great value of historical data-bases), and for navigation purposes. 

During 1906 and 1907 the first magnetic survey was carried out by the Observatorio 

Astronómico de México (Astronomical Observatory of Mexico). This first attempt to obtain a rational 

magnetic chart was supported by the Carnegie Institution of Washington D.C. The survey was made 

almost the whole Mexican territory; nevertheless Baja California peninsula was not included. Sandoval 

(1950), by assessing such results and combining with other surveys improved grid observations 

avoiding near magnetic anomalies. 

In 1948, Geophysics Institute started its operation, into National University of Mexico (UNAM). 

The department of Geomagnetism was in charge of several magnetic surveys in 1950, 1951 and 

1952. The last field survey was made in 1990. Figure I(A) shows the historical observations made in 

Mexico between 1947 and 2008. 

As a first evaluation on the field work, it is possible to recognize three years with a high number 

of repeat stations visited during the XX century: 1952, 1965 and 1990. The lack of information in the 

whole analyzed space increased the RMS values and mean. In the same periods of time the sites 

where the field was measured are mostly in the central part of Mexico (1995-2008). The 1952 chart is 

a poor parameterized map due to the lack of data and with the biggest RMS value (11.54’ for 

declination), in comparison with the 1907 chart (not displayed in this figure with 52.73’ for declination), 

and that for 1990 (18.7’ for declination). 

108
Figure 1. A) Geomagnetic repeat stations visited during 1947-2008. It is possible to recognize those 

years when reoccupation campaigns were carried out. B) Map of the basic 36 geomagnetic repeat 

stations in Mexico selected for this study. 

Some efforts had been made in order to assess and improve the national grid of geomagnetic 

repeat stations in Mexico. Geostatistical methods for example were applied to the 1990 chart, with 50 

first order magnetic stations distributed in all the Mexican area. The result of this study reported 3° 

(333 km) as a correlation distance; with a variance of 8’ and standard deviation of 4.2’ in the four 

orthogonal directions (Flores-Ruiz et Al., 2000). Such a chart was the best ever made, because it was 

a high continuous isotropic model. This defined the right number of magnetic stations that belong to 

the network since decades ago. 

Considering the different objectives of a repeat stations network (to create a valid secular 

variation model, monitoring the systematic decrease of dipole moment, the westward drift, and other 

local changes such as jerks); stations should be of first order, marked as permanent as possible with 

monuments to keep an accurate reoccupation. In the specific case of Mexico it has followed the policy 

proposed by IAGA (every five years). 

Even through the problems faced in the past surveys, a westward drift of geomagnetic field 

have been observed; and a systematic decrease in the dipole moment as well. Paying attention to 

certain improvements; variables in field work (portable gps, fluxgate theodolites, software and 

hardware in general) it was possible to make a monitoring of the behavior of the main magnetic field in 

the last decades in Mexico. 

Observations and Interpretations 

We analyze a time series of data for 1950-1990. Figure 1A shows a wider period in repeat 

stations reoccupation (1947-2008), in order to avoid biased maps the analysis was focused in this 

specific interval of time, the information beyond is scarce, regarding the whole area of the country (on 

average five reoccupations per year after 1990). Nevertheless there are several studies focused on 

specific phenomena, such as the agonic line across the Yucatan Peninsula. 

Field surveys considered those geomagnetic elements measured: declination (D), inclination 

(I), and Intensity (F). A detailed quality-control analysis of information was carried out in order to set 

up a final data-base of 36 repeat stations (Figure 1B). Stations with less than three occupations 

between 1952 and 1990 were discarded (geographic covering is still poor). The years selected for this 

study were 1952, 1955, 1959, 1964, 1970, 1974, 1979, 1984, and 1990. 

109
 According to Mizuno (1984) the methodology adopted was based on three main phases: The 

reduction of the geomagnetic elements to a defined epoch and a geographical reference position (in 

this special case, Teoloyucan). The second step is to compute the annual relative changes in the 

geomagnetic elements. Finally, a set of magnetic charts (D, I and F displayed in Figures 2, 3 and 4) of 

these results. For the first stage was adopted the relation: 

PR=Pt+[TR-Tt] 

Where Pt is the value of a component measured at the “t” epoch, and in a defined site into the national 

grid of geomagnetic repeat stations. PR is the value of a component referenced to the analyzed year. 

TR is the value of the component under consideration for the same year in Teoloyucan. Tt is the value 

of the particular analyzed component in the “t” epoch in the Teoloyucan. 

Pt is the variable we are interested in, by means of data collected in the repeat stations, and 

measured in parallel at Teoloyucan. By these means the values of the geomagnetic elements were 

calculated at the defined epochs and reduced to Teoloyucan. The resulting curves for each of the 36 

repeat stations were adjusted by squares minimums method (with a careful selection of degree, 

according to the number of data in every station), then the approximation functions were calculated. 

Finally a first derivative was applied in each component, in order to analyze the secular variation in the 

elements of the geomagnetic field. The maps of secular variation for D, I and F are displayed in 

Figures 2, 3 and 4. 

In the maps for magnetic declination (Figure 2), a regional anomaly is observed since 1952. 

This is located in the northeast part of the country, in the limits of the Gulf of Mexico coast, the state of 

Tamaulipas, and Texas. Negative values are observed in the Yucatan peninsula and Chiapas as well. 

For the next years (1955-1958) a steady tendency is observed with relatively small changes. By 1959 

westward drift is conspicuous. This anomaly migrated westward. By 1964, the corresponding map 

shows a positive high in the area of Nuevo Leon state. The values are mostly negative to the east of 

this anomaly. For 1970 the anomaly covers a wider area, emphasizing its positive character; and 

extending to the next year analyzed (1974). In this specific map, the anomaly seems to reach 

Chihuahua State. The area is smaller by 1979, when the anomaly reduces back its influence. At this 

time a relative low becomes visible in Sonora coast (Guaymas). This is the first negative value in the 

west area of Mexico for this analysis. In the 1984 chart, the main anomaly is deformed by a 

comparative low at south (Cd. Victoria); this low remains since the 1979 chart at western Sonora. The 

1990 chart (six years later) shows a more uniform anomaly, which extends an arm to Sinaloa 

(negative low in Sonora disappears for this epoch). In most of the cases, the repeat stations network 

don’t suggest severe changes. 

Total Intensity maps are shown in Figure 3. In 1952 an anomaly is located in the Mexico-USA 

border; the minimum is located in the state of Tamaulipas; the maximum (140 nT) is centered in the 

sea in front of the international border. It seems that the anomaly extends to the east of Yucatan 

peninsula. In the center of the country it is possible to see another relative low (in this area is the 

geographic position of the reference station; Teoloyucan). For 1955 chart, anomalies are in the same 

positions approximately; nevertheless it is clear the displacement eastward of Yucatan anomaly inside 

the Caribbean Sea. The Northwest of Mexico remains with the same tendency since 1952. In 1959 the 

northeast anomaly is diffused and expanded without the minimum observed in 1952 and 1955; and 

the Yucatan anomaly appears to experiment a sudden westward drift (in just four years). In the same 

map, the northwestern area of Mexico is influenced by a magnetic anomaly (near Rosario station). For 

1964, the morphology of the main anomaly at the border between Mexico and USA is getting a wider 

extension east-west, in the Yucatan Peninsula the observed anomaly in the past is completely lost. In 

the northwest still remains the anomaly presented in the years before, with minor changes in its 

amplitude. This pattern persists until 1970 with a low located in the south center of the country. 

Increasing in amplitude may explain the vanishing of some others anomalies such as the observed 

near El Rosario station, and Yucatan before. The behavior is the same until 1979; nevertheless it 

would be possible a small drift to the west of all anomalies. 

The westward drift of these anomalies are more evident in the 1984 chart, amplitudes and 

geographical appearance are mostly the same as it was in 1979. 1990 appears to keep a similar 

110
geometry as those anomalies well defined since the first chart displayed, but with an enlargement in 

amplitude for that centered precisely in the border Mexico-USA (200 nT). 

Magnetic charts for 1955 show a main positive anomaly in the border of Tamaulipas and Texas that 

strongly controls the whole behavior of northeastern part of the country and the Gulf of Mexico as well. 

Another anomaly is centered approximately near the Teoloyucan magnetic observatory. This fact 

should be taken carefully because an anomaly around a station could be considered as an artifact; it is 

possible to see the same effect for Baja California stations, in 1955 and 1959. In 1964 epoch is still 

possible see the same tendency, although the extension of the anomaly is wider. By 1970 two 

features are remarkable, first a drift to the west of a low anomaly centered in Sabinas station; another 

significant low over Teoloyucan is present too, maintaining this tendency until the next chart displayed 

(1974); where only the anomalies of Matamoros repeat station and Teoloyucan are the most notable. 

In 1979 the anomaly located at north disappear completely due possibly to the increasing of amplitude 

of the anomaly nearby Teoloyucan station (this goes from -0.33 to -0.77) and rising up its amplitude 

until 1984, (-1.30). This way of behaving still reaches the 1990 chart (-2.2) and no other anomalies are 

possible to see. 

Discussion 

Analysis of the evolution in time of geomagnetic field by means of Mizumo’s procedure brings 

with itself several conditions that should be carefully followed. A rigorous practice on repeat stations’ 

reoccupation; besides a precise geographic location are some of the requirements. Following the 

historical development of data acquisition in Mexico, it is necessary to assess and adapt the 

information in order to obtain satisfactory results regarding the specific conditions of field work, 

evolution of instrumentation in different surveys, and the magnitude of Mexican territory, for example. 

The analysis of the three components of the geomagnetic field (D, I and F) shows the 

characteristics of the secular variation (Bloxham et Al., 1989; 2002; Mizumo, H. 1984). A set of 

anomalies are influenced by a west drift of the field in time. Some episodes are possible related with 

acceleration in such time evolution, for example the change observed in total magnetic field in 

Yucatan peninsula (1952-1955). A dynamic geomagnetic field with a behavior is observed in the whole 

picture. Features such as the reported geomagnetic Jerks in literature (Bloxham et Al., 2002) are not 

evident in this analysis. Nevertheless the variation in time of amplitudes, shape and geographical 

positions of the observed anomalies may represent an influence of other phenomena related such as 

jerks. 

Conclusions 

This analysis determines that the historical work developed in the past in Mexico is valuable in 

the sense of acquisition data in the field, and the information accumulated over all surveys carried out 

in the past. Secular variation of the magnetic field of Earth is evident in time evolution. Changes are 

observed in amplitude, shape, size and geographical position. 

No conclusive evidence was found in the data related with geomagnetic jerks reported in 

literature (1969, 1978, 1991, and 1999). Nevertheless the analysis shows evidence of a dynamic 

evolution of the field that could be related with this phenomenon. It is recommendable to carry on with 

reoccupations in the national grid of Mexico. A valuable database should be completed regularly as 

possible; and by the other hand, the surveys can be focused to specific epochs and areas in the 

country. A second stage of this project involves the combination of Mizumo’s procedure with other 

techniques (i.e. Spectral Analysis, or Wavelet Analysis) besides a careful review and updating of field 

information. 

111
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112
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113
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114
References 

Backus, G.; Parker, R.; and Constable, R. Foundations of Geomagnetism. Cambridge University. 369 

p. 1996 

Bloxham, J.; Gubbins, D.; Jackson, A. Geomagnetic Secular Variations. Philos. Trans. R. Soc. Lond. 

Ser. A Vol. 329, no.1606. p 415-502. 1989. 

Bloxham, J.; Zatman, S. and Dumberry M. The Origin of geomagnetic jerks. Nature. Vol. 420. p 65-67. 

2002. 

Bullard, E.C., Freedman, C., Gellman, H. and Nixon J. The westward drift of the Earth’s Magnetic 

Field. Phil. Trans. Roy. Soc. Lond. A243, pp. 67-92. 1950. 

De Meyer, F. Westward drift in the secular variation of the main geomagnetic field derived from IGRF 

models. Institute Royal Meteorologique de Belgique. Publ. Scient. And tecn. No. 38. 26 p. 2005. 

Flores-Ruiz, J.H.; Marines-Campos, R.; Hernández-Quintero, J.E., Campos-Enríquez J.O.; UrrutiaFucugauchi, J. Evaluación Geo-estadística de la Red de Estaciones Magnéticas de la República 

Mexicana. Revista de Geofísica del Instituto Panamericano de Geoografía e Historia. No.52-53. P. 

107-122. 2000. 

Jault, D.; Gire, C. and Le Mouel, J.L. Westward drift, core motions and exchanges of angular 

momentum between core and mantle. Nature, v.333. p. 353-356. 1988. 

Jonkers, R.T.; Jackson A.; and Murray, A. Rev. Geophys. V.41 no. 2. 1006, doi: 

10.1029/2002RG000115. 2003. 

Kelly, P. and Gubbins, D. The geomagnetic field over the past five million years. Geophys. J. Int. 128, 

315-330. 1997. 

Mizumo, H. Rapid and Episodic Variations of the Geomagnetic Secular Variation Field. Bull. 

Geograph. Survey Inst. Japan. Vol XXIX, part 1. 101 p.p. 1984. 

Rikitake, T. Elimination of non-local changes from total intensity values of the geomagnetic field. Bull. 

Earthq. Res. Inst., 44. 1041-1070. 1966. 

Sandoval, O.R. Elementos magnéticos en la República Mexicana. Servicio Geomagnético. Instituto de 

Geofísica. UNAM. México. 182 pp. 1950. 

115
The Effects of Missing Data on Mean Hourly Values 

 

Donald C. Herzog 

National Geophysical Data Center/CIRES 

NOAA E/GC1 

325 Broadway 

Boulder, Colorado 80305-3328 USA 

email: don.herzog@noaa.gov 

 

Abstract 

Quantitative results are presented showing the effects of missing data on the computation of 

Mean Hourly Values (MHVs). Three stations, representing high-latitude (College, Alaska), midlatitude (Boulder, Colorado), and low-latitude (San Juan, Puerto Rico) locations, were examined over 

three levels of magnetic activity. Using K-Index values as a selection criterion, 3-hour intervals of 

data were chosen for Active (K=8), Moderate (K=5), and Quiet (K=0) periods for each station. Data 

sets from various days with equal K-Index values were combined to form 24-hour days for all three 

activity levels for each station. Sets of deletion intervals, ranging from 5-minutes through 40-minutes, 

were constructed from a random number generator and applied to the hours of the complete data sets. 

The MHVs from these deleted hourly values were then compared with the original MHVs. Two 

methods for computing MHVs for the missing data sets were considered. Results show that, even at a 

high-latitude station, more than half the data can be missing during the hour and still produce an 

accurate MHV; refuting the notion that a simple rule can be used for computing MHVs with missing 

data. The preferred choice of the two methods used for computing MHVs with missing data is shown 

to depend on the number of missing minutes. A simple method for estimating the expected error in an 

MHV with less than 30 minutes of missing data using only the K-Index is proposed. 

 

1. Introduction 

One problem that has persisted in the operation of geomagnetic observatories, 

particularly since the advent of digital electronic data collection, has been what to do about 

computing mean hourly values (MHVs) when data are missing during the hour. The question 

is what accuracy can one expect from computing an hourly mean if all of the data are not 

available? One of the difficulties in trying to study this question is that there are so many 

independent variables that can affect the result. It is well known, for example, that 

observatories at higher latitudes produce larger amplitude variations in the magnetic field 

components than those of stations located at middle and lower latitudes. Consequently one 

would expect to obtain larger errors in MHV computations at higher latitude stations than at 

lower latitudes for the same number of missing minutes. And it seems intuitively clear that 

computing MHVs when the missing data occur during active fluctuations in the field of the 

hour will generally result in larger errors than when the data are missing during quiet times. 

Additionally, one would expect that the more data that are missing during an hour, the larger 

the resulting error. These intuitive expectations can be summarized by the graph in Figure 1. 

Other factors play a role as well. If the missing minutes are distributed uniformly over the 

hour, a better representation of the true MHV might be generated than if the missing data 

were grouped during a particularly active segment of the hour. But the reverse could occur if 

the grouping happened to fall in a particularly quiet period during the hour. Since the data 

would be missing, there would be no way of knowing a priori which scenario one might be 

facing, unless there existed the opportunity to reference data from a nearby observatory that 

had a complete data set for that interval. 

116
Figure 1. Expected RMS Error in MHVs (arbitrary scale) for different station latitudes and number of 

missing minutes in the hour. 

 

Although references to this problem are scarce, recent workers have looked into the issue 

using different approaches. Mandea (2002) studied one-minute values over 120 days during 

the year 1999 using one high-latitude, one equatorial, and two mid-latitude observatories. 

This study considered sets of disturbed and quiet days, based upon a K-Index selection 

criterion, for all three magnetic components, with up to 30 minutes of data missing in 1-

minute intervals, and concluded that a reliable MHV could be computed if less than 10% of 

the data were missing. 

Schott and Linthe (2007) took a statistical approach using data from the Port-AuxFrançais (PAF) observatory for the years 1999 to 2003. They considered distributions of 

actual data gaps in the observatory records and evaluated the dispersion of the hourly means 

due to those data gaps. They concluded that their goal of discovering a simple rule for 

whether or not to compute a MHV could not be achieved because of the number of variables 

involved. 

 

2. Study Design 

Because of these multiple possible ways in which the missing data could influence the 

computed MHVs, it was decided to randomize the minutes of the hour deleted. By using a 

random set of deletion minutes, it was hoped that it would thus provide a sampling of the 

various possible cases of missing minutes encountered in the real-world situations. In 

principal, a large enough ensemble of random deletion sets would include virtually all the 

scenarios of missing data usually encountered in actual observatory operations. 

Unfortunately, the current study only provides a small set of such possible cases. 

Three U.S. Geological Survey magnetic observatories were used in this report, providing 

a high-latitude (College, Alaska), mid-latitude (Boulder, Colorado), and low-latitude (San 

117
Juan, Puerto Rico) data set. The 1-minute data were obtained from the INTERMAGNET 

CD-ROM set for various years. For the magnetic activity selection criteria, K-Index values 

were used. The K-Index is often used as a measure of magnetic activity, and it provides a 

way to control for magnetic activity over a range of latitudes. The K-Index is a 3-hour quasilogarithmic scale developed to measure magnetic activity ranging from zero, representing 

virtually no magnetic activity, to nine representing extreme magnetic activity (Mayaud, 

1980). In order to have a somewhat consistent scale of magnetic activity between 

observatories at high latitudes, where field variations can be quite large in amplitude, and 

those at low latitudes, each observatory is assigned its own set of amplitude ranges 

corresponding to the various K-index levels. Thus, for example, a K-Index of 5 at College 

corresponds to a lower limit of magnetic activity range of 350 nanoTeslas (nT) over the 3-

hour interval, while at San Juan this same K-Index level corresponds to a lower limit of 

magnetic activity of 40 nT. The intent was to have the K-Index compensate for the influence 

of latitude on magnetic activity, so that a K-Index of 7 at College and San Juan would 

represent the same magnetic storm intensity despite the actual differences in the range of 

magnetic fluctuation amplitudes at the two latitudes. Thus, the K-Index was used as a 

selection criterion for magnetic activity. 

Three K-Index levels were used for the magnetic activity selection criteria corresponding 

to Quiet (K-Index = 0), Moderate (K-Index = 5), and Active (K-Index = 8) conditions. Table 

1 shows all the K-Index levels for the three stations used in this study. Although the K-Index 

is determined based upon whichever is the greater in the range of variation of the Horizontal 

component (H) or Declination (D) during the 3-hour interval, the 1-minute values of the Xcomponent (North-South) were used in this study. This was a matter of convenience since the 

more recent data sets available are provided in the Cartesian (X, Y, and Z) configuration. 

 

Table 1. Lower limits of K-Index levels for College, Boulder and San Juan (in nT). 

 

The data used in this analysis were selected by joining eight 3-hour intervals of 1-minute 

values with equal K-Index levels into a composite 24-hour day for each station and for each 

of the three activity levels. With one exception, the composite data days were complete data 

sets with no missing minutes for any hour, and these were used as the control set. The only 

exception was for 8 missing minutes during one hour in the Boulder Active case (November 

8, 2004, 07:41-07:48 UT), and no special adjustment was made for this hour. The missing 

minutes were ignored in the computation of the control and deleted MHVs for this case. The 

collection of hours used to construct these composite days is shown in Table 2, and a plot of 

the composite days for College for the three activity levels is shown in Figure 2. Similar plots 

(not shown) were constructed for Boulder and San Juan. 

118
Table 2. Dates and intervals used to construct 24-hour composite days according to three K-Index levels. 

Figure 2. Plot showing composite days for Active (red), Moderate (green), and Quiet (blue) activity 

levels at College. Plots were constructed by merging data from 3-hour time segments with equal K-Index 

values to produce a 24-hour day for each activity level. 

 

Deletion sets of 5-, 10-, 15-, 20-, 25-, 30-, 35-, and 40-minutes, from minutes 1 to 60, 

were produced using a random number generator. The resulting deletion set minutes are 

given in Table 3. The table has been split into two parts to fit onto a single page. The 

number of deleted minutes is shown along the horizontal axis and the deletion sets are 

119
shown along the vertical axis. The minutes of the hour deleted are given in the 

corresponding cells. The numbers have been collated for better visualization. These deletion 

sets were then applied in turn to the composite data days for each hour, and differences were 

computed between the decimated hourly means and the original MHVs. 

 

Table 3. Minute values of the hour used in deletion sets. Deletion sets (DEL-05 for 5-minutes, etc.) are 

shown on vertical axis and number of deletions on horizontal axis. Minutes of the hour deleted are given 

in the cells. The table is split into two parts to fit on one page.

 Two methods of computing MHVs were used. First, a standard arithmetic mean method 

was used by adding the remaining minute values after decimation, and dividing that by the 

number of remaining minutes in that hour. The second method involved generating a linear 

regression line using the remaining undeleted values during the hour and taking the 

midpoint of this line as the MHV. The first method is the standard procedure used at most 

observatories, while the second method was suggested to the author as being commonly 

used in the satellite community. 

Differences were computed between the MHVs of the complete data set and those of the 

various data sets after the deletions were applied. The 24 differences were then used to 

compute the Root Mean Square (RMS) error for each day according Equation 1, where x

i

corresponds to the individual MHV difference for each hour, and x-bar is the average of the 

24 differences for the day. N is always 24 in this case. 

(1)

RMS error is a measure of the dispersion of a set of values about a mean, and is useful for 

cases where the differences can be both positive and negative. 

120
3. Results 

Figure 3 shows the results of these 

computations for the arithmetic mean 

method. It gives the RMS errors for 

all three stations, activity levels, and 

eight deletion sets. Similar results (not 

shown) were observed for the linear 

regression method of computing 

MHVs. A number of interesting 

points stand out from these results. 

First, the expected continuous 

increase in the RMS error with 

increasing number of missing data 

points does not always hold. While 

that is generally the case for deletion 

sets of 10-, 15-, and 20-minutes, the 

errors for the 5-minute deletion sets 

are as large or larger than those for the 

10-minute deletion sets, and in some 

instances, are larger than those cases 

with 25 and even 30 missing minutes. 

And this pattern holds for all three 

stations during both the active and 

moderately active conditions. For the 

Quiet-level case, the errors are so 

small that it is difficult to make 

similar generalizations, except to note 

that none of these errors exceeded 0.5 

nT. This was true even at the high 

latitude station with 66% of the data 

missing. This is remarkable and 

indeed encouraging since it indicates 

that during quiet activity levels, 

having very little data for the hour is 

still likely to produce a reasonably 

accurate MHV, regardless of the 

latitude. 

Notice too, that in virtually every 

case (except for the quiet condition), 

the errors observed for the 15- and 20-

minute deletion sets were larger than 

those for the 25- and 30-minute deletion sets. But most notable, perhaps, is the result for the 

35-minute deletion case. For all stations and both non-quiet activity levels, this deletion set 

consistently produced the largest RMS errors. It was also nearly always larger than those 

cases with 40 missing minutes. This result suggests that one cannot always rely upon the 

intuitive notion that the more missing data there are the less accurate the MHV will be. 

Figure 3. Plots of the RMS errors for College (red), 

Boulder (green), and San Juan (yellow) in nT. Top, 

middle, and lower panels for Active, Moderate and 

Quiet activity levels respectively. Note the changes in 

scale for the different activity levels. 

121
 One might be inclined to attribute these results to some artifact of a statistical anomaly 

resulting from the particular random minute values used in the magnetic field data set. But 

this can hardly be the case since the results hold for all stations and activity levels. And, 

with one exception, these are independent data sets derived from different parts of different 

days. In only one time segment (Boulder and College for moderate activity levels both used 

12-APR-2005 hours 00:00-06:00), was the same time period used in constructing the 

composite magnetic days. Otherwise, there are no temporal connections between the data 

points used in constructing the composite data days for the three stations. If the same days of 

data had been used for all three stations, one might argue that this anomaly reflected 

something about a relationship between the same magnetic field being recorded at different 

stations. But this was virtually never the case. 

Another interesting finding from these results is that there is very little difference in the 

RMS errors for the mid- and low-latitude stations. Even for the active magnetic levels, the 

average difference between Boulder and San Juan for all deletion sets is slightly more than 1 

nT. For the moderate cases, the average difference between these two is less than 0.1 nT. 

Again this runs counter to what our intuition might lead one to believe. 

The numerical results of these studies, both for the Arithmetic Mean (A.M.) method of 

MHV computation and the Linear Regression (L.R.) line midpoint method, are given in 

Table 4. In general, a 

comparison of these two 

methods shows that the 

Regression Line midpoint 

procedure produces smaller 

errors for cases where there are 

less than approximately 25 

missing minutes while the 

arithmetic method seems to do 

better when there are more 

than 25 minutes missing from 

the hour. If this pattern holds 

true in other tests, perhaps one 

method might be more 

preferable than the other 

depending upon how many 

minutes are missing from the 

hour. Figure 4 provides a 

typical example, in a graphical 

representation, of the 

comparison between these two 

methods of MHV computation. Similar results (not shown) were obtained for the other 

stations and activity levels. 

Table 4. Numeric results for all stations, activity levels, and 

deletion sets. Results are in nT. Upper panel is for Arithmetic 

Mean (A.M.) method, and lower panel is for Linear Regression 

(L.R.) method. A = Active, M = Moderate, Q = Quiet. 

The large disparity in RMS errors between the low-/mid-latitude observatories and 

College makes it difficult to form generalizations about the accuracy of MHVs computed 

when data are missing during the hour. However, one may try to “normalize” the errors in 

the sense of viewing them as a ratio of the given K-Index for each station and activity level. 

In other words, how do the errors compare to their K-Index value for some particular 

122
activity level? 

Figure 5 shows the result of dividing the RMS error by the K-Index value for each station 

and activity level using the deletion sets of 5 to 30 missing minutes. For example, the RMS 

error for Boulder in the active case (BOU-A) with 30 missing minutes is 3.3 nT (See Table 

4, A.M. case). The lower limit value 

for a K-Index of 8 at Boulder is 330 

nT. This produces a ratio of 0.01 or 1 

percent. Doing this for all cases 

where there were 30 minutes or less 

of missing data is shown in Figure 5. 

The 30-minute cutoff was chosen to 

avoid the anomalous 35-minute case. 

Remarkably, except for the Quiet 

cases, virtually all of these 

“normalized” ratios were less than 2 

percent, and most were less than 1.5 

percent. This is a potentially very 

useful result because it suggests that 

one can estimate the expected error 

in a computed MHV for any station, 

with up to 30 minutes of missing data, based only upon the K-Index value for that station. 

For example, we would expect that the RMS error for a computed MHV at Boulder with 30 

minutes or less of missing data and having a K-Index level of 6 (K-Index =120), to be less 

than 2.4 nT for the 2% case (120 * 0.02), and probably closer to 1.8 nT if one uses a 1.5% 

value for the estimate. If the K-Index were unavailable during that hour, the K-Index from a 

nearby station could be used. 

Figure 4. Comparison of RMS errors produced for the 

College Active case between Arithmetic and Regression 

Line midpoint methods of computing MHVs. 

Figure 5. The ratios of RMS error divided by the lower limit K-Index for all stations and activity levels 

expressed as a percentage. The letter following the 3-letter station code represents the activity level. 

123
4. Discussion 

The original intention of this study was to establish quantitative estimates of the errors 

one would expect when computing hourly means with various amounts of data missing 

during the hour for different latitudes and magnetic activity levels. The intuitive expectation 

was that the errors would be larger for higher latitude stations, and larger as the number of 

missing minutes during the hour increased and the magnetic activity increased. The question 

was by how much? The findings, however, suggest a more complex picture. Why, for 

example, do we find that the RMS errors for 5 minutes of missing data are as large as or 

larger than those with 10 or even 25 minutes missing? And this relationship holds for all 

stations with all but the lowest levels of magnetic activity. And why is the 35-minute 

deletion set such an anomaly, with this deletion set producing errors more than double those 

with 30 missing minutes? Again this holds for all stations and activity levels. It has been 

argued above that it is doubtful that these are the consequence of something common to the 

data used because, with one small exception, the data sets used for the different 

observatories and magnetic activity levels were all different. 

One interesting avenue for speculation about this conundrum might be to consider the 

possible role of Fibonacci numbers (Vorobiev, 2002). Fibonacci numbers were introduced 

to Europe in the 13th Century by the Italian mathematician Filius Bonacci as a sequence of 

numbers formed by starting with zero and one and constructing each successive number by 

adding the two previous numbers together. The sequence looks like the following: 

0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, ... 

These numbers appear in many ways as patterns in nature. The arrangement of leaves and 

flower petals in plants often follow the Fibonacci numbers. It even appears in the relative 

length of the finger bones in a human hand. It may be purely coincidental, but it is intriguing 

that the two most anomalous deletion sets in this study are the 5-minute set, where 5 is a 

Fibonacci number, and the 35-minute set which is a number adjacent to a Fibonacci number. 

If these two deletion sets continue to stand out in further studies on MHV errors, it might be 

worth considering the possibility that there is some pattern in the geomagnetic field that can 

be represented using Fibonacci numbers. 

One issue that needs further consideration in this study is the fact that only one of many 

possible random deletion sets was used for each deletion case. There are a great many other 

possible combinations of missing minutes that could be used as the deletion minutes for the 

8 different deletion sets. Perhaps a next step would be to repeat this analysis multiple times 

using an ensemble of random minutes for the deletion sets to increase the sampling 

population of MHV errors and obtain further insight into the relationship(s) between these 

variables. If the averages of an ensemble of random deletions were to show a similar pattern 

to what has been observed here, it would give greater confidence in these results. 

Another factor to consider about the current study is the selection criteria used for 

magnetic activity. The K-Index is a 3-hour range index, and as is well known, those three 

hours can contain large variations in amplitude during one hour and perhaps not during 

another. This can be best illustrated with Figure 6, showing a plot for Boulder on May 15, 

2005. The shaded box indicates the range of variation from hours 9 to 12 that produced a KIndex of 8 and was one of the segments used to form the composite active day for Boulder. 

(The previous 3-hour interval produced a K-Index of 9, incidentally). One can see that the 

first hour in this interval produced the vast majority of magnetic variation that led to the 

determination of the K-index value for this 3-hour interval. Deleting data from hours 11 to 

124
12 would not have nearly the effect on MHV error as the same deletions for the hour from 9 

to 10. Clearly the selection criteria for magnetic activity might follow expectations better if 

it were based upon an hourly range rather than a 3-hour range index. The problem with this 

approach, however, is that it does not allow one to control for magnetic activity across 

observatories at different latitudes as the K-Index does. Some form of latitude-compensated 

hourly range index may be needed. 

Figure 6. Plot of Boulder X-component for May 15, 2005 highlighting the interval between hours 9 and 

12 used as a time segment for the Boulder Active composite day. 

Despite these concerns, this study has answered the original question to a certain degree. 

We have found, for example, that for the Quiet condition, even at a high-latitude station, the 

error in computing an MHV should be no more than 0.5 nT, which is within the accuracy of 

the data values themselves. And these results hold for the cases where two-thirds of the data 

are missing during each hour. For the cases of Moderate magnetic activity, except for the 

anomalous 35-minute deletion set, the RMS error for College was less than 6 nT, even with 

half the data missing. For the mid-and low-latitude stations, the error was often less than 1 

nT. Even for the Active cases, with half the data missing, the errors are not unduly large, 

particularly for the mid- and low latitude observatories. The largest error found for these 

two latitudes in all cases was only 11.1 nT. The question is whether or not such errors are 

acceptable as estimations of the true hourly means? 

It must be remembered, however, that these findings are only for the X-component. 

Further study would have to be done to identify the characteristics of the Vertical (Z) and 

East-West (Y) components as well. It is not clear, for example, whether the RMS/K-Index 

parameter proposed here would work for the Z-component since that is not used in the 

computation of a K-Index. 

Nonetheless, this investigation points to at least two conclusions. First, those who argue 

that hourly means should not be computed when any data are missing during the hour are 

125
sacrificing a good deal of information for what might be the sake of computational purity 

and simplicity of operational procedures. This approach reminds one of the old proverb: 

“For the want of a nail the shoe was lost; for want of a shoe the horse was lost; ...) and so 

on until the kingdom was lost. If we allow ourselves to fall prey to an analogous chain of 

conditionals, would we not then likewise argue that we should not compute an annual mean 

if only one minute of data were missing during an hour? With the results presented here, 

perhaps we can proceed with some assurance that there are less draconian ways of setting 

standards for computing hourly means, even when relatively large amounts of data are 

missing. 

Secondly, it seems clear that a one-rule-fits-all approach to the question of when an 

MHV should or should not be computed will not be adequate either. There are cases where a 

relatively small amount of data during an hour will produce a reasonable MHV. In the 

majority of cases considered here, for example, the errors proved to be less than a few nT. 

This is not to say that an MHV should always be computed. Further study is needed to more 

clearly identify the profile of MHV errors that result from missing data. And the 

geomagnetism community needs to decide what size errors for MHVs in these cases will be 

acceptable, and under what conditions. 

 

5. Acknowledgements 

The author would like to thank Susan McLean and William Denig of the National 

Geophysical Data Center (NGDC) for their support and encouragement while studying this 

problem. Thanks also to Carolyn Hess for the suggestion about the possible role of 

Fibonacci numbers. I am grateful as well to the USGS Geomagnetism Program, which 

provided a superior data set for the observatories used in this study, and to 

INTERMAGNET for the CD-ROMs that contain the data. 

 

References 

Mandea, M., 2003, 60, 59, 58, ... How many minutes for a reliable hourly mean?, 

Proceedings of the Xth IAGA Workshop, Hermanus 112-120 

Mayaud, P.N., Derivation, Meaning, and Use of Geomagnetic Indices, Geophysical 

Monograph 22, American Geophysical Union, Washington, D.C., 1980 

Schott, J., and Linthe, H.J., 2007, The hourly mean computation problem revisited, 

Publication of the Institute of Geophysics Polish Academy of Sciences, C-99 (398) 

Vorobiev, N.N., and Martin, M. (translator), 2002, Fibonacci Numbers, Birkhäuser Verlag, 

Basel, 176 pp. ISBN 3-7643-6135-2 

126
Modelling Southern African Secular Variation Using 2005 - 2007 Field Survey Data 

P. B. Kotzé1, M. Mandea2 and M. Korte2 

1

 Hermanus Magnetic Observatory, Hermanus South Africa 

2

 GeoForschungsZentrum, Potsdam, Germany 

Abstract 

Secular change of the Earth’s magnetic field is a comparatively regional phenomenon and 

does not proceed in a regular manner across the Earth. This gives rise to regions where the 

magnetic field changes more rapidly than elsewhere, like for instance southern Africa. As 

part of a cooperative project between Germany and South Africa, called Inkaba ye Africa, the 

COMPASS (COmprehensive Magnetic Processes under the African Southern Sub-continent) 

program aims to study the regional geomagnetic field and in particular its evolutionary 

behaviour. In addition to a rapid decrease of the geomagnetic field in this region, as 

evidenced by the 20% decrease observed at Hermanus, the orientation of the geomagnetic 

field in southern Africa is also changing rapidly. In the north-western part of southern Africa 

the declination of the magnetic field is propagating eastward (Tsumeb) and in the southeastern part it propagates westward (Hermanus and Hartebeesthoek). This results in an 

overall increase of the spatial gradient over the subcontinent with time. During 2005, 2006 

and 2007 joint field survey campaigns were conducted by the Hermanus Magnetic 

Observatory (HMO) and the GeoForschungsZentrum Potsdam (GFZ) in South Africa, 

Namibia and Botswana in order to characterize the time variation of different components of 

the geomagnetic field. Results obtained from these field surveys, together with data from the 

3 continuous recording magnetic observatories in southern Africa at Hermanus, 

Hartebeesthoek and Tsumeb, are used to model the geomagnetic field time variation for 

2005-2007. 

1. Introduction 

The geomagnetic field of the Earth varies on timescales ranging from seconds to millions 

of years. Variations on short timescales are mostly dominated by external sources, while 

variations on longer timescales (~ 1 year and longer) are collectively known as secular 

variation (SV) and are predominantly of internal origin. It is also known that secular change 

is a comparatively local phenomenon and does not proceed in a regular way all over the Earth 

(Bullard, 1948). This results in regions where the field changes more rapidly than elsewhere, 

as is the case for southern Africa (Kotzé, 2003). On the other hand, abrupt changes in the 

linear secular variation trend occur from time to time and have been named ‘geomagnetic 

jerks’ or ‘secular variation impulses’ (Mandea et.al., 2000).The observed high SV in southern 

Africa has been ascribed by Bloxham and Gubbins (1985) to rapidly drifting core spots. 

These intense patches normally occur in the Southern Hemisphere and drift westward 

towards South America. 

In order to better understand the mechanisms underlying the generation of the Earth’s 

magnetic field, it is desirable to have as long a record as possible of the evolution of the field. 

The evolution of declination and inclination of the magnetic field have been measured over 

many centuries (Jackson et al., 2000, Jonkers et al., 2003). In southern Africa, the Hermanus 

Magnetic Observatory (HMO) has conducted geomagnetic repeat surveys on a routine basis 

for close to 50 years. These surveys normally include South Africa, Namibia, Zimbabwe and 

Botswana, and have been conducted at regular intervals of 5 years. This procedure was 

continued until 2000 with measurements taken at almost 70 secular variation positions. 

Thereafter, because of the rapid change of the geomagnetic field in this region, it was decided 

127
to conduct field surveys on an annual basis, but at fewer selected repeat stations. Hence, eight 

selected repeat stations were visited at annual intervals for 2003 and 2004. The experience 

gained from these surveys has shown that the limited number of stations over the Southern 

African region is insufficient to accurately model the secular variation due to the increasing 

temporal and spatial gradients. A better spatial resolution, however, demanded an increase in 

the density of the repeat stations. In 2005, a total of 40 stations were selected for such a 

survey campaign (Kotzé, 2007). 

The results obtained during subsequent field surveys are used to derive mathematical models 

of the time variation of all geomagnetic field components measured for the period 2005-2007, 

using polynomials that can be expressed as a function of latitude and longitude. Here, we 

present the modelling results for declination (D), horizontal component (H), and vertical 

component (Z). 

2. Field Surveys and Data Collection 

For field survey purposes, field stations are marked by concrete beacons, ensuring that 

all observation points are exactly reoccupied during surveys. Most measurements are taken 

on a standard 1.2m pillar, while in a few cases observers had to use a tripod mounted above a 

clearly marked shorter beacon. The field surveys of 2005, 2006 and 2007 were separated into 

three different sectors. At first a survey was done by only HMO field surveyors. Thereafter, 

two independent teams, each consisting of a staff member from HMO and GFZ, conducted a 

simultaneous field survey in southern Africa. A map showing the repeat station beacons 

visited in the surveys is shown in Figure 1. 

Figure 1. A map showing the 

positions of the repeat station 

beacons (●) as well as the 

INTERMAGNET observatories 

at HMO, HBK and TSU 

together with the newly 

established observatory at 

Keetmanshoop (KMH) in 

netic observatories, sometimes a distance of more than 300 km away (see Korte et 

al, 

Namibia. 

More information on the repeat station methodology is given by Korte et al. (2007). A 

DI fluxgate magnetometer was used as the primary instrument during field surveys to obtain 

values of declination and inclination, while an Overhauser magnetometer delivered values of 

total field intensity (F). Corrections for diurnal variation and other disturbing effects were 

made by comparing field station observations with magnetic data recorded on site with a 

LEMI suspended tri-axis fluxgate instrument. This proved to be a vast improvement to the 

use of mag

2007). 

128
Continuous recording of geomagnetic field variations are conducted at Hermanus (34o 

25.5’ S, 19o 13.5’ E), Hartebeesthoek (25o 52.9’ S, 27o 42.4’ E) and Tsumeb (19o 12’ S, 17o 

35’ E). All these observatories comply with INTERMAGNET standards 

(www.intermagnet.org). The primary instrument for recording of magnetic field variations is 

the FGE fluxgate magnetometer, manufactured by the Danish Meteorological Institute in 

Copenhagen, Denmark. This instrument is based on three-axis linear-core fluxgate 

technology, optimised for long-term stability and records the components H, D and Z. An 

Overhauser-type magnetometer further provides absolute total field information, while 

baselines for the other components are obtained using a DI (Declinometer-Inclinometer) Flux 

theodolite (www.bartington.com/flux1). 

d as input to derive 2

egree polynomial models as a function of latitude and longitude, with 6 statistically 

 field component modelled: 

+ Fyy 

 SV=Secular variation component (H, D, Z) 

min/y. Results obtained by modelling the secular variation field survey 

bservations by means of a 2nd degree polynomial are given in Figures 2, 3 and 4 

respectively.

3. Modelling of Secular Variation Data 

Secular variation data obtained from the field surveys were use nd

d

significant coefficients for each magnetic

SV = A + Bx + Cy + Dxx + Exy 

 Where: 

 X=26 ̊ - Latitude 

 Y=24 ̊ - Longitude 

 A-J=coefficients derived from field survey data using least squares fit 

Since secular variation is not measured directly, but is derived as a time derivative of the 

geomagnetic field, one can model the main field and then differentiate the corresponding field 

model to get a secular variation model, or one can numerically differentiate the main field data 

and then fit a secular variation model directly. The latter derivative–fit approach is able to remove 

crustal contamination which is the overwhelming source of error in determining a main field 

model (Cain et al., 1965, Dawson and Newitt, 1978) and has been applied in this study of 

observatory and repeat station data. First central differences from annual mean observatory data 

as well as repeat stations, divided by their respective time intervals in years, are used as input data 

to our secular variation model. As observatory data, in general, are more accurate than repeat 

survey data (because of better baseline control and because seasonal and other short-term 

variations are more effectively removed by using annual means) we weighted observatory and 

repeat station secular variation data in a ratio 1:0.7 in the least-squares solution. This ratio was 

determined by minimizing the RMS difference between model fits and survey data. Secular 

variation models for the periods 2005.5 – 2006.5, and 2006.5 – 2007.5 were subsequently 

derived. The least-squares routine used to fit the data is the stepwise regression procedure 

described by Efroymson (1960) that has the ability of both entering and removing variables at 

given levels of statistical significance. The scatter about the fit for declination secular variation is 

less than 1 

o

129
Figure 2. A contour plot showing the secular variation in declination D (min/y) for both 2005 – 2006 and 

2006 – 2007, as modelled by a 2nd degree polynomial using field survey data obtained over southern 

Africa. 

Figure 3. A contour plot showing the secular variation in the horizontal component H (nT/y) for both the 

period 2005 – 2006 and 2006 – 2007, as modelled by a 2nd degree polynomial using field survey data 

collected over southern Africa. 

Figure 4. A contour plot showing the secular variation in the vertical component Z (nT/y) over southern 

Africa as modelled by a 2nd degree polynomial, using ground survey data for the periods 2005 – 2006 and 

2006 – 2007. 

130
4. Conclusions 

Due to the dynamic variation of the magnetic field in southern Africa (see Mandea et al.,

2007), it is important that a dense array of suitable and extensive ground-based observations 

consisting of both repeat station beacons and continuous recording magnetic observatories, be 

employed to determine the morphology of the geomagnetic field in this region. 

Results obtained by modelling the declination secular variation for both 2005 – 2006 and 

2006 - 2007 over southern Africa reveal a pattern dominated by an eastward SV in the northwestern part of the region, while the south-eastern region of southern Africa is under the 

influence of a westward SV (Figure 2). This indicates a growing gradient in the orientation 

of the geomagnetic field of southern Africa in a north-west, south-eastern direction. However 

during the period 2006 – 2007 we observe that this pattern is busy changing, especially if we 

analyze the behaviour of the zero-line of secular variation, showing that there is a clear 

tendency for the field orientation to change eastward. Over certain parts of Namibia and 

Botswana the secular variation changed from westward to eastward completely, clearly 

indicating the development of a geomagnetic jerk in southern Africa. 

The pattern displayed by the horizontal component secular variation (Figure 3), clearly 

indicates that the sub-continent is dominated by a central region where the horizontal 

component of the magnetic field (H) is declining at a rapid rate, exceeding 30 nT/y in certain 

areas. In contrast to the western region of southern Africa where H is declining, the eastern 

parts show a clear tendency to increase. However during 2006 – 2007, the area where H used 

to increase, showed a clear tendency to diminish, especially if we consider the eastward 

movement of the zero line of secular variation. This also indicates that a jerk is in progress 

over southern Africa. For the vertical field component, Z, the tendency is that of a 

diminishing field over the whole of southern Africa (Figure 4). Over the south-western part 

of the subcontinent the decay of Z is the most prominent, where the decreasing rate is around 

35 nT/y during 2005 – 2006. However during 2006 – 2007 the decreasing tendency slowed 

down to almost 25 nT /y over the same region. This shows that the geomagnetic field over 

southern Africa changes at an extremely fast rate in comparison to other parts of the world. In 

addition, the secular variation in this region varies non-uniformly on an annual basis, serving 

as evidence of the development of a geomagnetic jerk at present in southern Africa. 

The growth and changes in the position of patches of the reverse flux under the South 

African region, from 1840 onwards, may be responsible for the observed changes in the 

geomagnetic field. Most interestingly in this regard, it has been shown recently (Dormy and 

Mandea, 2005) that patches of intense secular variation appear in the South Atlantic 

hemisphere that are very rapidly displaced in a south-east – north-west direction, consistent 

with observations of the present study. 

Acknowledgements 

We wish to thank the Geological Survey of Namibia, Ministry of Mines and Energy, as 

well as the Geological Survey of Botswana for continuous support in carrying out repeat 

station measurements in these countries. We particularly acknowledge the active participation 

of Tiyapo Ngwisanyi in the surveys in Botswana. This work was made possible through 

financial support from the Hermanus Magnetic Observatory in South Africa as well as the 

GeoForschungsZentrum Potsdam, Germany. Support provided by the Geological Survey of 

Namibia in the operation of the magnetic observatory in Tsumeb is acknowledged. 

131
References 

Bloxham, J. and D. Gubbins, The secular variation of Earth’s magnetic field, Nature, 

317, 777-781, 1985. 

Bullard, E. C., The secular change in the Earth’s magnetic field, Monthly Notices Royal 

Astronomical Society Geophysics Supplement, 5, 248-257, 1948. 

Cain, J.C., W.E Daniels, S.J. Hendricks, and D.C. Jensen, An evaluation of the main 

geomagnetic field 1940-1962, Journal of Geophysical Research, 70, 3647-3674, 1965. 

Dormy, E. and M. Mandea. Tracking geomagnetic impulses down to the core-mantle 

boundary, Earth and Planetary Science Letters, 237, 300-309, 2005. 

Dawson, E., and L.R.Newitt, An analytical representation of the geomagnetic field in Canada 

for 1975, II, The secular change, Canadian Journal of Earth Science, 15, 237-244,1978. 

Efroymson, M.A., Multiple regression analysis, in Mathematical Methods for Digital 

Computers, edited by A. Ralston, and H.S. Wilf, pp 191-203, John Wiley, NewYork, 1960. 

Jackson, A., A.R.T. Jonkers, and M.R. Walker, Four centuries of geomagnetic secular 

variation from historical records, Philosophical Transactions Royal Society London A, 358, 

957-990, 2000. 

Jonkers, A.R.T., A. Jackson., and M.R. Walker, Four centuries of geomagnetic data from 

historical records, Reviews of Geophysics, 41, 1006,doi:10.1029/2002R G000115, 2003. 

Korte, M., M. Mandea, P.B. Kotzé, E. Nahayo, and B. Pretorius, Implications of 

improved geomagnetic repeat station surveys of southern Africa for the Earth's 

conductivity, South African Journal of Geology, 110, 175-186, 2007. 

Kotzé, P.B., The time-varying geomagnetic field of Southern Africa, Earth Planets and 

Space, 55, 111-116, 2003. 

Kotzé, P.B., Modelling the southern African geomagnetic field secular variation using ground 

survey data for 2005, South African Journal of Geology, 110, 187-192, 2007. 

Mandea, M., E. Bellanger, J.-L. Le Mouël, A geomagnetic jerk for the end of the 20th

century? Earth Planet. Sci Lett., 183, 369-373, 2000. 

Mandea, M., M. Korte, D Mozzoni, and P. Kotzé, The magnetic field changing over 

southern African continent: its unique behaviour, South African Journal of Geology, 110, 

195-202, 2007. 

132
Installing a Geomagnetic Observatory on St. Helena Island 

– a Special Challenge 

Hans-Joachim Linthe, Mioara Mandea, Monika Korte 

Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences Potsdam, Germany 

Abstract 

In and around the south Atlantic the network of continuously recording geomagnetic observatories 

is sparse, while this region of anomalously weak core field is of particular interest for scientific 

studies. Over the last few years we have improved monitoring the geomagnetic field around the 

southern Atlantic by setting up three new, remotely operated observatories. They are located in 

Bolivia, Namibia and on the island of St. Helena. Installing and operating remote observatories 

requires substantial efforts. 

The biggest challenge was the installation of St. Helena observatory, which had to be carried out in 

rather limited time due to the time-consuming and somewhat infrequent accessibility of the island. 

Cost-efficient structures and well proved and tested instrumentation were installed in November 2007. 

First recordings of several months look promising. Problems with the baseline and an interruption of 

the recording due to flooding after heavy rains are expected to be resolved in near future after a 

training visit of the contracted observer to Niemegk observatory and shipment of replacement 

electronic units, respectively. 

Introduction 

The Earth's magnetic field is anomalously weak in a region centred in the South Atlantic and 

covering parts of southern Africa and South America. This area, where the field reaches less than 60% 

of the field strength at comparable latitudes, is known as the South Atlantic Anomaly (SAA). Detailed 

studies of the SAA, and in general reliable modelling of the Earth’s magnetic field require a good 

coverage with reliable ground data provided by geomagnetic observatories [Korte et al]. 

Fig. 1: Observatory locations in the region of the South Atlantic Anomaly; the isolines of the total 

intensity F are depicted for reference 

The observatories which are operated at present in the region of the SAA including the new one of 

St. Helena are listed in table 1 and depicted in Fig. 1. Ten of the 21 observatories are IMOs 

(INTERMAGNET observatories). The IAGA codes of the 3 newly installed observatories are still 

provisional. 

133
IAGA code Observatory Country Latitude Longitude 

KOU * Kourou French Guiana 5.100° 307.400 

BNG * Bangui Central African Republic 4.437° 18.565° 

TTB Tatuoca Brazil -1.200° 311.480° 

ASC * Ascension Island British overseas territory -7.949° 345.624° 

ANC Ancon Peru -11.683° 282.852° 

HUA * Huancayo Peru -12.045° 284.670° 

ARE Arequipa Peru -16.470° 288.520° 

VRE 1 Villa Remedios Bolivia -14.770° 291.830° 

NMP Nampula Mozambique -15.083° 39.250° 

SHE 1 St. Helena British overseas territory -15.965° 5.748° 

PTY Patacamaya Bolivia -17.250° 292.050° 

TSU * Tsumeb Namibia -19.220° 17.700° 

VSS * Vassouras Brazil -22.400° 316.350° 

HBK * Hartebeesthoek South Africa -25.882° 27.707° 

LMM Maputo Mozambique -25.920° 32.580° 

KMH 1 Keetmanshoop Namibia -26.541° 18.110° 

PIL Pilar Argentina -31.670° 296.120° 

HER * Hermanus South Africa -34.425° 19.225° 

LAS Las Acacias Argentina -35.007° 302.320° 

TRW * Trelew Argentina -43.250° 294.680° 

PST * Port Stanley British overseas territory -51.700° 302.100° 

Table 1: Observatories in the region of the SAA. *INTERMAGNET observatories; 1provisional IAGA 

code. 

Apart from Ascension Island, the Atlantic Ocean is void of magnetic observatories. Ocean bottom 

observatories cannot be installed in the near future in the Southern Atlantic Ocean, and therefore all 

possible islands should be used to place magnetic observatories. The number of islands there, 

however, is quite small. Any of them would be a useful location for the installation of a new 

observatory. 

Several institutions [Berarducci, 2002], [Crosthwaite et al, 2007], [Grekhov et al, 2004], [Linthe et 

al, 2007], [Rasson et al, 2002], [Rasson et al, 2004], Schott et al, 2004], [Torta et al, 2001] installed 

new observatories and upgraded existing ones with modern instruments to consolidate the network 

and to improve the general data situation for scientific research. 

We choose to install a new, remotely operated observatory on the island of St. Helena in order to 

complement the existing network within the SAA region. Installing an observatory at such a remote 

location is a particular challenge. Here, we report on the logistical preparations, the actual installation 

by a team of two GFZ employees in November 2007 and first data results from this new observatory. 

Logistical Situation 

The island of St. Helena is situated quite remotely in the Southern Atlantic. Fig. 1 shows the 

location of St. Helena within the SAA and the existing geomagnetic observatories. Establishing an 

observatory in such a remote location was a particular challenge. Firstly, because access to the island 

134
is only possible by a ship connecting Ascension Island, St. Helena, Cape Town and Walvis Bay on a 

fixed but somewhat irregular schedule. Secondly, such an installation is costly both in time and 

money. The complete observatory installation by GFZ staff therefore had to be done during one 17 

day visit, requiring careful planning to achieve a long enough but not extensive stay on the island. All 

together the trip took 36 days, travelling as depicted in Fig. 2. From Berlin to London and back we 

used a regular flight. The Royal British Air Force offers some seats for civilians on their regular 

flights from a military air base near by Oxford to Ascension Island. From there, we took the Royal 

Mail Ship (RMS) “St. Helena”. We arrived at the island on 27 October 2007 and had to leave on 13 

November. Inevitable layovers at Ascension Island of 4 and 7 days on the way to and from St. Helena, 

respectively, were used to carry out some absolute measurements at the British Geological Survey 

magnetic observatory on the island, for which the British colleagues had asked us. 

Berlin

St. 

Helena

Acsension

Oxford

London

R

Fig. 2: Sketch of the travel route 

The instruments and further necessary materials were sent beforehand using the service of a British 

shipment company from Portland, England to St. Helena by the RMS “St. Helena”. 

To install this observatory logistical support was obtained from the International Deployment of 

Accelerometers (IDA) seismological network of the University of California at San Diego (UCSD), 

which has one of its stations on St. Helena. For the geomagnetic observatory a piece of government 

land, next to the IDA station, has been leased free of charge. The station is located at the end of a culde-sac and has electricity and telephone connection, yet is far away from human and technical 

135
disturbances. The observatory land of 40 by 5 m lies on a slope of the mountainous island, some 25 

km away from the capital, Jamestown. Fig. 3 shows a map of the island with the observatory location. 

The roads from Jamestown to this site, which is called Horse Pasture, are very narrow and twisty. It 

was not possible to carry out a survey for magnetic homogeneity of the planned observatory area 

beforehand due to the difficulty to visit the island. The strongly magnetic volcanic rocks that form the 

whole island cause very steep gradients in the order of 5 nT/m within the observatory premises, and a 

difference of 250 nT between absolute and variation pillar, which could not be avoided. 

Fig. 3: Map of the island of St. Helena showing the location of the observatory. 

The St. Helena citizen Peter Thorpe is contracted by USCD to do the necessary service of their 

seismological station. He agreed to operate the magnetic observatory as well, particularly to carry out 

the necessary manual absolute observations. 

Installation of the Observatory 

The instruments for the new observatory are listed in table 2. The permanent recording equipment 

consists of a suspended FGE variometer, a GSM90 Overhauser proton magnetometer, a GPS receiver 

and a MAGDALOG data logger developed at the Niemegk Adolf Schmidt Observatory [Linthe, 

2004]. Fig. 4 shows the block diagram of this logger. The logger board, consisting of a 20 bit 4 

channel analogue to digital converter ADC, an embedded 386 personal computer (DIMM PC) and 

optical transmitters, is mounted in the housing of the electronic unit of the vector magnetometer FGE. 

The DIMM PC is controlled by a DOS operating system. Its clock is synchronized by a GPS receiver 

connected to the COM1 serial interface. The ADC is controlled by means of the parallel port LPT1. 

The DIMM PC controls the scalar magnetometer by means of COM2. COM3 is connected to an 

RS232 line of a normal DOS PC for data transmission. All RS232 lines are made of fibre optics to 

avoid lightning damages. 

The normal PC uses the DOS operating system and is connected to an Ethernet local area network 

(LAN). A further normal PC, working under a Linux operating system, is also connected to the LAN, 

which has internet access. The sampling rate is 2 Hz for the 3 FGE components H, D and Z and its 

136
sensor temperature and 0.2 Hz for the scalar magnetometer GSM90. The buffer memory of the DIMM 

PC allows a 6 hours intermediate storing of the data. A special software handshake between the 

DIMM PC and the DOS PC avoids data loss in the data transmission. The software running on the 

DOS PC receives the data from the DIMM PC and stores them on its own hard disk and on the hard 

disk of the Linux PC and calculates mean values. Further software runs on the Linux PC to calculate 

K indices and to produce output files (e.g. INTERMAGNET one minute files) and to transmit data by 

means of the internet. 

DI-flux Zeiss THEO 010B equipped with Bartington MAG 01H 

Scalar magnetometer GEM Systems GSM19 

Vector variometer Danish Meteorological Institute suspended FGE 

Scalar variometer GEM Systems GSM90 

Data logger GFZ Potsdam MAGDALOG 

Table 2: Observatory instruments 

Plastic fibre

Plastic fibre

Glass fibre

DOS-PC LINUX-PC

RS232

Ethernet-LAN

RS232

Fig. 4: Block diagram of the data logger 

137
When we arrived at St. Helena the instruments were already on-site. Peter Thorpe had successfully 

taken care to obtain all the necessary permissions for the observatory installation beforehand. He had 

also hired a contractor with suitable machinery for the necessary construction. 

Some 50 m of the cable duct, connecting the seismological station and the magnetic observatory 

along a rather rocky slope were already finished when we arrived. Some magnetic soundings at the 

intended place for the observatory showed very high gradients of about 5 nT/m horizontally and 

almost 50 nT/m vertically. Due to the volcanic nature of the island a more suitable place could not be 

expected. So the construction was continued by flattening an area of 45 m by 8 m on the slope. 

Instead of buildings cost-effective constructions are used. Three commercially available containers 

with watertight lids consisting of reinforced plastics were used. Thus the instruments can be 

positioned far enough apart to avoid electrical interferences. Two containers with ground dimensions 

of 1.2 by 1.6 m and a height of 0.8 m were buried in the ground some 5 m apart. One contains the 

vector magnetometer sensor on a non-magnetic concrete pillar, the other one the power supply units 

and the vector magnetometer electronics including the data logger. The GPS antenna is mounted close 

to this container on a plastic pipe about 1.5 m above the ground. Another, smaller container with 

ground dimensions of 0.6 by 0.9 m and a height of 0.3 m was buried further 5 m apart, housing the 

Overhauser magnetometer electronic unit. The GSM90 sensor is mounted on a plastic pipe about 1.5 

m above the ground close to the small plastic container. Cable ducts using pipes of 110 mm diameter 

connect the instruments in the containers to a computer in the seismological station building some 70 

m away. 

Styrofoam slab liners of 6 cm thickness are used to ensure the best possible temperature stability 

within the plastic containers. All remaining space within the containers is additionally packed with 

plastic cans filled with water. 

The absolute measurement pillar of 360 mm diameter was constructed from non-magnetic concrete 

on a concrete basement of 0.8 m by 0.8 m by 0.8 m. A simple wooden roofing was constructed above 

the measurement pillar for rain and solar radiation protection. The area was fenced with non-magnetic 

aluminium netting wire mounted on aluminium poles for security. Fig. 5 shows a sketch of the 

observatory premises. 

N

20 m 5 m 5 m 5 m

40 m

1 2 3 38 4 5 6 7

5 m

1 Absolute measurement pillar

2 FGE sensor container with pillar

3 Sleeve pipe of 110 mm diameter

4 Electronic units container 

5 GPS a

6 GSM90 electronic unit container

7 GSM90 

8 Sleeve pipe of 110 mm diameter 

 to the technical building

ntenna

Sensor

Fig 5: Outline of the observatory 

nough empty beverage 

bottles and a glass cracking equipment on an island of 5,000 inhabitants only. 

. 

The construction of the non-magnetic pillars proved to be a special challenge. The gravel from the 

island, which the contractor could provide, turned out to be strongly magnetic. Glass granulate was 

considered to be used instead. It took the contractor a while to organize e

138
A suitable place for an azimuth mark target could be found at an opposite weakly timbered slope 

in some 470 m distance. The area is called the Saddle. The land planning and forest agencies rather 

quickly granted the necessary permissions to construct a concrete pillar for this purpose. 

Two computers and 2 uninterruptible power supply units (UPS) of 1,500 VA, 20 minutes 

autonomy time each, were placed at a newly constructed bench in the seismological station building. 

A cable for the 240 V AC power supply of the magnetometers and data logger and a 4 cores fibre 

optics cable (2 cores used) for the RS 232 communication were installed in the cable duct between the 

seismologic station building and the magnetometer containers. The power supply cable is protected 

against lightening damage at both its ends by lightening barriers. Furthermore a general lightening 

barrier is connected to the power line input of all the equipment. The power supply of the GPS 

receiver is also protected by 2 lightening barriers. An existing ground connector in the seismological 

station could be used. For the ground connection of the lightening barriers in the containers 4 

connected copper plates of 0.8 m2

 were buried and washed in beneath the electronic unit container. 

The local telecommunication company Cable & Wireless provided the necessary IP addresses for 

the internet connection of the observatory. The continuous operation of the variometers was started on 

12 November 2007. 

The geographic coordinates and the elevations above sea level of the absolute measurement pillar, 

the variometer pillar and the azimuth mark target were determined by means of a hand-held GPS 

navigator. The results can be found in table 3. 

Pillar Longitude Latitude Elevation 

Absolute pillar 05° 44’ 51.6” W 15° 57’ 55.6” S 586 m 

Variometer pillar 05° 44’ 51.4” W 15° 57’ 55.0” S 585 m 

Azimuth mark target 05° 44’ 57.4” W 15° 58’ 09.9” S 613 m 

Table 3: Geographic coordinates of the observatory 

The azimuth value of the azimuth mark target in relation to the absolute measurement pillar was 

determined by means of solar observations on 12 November 2007 as 201° 40’ 59”. At the same day 

an absolute measurement was carried out. The local lands and planning agency was asked to carry 

out a geodetic azimuth value determination for confirmation. However, their result of 217° 58’ 53” 

seems to be unreliable. Consequently, the result of the solar observation is used. Peter Thorpe was 

instructed to take care of the proper operation of the data logger and to carry out weekly absolute 

measurements. 

Fig. 6 shows 2 photos of the observatory location. The upper one shows the site before the start of 

the construction, the lower one shows the situation after the flattening of the slope. In the upper photo 

of Fig. 7 the buried containers can be seen together with the vertical tubes for mounting the GSM90 

sensor and the GPS receiver. The absolute measurement pillar is located to the right of the excavator 

in the background. The lower photo of Fig. 7 shows a view into the container which houses the power 

supply units and the FGE electronic unit with the integrated data logger. The insulating styrofoam 

slabs are visible. The container is already partly filled with water cans. The upper photo of Fig. 8 

shows a view from the location of the azimuth mark to the observatory. At the lower photo of Fig. 8 

the observatory is seen in its final situation. 

139
Fig. 6: Observatory site before construction beginning (up), and the flattened area (down) 

140
Fig. 7: View of the observatory site during the construction (up) 

and view into the electronic unit container 

141
Fig. 8: View from the azimuth mark site to the observatory (up) 

and final view of the observatory 

142
First Results 

The observatory worked continuously since 12 November except during time periods when the 

power supply was interrupted for more than 20 minutes. As soon as the power is switched on again, 

the system automatically starts its normal operation. The 3 computers are rebooted in such a case. The 

data acquisition process starts again when the DIMM PC is completely rebooted. Fig. 9 shows the 

recording of the time interval 17-31 December 2007. 

Fig. 9: Recording example of the time interval 17-31 December 2007 

Peter Thorpe carried out one absolute measurement per week. Fig. 10 shows the results of all 

absolute measurements and a first attempt to construct an adopted base line. The dispersion is very 

large. Moreover, there is an unusual drift in the adopted base lines. We suppose that this is a problem 

of lack of training of the observer, who never used a theodolite before and had to be instructed in the 

very short time that remained during the installation of the observatory. His measurement practice 

meanwhile has been improved during a 2 weeks stay at Niemegk Adolf Schmidt observatory in 

August 2008. 

Unfortunately the operation of the observatory was temporarily stopped by an accident on 21 

March 2008. Heavy rainfall caused water to enter into one of the man holes of the cable duct. The 

water flooded the first (housing the GSM90 electronic unit) and second container (housing the power 

supply units and the FGE electronic unit). To prevent further damage Peter Thorpe drained the 

containers, dismounted the damaged components and sealed provisionally the section of the cable 

duct next to the first container. 

Repeat Station Measurements 

The colleagues of the British Geological Survey (BGS) had asked us to carry out repeat station 

measurements at 2 stations on St. Helena during our stay. The last measurements at these stations 

were carried out in 1995 and 1998, respectively. We found time to do these measurements. Following 

the descriptions which we got from BGS we could find the stations, but unfortunately the azimuth 

mark (the same one for both the stations) was hidden by the surrounding vegetation. After several 

visits to the local lands and planning agency and some soundings we could get replacement azimuth 

marks and the necessary azimuth values. Two measurements were carried out at both the stations, 

143
using the BGS tripod, which we brought with us from the Ascension Island observatory, and the St. 

Helena observatory absolute instruments. Our activities to carry out the measurements were reported 

to the BGS together with the results. 

Fig. 10: Absolute measurement results (black quadrates) and adopted base lines (red lines) 

Summary and Outlook 

A new magnetic observatory was established on the island of St. Helena in order to obtain high 

resolution, continuous recordings of the Earth’s magnetic field in this special region of the South 

Atlantic Anomaly. To minimise the cost, no buildings were planned, but reinforced plastic containers 

were buried in the ground. The complicated logistical situation made the installation a challenge. 

Logistical support was provided by the IDA network operated by UCSD. A St. Helena citizen could 

be contracted to do any necessary on-site checking of proper operation and to carry out the absolute 

measurements. 

In order to get the station operating again after the damage from the rain water flooding some 

replacement components were necessary. Due to the complicated logistical situation this took almost 

3 months. The contractor, who already did all the constructions, has been instructed to properly seal 

the man holes of the cable duct against entering of water. Peter Thorpe will install the replacement 

components to restart the variometer recordings. His training at Niemegk Adolf-Schmidt-Observatory 

should improve the results of the future absolute measurements. 

Despite the thermal insulation of the containers by means of styrofoam slabs and the “thermal 

buffer” by means of water cans we observed a daily temperature variation of 3°C. This is obviously 

caused by the solar radiation. In order to eliminate this variation we instructed the contractor to 

construct non-magnetic protection roofs above the 2 big containers. 

During the 4 months of operation of the data logger we recognized that power supply breakdowns 

longer than the autonomy time of the UPS occur from time to time. It is planned to purchase and 

install a UPS of a sufficient capacity. 

The discrepancy in the azimuth values of our own solar observation and of the result of the local 

lands and planning agency could not be clarified. Probably some difference in our and their 

understanding of the term “azimuth value” or in the coordinate system is the reason. Only our value 

provides reasonable declination results with respect to the past and present repeat station survey 

results from St. Helena island. We have seriously tried to resolve the reason for the difference but it 

seems impossible without a personal meeting, i.e. another visit to St. Helena island. 

144
At present disturbing constructions and activities nearby the observatory site are not expected. 

Nevertheless, an official letter was sent to the St. Helena Government Chief Secretary to inform him 

about the significance and the necessity of a magnetically undisturbed environment for the 

observatory. This way we hope to be notified in time about any planned developments that might 

disturb the magnetic recordings in order to try and find a compromise that ensures the undisturbed 

condition of the observatory. 

Our activity to establish a new magnetic observatory at St. Helena is intended to provide more data 

for a scientifically highly interesting area. Moreover we tie in with the tradition of the Goettingen 

Magnetic Union, which established a magnetic observatory at St. Helena in 1841. After 160 years we 

now follow in the footsteps of Carl Friedrich Gauss, Wilhelm Weber, Alexander von Humboldt, John 

Ross, James Clark Ross and Sir Edward Sabine, who obviously recognized already at that time 

the importance of magnetic observations at that location. 

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Gingin. Proceedings of the XIIth IAGA Workshop on Geomagnetic Observatory Instruments, Data 

Acquisition and Processing, 19-24 June 2006. Belsk, Poland. Warsaw, 2007. pp.46-48. 

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observations in the South Atlantic Anomaly region. Accepted by Annals of Geophysics. 

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9-17, 2004 Kakioka and Tsukuba, Japan. pp. 90-94. 

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Acquisition and Processing, 19-24 June 2006. Belsk, Poland. Warsaw, 2007. 

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9-17, 2004 Kakioka and Tsukuba, Japan. pp. 43-48 

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145
Geomagnetic Activities in Pakistan since 2006 - to date 

Madeeha A.1, G. Murtaza2, J. L. Rasson3,

 Chris Turbitt4 

12 Pakistan Space & Upper Atmosphere Research Commission (SUPARCO), P. O. Box # 8402, Karachi75270, Phone No: + 9221 4650765-79, Fax No. +9221 4644928, 4644941 3

Royal Meteorological Institute (IRM) of Belgium 4

British Geological Survey 

Author’s email: 1misshah84@gmail.com, 2gmurtaza66@hotmail.com, 3jr@om.be

Abstract: 

Pakistan (23–40o

N, 60–80oE) is spread over 800,000 square km, where South Asia 

converges with Southwest Asia and Central Asia. The country consists of a variety of 

terrain with flat Indus plain in east, mountains in north and northwest Balochistan plateau 

in the west. 

A geomagnetic observatory was established at Karachi (geog coord; 24.95o

N, 

167.14o

E) in 1983. Due to perturbations traceable to the increased urbanization, the 

observatory has been shifted to a site “Sonmiani”, 80 km north-west of Karachi, where 

long term protection from cultural noise is offered. This site in a sparsely built research 

complex was selected after a magnetic survey. A new observatory has also been 

established at Islamabad (geog cord; 33.75oN, 72.87oE) which is mountainous region. We 

also plan to upgrade the observatory at Quetta. 

We intend to offer geomagnetic services to the scientific and socio-economic sectors 

in the country. Different departments will be approached to provide them with the 

geomagnetic data and onsite measurements. Repeat station work will be started with the 

collaboration of IRM, Belgium. Other collaborative studies will be initiated on the related 

activities. 

Introduction: 

Pakistan marking the area where South Asia converges with Southwest Asia and 

Central Asia. Total area of Pakistan is 796,095 square km. Pakistan Space & Upper 

Atmosphere Research Commission (SUPARCO) being national space agency operates 

Karachi Geomagnetic Observatory, which was opened in 1983. Its geographical location 

is at 24.95N latitude, and 67.14E longitude. The observatory operated AMOS-III 

(Automatic Magnetic Observatory System) continuously since 1983, designed and 

manufactured by EDA Instrument Inc., Toronto, Canada. In 2005 SUPARCO initiated a 

project to upgrade Karachi observatory and to establish new observatories in Islamabad 

and Sonmiani. In November 2007 SUPARCO upgraded Karachi observatory with the 

cooperation and assistance of Dr Jean L Rasson. It also purchased new equipment (dIdD, 

dI-flux theodolite) for the establishment of new observatory. SUPARCO planed to shift 

the observatory from Karachi to Sonmiani due to urbanization. As the observatory is in 

the vicinity of SUPARCO HQ, vehicles passing within 20m of the observatory produce 

disturbances in data. Sonmiani is a coastal town in the southeast of Balochistan province 

in Pakistan, approximately 85 kilometers northwest of Karachi. Establishment of 

observatory in Quetta is also under consideration as it is a rural area, and has 

mountainous topography appropriate to obtain refined data. Tectonic activities very 

146
promptly emerge in this region, therefore this observatory data will facilitate scientists to 

study precursory signature of earthquake. 

Karachi observatory owns the following classical instruments: 

Automatic Magnetic observatory System (AMOS) consist of, 

• Fluxgate magnetometer system FM-100C for X, Y & Z magnetic field component 

measurement 

• Sensor Head Fluxgate, Three components FM-157 

• Proton Precession Magnetometer system PPM-105 for total field measurement 

• Sensor Head, Precession 

SUPARCO purchased the following instruments for the establishment of new 

observatories: 

• Two Delta Inclination Delta Declination (dIdD) from GEM (GSM-19FD) 

• Two fluxgate magnetometer data logger type FLV1/A ”LAMA” variometers from 

IRM 

• Two DI-flux Theodolite (ZEISS) from MINGEO 

Observatories Up-gradation & Establishment: 

Karachi Observatory: 

In Nov 2007, we started the upgrade of Karachi Observatory with the aim to attain 

INTERMAGNET standards. BGS and IRM were involved. The idea was to train the staff 

and provide equipment. The BGS provided EDA fluxgate magnetometer type B, 

Digitizer and GPS receiver for H, Z and D measurements with data transmission on 

optical fiber cable. A DI-flux consisting of a Tavistock Theodolite mark-II serial T20 

provided the absolute declination and inclination measurements. The sensors of AMOSIII system and EDA fluxgate magnetometer are placed at extreme edges of the V-shaped 

magnetic hut. Tavistock Theodolite T20 and a Bartington fluxgate MAG 01H was placed 

on the pillar which is installed outside towards North. The azimuth value of a distant 

mark has been determined by sunshot for this pillar. The observatory staff was trained to 

carry out the D and I absolute measurements by means of the instruments. Two 

computers are placed in the main building of the observatory: DOS PC for running the 

AMOS software and window PC for GDAS View. 

The absolute measurements by means of the DI-flux theodolite are carried out 5 days 

in a week. The corresponding total readings are taken from the AMOS PPM-105 

recordings taking into account the difference between the location of the PPM 105 and 

the measurement pillar. 

Geomagnetic activity indices K calculate from Program Kasm.exe on the basis of 

recorded geomagnetic field components, according to the ASm1 method. The program is 

a 32-bit console application working under Windows. It accepts two formats of input 

data: 

a) binary format such as the Definitive data format in the INTERMAGNET CD-ROM, 

b) IMFV1.22 text format. 

 

147
The K-indices calculated with the use of program Kasm.exe can be stored either in the 

binary Intermagnet files or *.DKA text file, depending upon the parameters of program 

call. 

Initial Baseline Values of Karachi Observatory: 

At present, 8 months of absolute measurements by means of DI-flux theodolite are 

available. The obtained baselines are satisfying. Fig 4 shows the absolute measurements 

of H, D & Z. Baselines are adopted from the reduced absolute measurements by means of 

the DIflux theodolite on pillar and the Proton magnetometer. The total intensity 

difference between pillar and the proton magnetometer was determined to: 

 ∆Fppm-pillar = Fppm - Fpillar = 15.5 nT (1) 

Fig 1: Ground plan of Karachi Geomagnetic Observatory 

Fluxgate Magnetometer Electronics & 

Sensor Head, Upgraded Equipment 

Automatic Magnetic Observatory System 

(AMOS-III), Old Equipment 

Fig 2: Old and up-graded instruments of Karachi Geomagnetic Observatory 

N

1 

3 2

4 

5 

6

Legends: 

1. Main office 

2. Sensor hut 

3. Fluxgate sensor 

4. AMOS PPM sensor 

5. AMOS fluxgate 

sensor

12

20m

100m

100m

m

148
Baseline of Vertical component of KRC Obs

27550

27560

27570

27580

27590

27600

27610

27620

25-Sep-07 14-Nov-07 03-Jan-08 22-Feb-08 12-Apr-08 01-Jun-08 21-Jul-08

Months

Z (nT)

Baseline of Declination of KRC O bs

0

5

10

15

20

25

30

35

40

45

50

25-Sep-07 14-Nov-07 3-Jan-08 22-Feb-08 12-A pr-08 1-Jun-08 21-Jul-08

Months

D (min)

Baseline of H oriz ontal component of K RC Obs

35230

35235

35240

35245

35250

35255

35260

35265

35270

35275

35280

35285

25-Sep-07 14-N ov-07 03-Jan-08 22-Feb-08 12-Apr-08 01-Jun-08 21-Jul-08

Months

H (nT)

Fig 4: Baseline values of H, D & Z components of Karachi Observatory 

Sonmiani & Islamabad Observatories: 

In July 2008, SUPARCO established two geomagnetic observatories, one at 

Sonmiani, 85 km away from Karachi, and other at Islamabad. At Sonmiani, the FLV1/A 

Triaxial Fluxgate Variometer Magnetometer (LAMA) PANDECT sensor manufactured 

by the IRM and backup 12V battery are placed in the northward magnetic hut. The sensor 

of the dIdD GSM-19FD is placed in the magnetic hut and Zeiss THEO 010 is placed on 

the pillar in north east direction. 

149
In Islamabad, the magnetic hut is in the South-west direction. The sensors are placed 

at the extreme edges of the hut. The pillar is in the west direction of the main building 

and the azimuth value of the pillar has been determined for Islamabad while the azimuth 

value of the pillar at Sonmiani was measured during installation of the equipment. At 

both observatories, the connecting glass fiber cable (RS232 connection) and the power 

supply cable are placed into a pipe, which runs underground between the building and 

both magnetic huts. 

Fig 4: Ground plan of Sonmiani Observatory Fig 5: Ground plan of Islamabad Observatory 

At both observatories, the data logger is in the main building. LAMA electronics 

console, dIdD electronics and two computers are placed in the main building: a window 

XP PC for running the AD77 and GUDE expGPS LAMA softwares and a window PC for 

GEMLinkW3.0 software. The FLV1/A electronics console, GSM-19FD electronics 

console and PCs are powered by an uninterruptible power supply unit (UPS) with a 

capacity of about 30 minutes. The GUDE GPS PC clock synchronizer is connected to the 

PC via a USB adapter cable. 

Fig 6: Sonmiani Geomagnetic Observatory and Overhauser Magnetometer 

150
Fig 7: Islamabad Geomagnetic Observatory and Absolute pillar 

The installation of the instruments, data loggers and computers was carried out on 

23-27 July 2008. During this interval, staff were instructed to use the DI-flux, to reduce 

the absolute measurements, solar observations for determination of azimuth and to handle 

the recording system. Officers discussed technical aspects of recording system of 

variometer and absolute equipments with the experts. 

Both observatories produce data sets and send to Karachi which acts as central point. 

At Karachi all the data is compiled and a monthly bulletin is published. Information on 

magnetic storm is provided to HF data operators. The data is also used by ionospheric 

research section for reference and comparison. The data with test status is also uploaded 

on INTERMAGNET. 

Restoration of Quetta Observatory: 

Another plan is to restore Quetta observatory. Quetta is situated at an elevation of 

1680 meters above sea level in north-western Pakistan. It is north west of Karachi and 

south west of Islamabad. Quetta geomagnetic observatory was established in 1953 by 

British Geological Survey (BGS). Ruska Variograph for continuous recording whereas 

Quartz Horizontal Magnetometer, Balance Zero Magnetometer and Unimagproton 

Magnetometer for absolute measurements installed in this observatory since 1953. BGS 

is agreed to support SUPARCO in this respect and will provide us fluxgate and proton 

magnetometers to re-establish this old observatory which is situated at magnetically clean 

environment. 

ABSOLUTE 

HUT

variometer 

HUT

Fig 8: Quetta Geomagnetic Observatory 

151
Repeat Station Work: 

Dr. Rasson visited Pakistan in July 2008 to install LAMA variometer at 

Islamabad and Sonmiani. During his stay, repeat station work was also carried out at 

three selected sites to check and validate the work already done in 2004 by Geological 

Survey of Pakistan. 

Selected locations for repeat station survey by Geological Survey are: 

Locations Latitude 

(deg min sec) 

Longitude 

(deg min sec) 

Elevation 

(m) 

Declination 

(deg min sec) 

Skardu 35o 18' 16" 75o 37' 50" 2300 03o 18' 04" 

Gilgit 35o 55' 50" 74o 18' 35" 1525 02o 32' 01" 

Multan 30o 15' 47" 71o 30' 15" 156 - 

Repeat station survey: 

Locations 

(Lat, Long) 

Elevation 

(m) 

Declination 

(deg) 

Inclination 

(deg) 

Total Field 

(nT) 

Obs IGRF Obs IGRF Obs IGRF 

Skardu 

35o 17' 45.5"N, 

75o

 39' 07.7"E 

2300 3.5899 2.3166 53.6838 53.8667 50826.9 51056.2 

Gilgit 

35o 55' 41.3"N 

74o

 18' 11.1"E 

1525 2.6848 2.6167 54.3867 54.6833 50660.9 51211.8 

Multan 

30o

 15' 46.5"N 

71o 30' 15.1"E 

156 1.5848 1.3833 46.6774 46.8667 47751.1 48040 

Difference of observed and IGRF-10 model values is: 

Locations Declination 

(deg) 

Inclination 

(deg) 

Total Field 

(nT) 

Obs-IGRF Obs-IGRF Obs-IGRF 

Skardu 1.2733 -0.1829 -229.3 

Gilgit 0.0681 -0.2966 -550.9 

Multan 0.2015 -0.1893 -288.9 

Skardu Gilgit Multan 

Fig 2: Repeat Station Work carried out in Skardu, Gilgit & Multan 

152
Future Plans: 

We intend to offer geomagnetic services to the scientific and socio-economic 

sectors in the country. Different departments will be approached to provide them with the 

geomagnetic data and onsite measurements. As a test case, Civil Aviation Authority has 

been selected and being approached for providing our services. Other collaborative 

studies will be initiated on the related activities.

Summary: 

 Up to 2006, SUPARCO operated one geomagnetic observatory in Pakistan. At 

present SUPARCO is operating three observatories at Karachi, Sonmiani and Islamabad 

(all three observatories are operated with view to join INTERMAGNET) with the support 

of IRM and BGS. With the upgradation and establishment of observatories, a tradition of 

international cooperation with IRM and BGS has been continued. BGS will continue its 

support for the restoration of Quetta observatory. 

In 2008, SUPARCO started Repeat Station Survey to acquire geomagnetic data of 

highest possible quality for research purposes. In near future, SUPARCO has planned to 

conduct aeromagnetic survey of Pakistan. 

Acknowledgement: 

The authors thank the referee for the helpful comments and suggestions for their 

guidance involved in the improvement of this paper. The authors are highly indebted to 

John Riddick for the up-gradation of Karachi geomagnetic observatory, and Mr. 

Muhammad Ayub and Mr. Ayyaz Ameen for their technical support to establish 

Sonmiani and Islamabad Observatories. 

153
Livingston Island Observatory Upgrade: First Results 

S. Marsal, J. J. Curto, J. C. Riddick, J. M. Torta, O. Cid, M. Ibañez 

Observatori de l’Ebre, CSIC – Universitat Ramon Llull 

 Crtra.de l’Observatori, 8. 43520 Roquetes, Spain 

e-mails: smarsal@obsebre.es, jjcurto@obsebre.es, john_riddick@hotmail.com, 

jmtorta@obsebre.es, ocid@obsebre.es, mibanez@obsebre.es

Abstract 

A suspended DMI fluxgate magnetometer has been set up at the Livingston Island 

geomagnetic Observatory, Antarctica, during the 2007-08 summer survey. Power constraints at 

this site do not permit the operation of the fluxgate in a temperature-controlled environment. To 

investigate the temperature effects it was decided to continue operating the existing δD/δI 

variometer, which shows relatively good temperature stability. By operating two independent 

variometers and data loggers we can evaluate the performance of both instruments, as well as 

ensuring more complete data sets during the austral winter, when the station remains 

unmanned. Timing and data acquisition are now managed by a microprocessor-based 

controller. Fluxgate data are sampled at both 0.1 and 1 Hz frequencies, and a complete cycle of 

measurements is obtained from the δD/δI coils every minute. Once filtered, data are transmitted 

to the Ottawa GIN via the GOES-E satellite link using the INTERMAGNET facilities. The 

system design is briefly described along with the main difficulties and solutions adopted, 

especially those related to temperature stability. Finally, some initial results from the 

comparison of both variometer systems are presented; these include a proposed temperature 

correction for fluxgate measurements by means of a simple thermal conductivity model 

between the fluxgate head and the surrounding air. 

1. Introduction 

The Livingston Island geomagnetic observatory (62.67o S, 60.39o W), operated by 

the Ebre Observatory Institute, was initially deployed during December 1996 on the 

Spanish Antarctic Station, which is located in the South Shetland Islands, to the north of 

the Antarctic Peninsula. The observatory has been recognized by IAGA, who has 

assigned it the three-letter code LIV. It has served as a base station for the reduction of 

airborne and marine magnetic surveys in the tectonically active region where it is 

located (Maldonado et al., 2000; Livermore et al., 2000; Catalán et al., 2006), and its 

data have been used in several studies and models (Torta et al., 1999, 2001, 2002; Cain 

et al., 2003; Gaya-Piqué et al., 2006; Marsal and Torta, 2007; Marsal et al., 2007). 

Until December 2007, the Observatory instrumentation consisted of: a) a DI-flux 

Elsec 810A mounted on a Zeiss 015B non-magnetic theodolite, used to make the 

absolute measurements, and b) a variometer consisting of a GEOMAG SM90R 

Overhauser-effect proton scalar magnetometer mounted at the centre of a pair of dual 

axis Helmholtz coils in a δD/δI configuration, British Geological Survey design 

(Riddick et al., 1995). Due to the relative stability of this instrument with temperature, it 

can be considered to be semi-absolute. Nevertheless, moderate baseline interannual 

drifts of about 8nT/year on average were observed during the first six years of 

operation. It should be noted that the Spanish Antarctic Station is only manned during 

three and a half months every year, although the instruments are left recording the 

whole year. This fact prevents us from making absolute observations during the austral 

winter. The baselines must therefore be interpolated during the long period between 

summer surveys, leading to a great uncertainty. Fortunately this tendency has changed 

in the last five years, with the baselines currently drifting as little as 2 or 3 nT/year, as 

154
reflected in the yearly bulletins 

(www.obsebre.es/php/geomagnetisme/boletin_livingston.php). This fact made us decide 

to continue operating the δD/δI system. 

2. New Objectives and Installation 

With a new generation of observatory instruments available, along with our aim 

to meet INTERMAGNET requirements, we decided to update the Observatory 

equipment. The most significant upgrade consists of the suspended DMI fluxgate, 

model FGE version K, with a (total) dynamic range of 3200 nT and a resolution of 0.05 

nT. During December 2007 it has been deployed in a newly constructed non-magnetic 

fibreglass hut. 

The second objective was to increase the sampling rate. In addition to the 1-

minute data obtained with the previous vector magnetometer, with the new fluxgate we 

are now sampling the magnetic field at both 1 and 10 s periods. The 10-s output file 

contains the following items: date, GPS time along with its signal quality code, the three 

fluxgate components, sensor and electronics temperatures for correction purposes, δD/δI 

data and a Proton quality code denoting the Signal-to-Noise ratio of the Overhauser 

magnetometer. 1-s data files simply contain time and the three fluxgate components. 

A third objective was related to data transmission. A new Microcom GTX 

GOES satellite transmitter has been installed, and so far transmissions surpass 99% 

reliability. The 10 second data are filtered to one minute means and transmitted in near 

real-time to the Ottawa GIN through the GOES-E satellite link using the 

INTERMAGNET facilities. After reception, these preliminary data are made available 

in the Ebre Observatory website 

(www.obsebre.es/php/geomagnetisme/liv_graph01.php). 

Figure 1 represents the new observatory layout. The newly installed elements are 

the three component fluxgate, a Linux-based low consumption (7.5 W) embedded DCpowered PC, the new satellite transmitter and the GPS receivers for accurate time 

supply. 

The elements designed in Ebre Observatory include the PIC 16F877 

microcontroller, which controls the 10-s data acquisition. As the distance between the 

fluxgate magnetometer and the microcontroller is almost 80 m, to ensure reliable 

transmission the analog output from the fluxgate is converted to a digital RS-485 signal 

using a 16-bit ADAM-4017 analog-to-digital (A/D) converter in the magnetometer hut 

before being relayed to the microcontroller hut. 

In the controller box, this signal is converted to RS-232 using an ADAM-4520 

module and input to the PIC. At this point, the fluxgate 10-s data are combined with 

Proton data and sent to the embedded PC, which both stores and filters the data to be 

transmitted through the satellite link. The 1-s data acquisition is directly managed by the 

PC, which controls the sampling of a second A/D converter. 

24 volts DC are supplied from the Spanish Station, and afterwards converted as 

required by the different elements. The total energy consumption amounts to about 15W 

on average, which is suitable for most unmanned observatories. 

In order to investigate other methods of data transmission, we are working with 

an engineering group aiming to find an alternative way to transmit the observatory data. 

This alternative consists of an HF digital radio-link between Antarctica and Spain 

(Torta et al., 2008), which will be implemented with real data during the 2008-09 

Antarctic Survey. 

155
Fig. 1. The new observatory layout. In red, the elements of the existing observatory; in blue the 

new acquisitions; in yellow the elements designed in Ebre Observatory. 

3. Temperature stability 

Fluxgate measurements are affected by temperature, and hence there is need to 

thermally insulate the instrument. Unfortunately, the construction of a properly 

thermally insulated building in Antarctica would entail a great investment in time and 

money, besides the evident environmental impact in this protected zone of the world. 

Also, a series of power constraints in the Spanish Station prevent us from controlling 

the temperature of the fluxgate hut. The solution adopted consists of isolating the 

immediate instrument surroundings with a thick layer of foam. All the fluxgate 

electronics were placed in a thermally insulated box, with an additional layer of foam 5 

cm thick. The fluxgate pillar, on the other hand, was capped by a 20 cm thick insulator 

used in building construction industry called “foamglas” in order to provide thermal 

separation from the concrete, see Fig. 2. Additionally, a 7.5 cm thick foam cover 

insulates the sensor from the air. As a result, the daily thermal variation is attenuated to 

about 1.5oC on average in both the sensor and electronics interiors. 

156
4. On-site calibrations 

After installation, the main fluxgate 

parameters were checked. Following the 

precepts in the fluxgate manual (DMI, 2006), 

a first check was made concerning the 

fluxgate inter-element orthogonality. The 

measured misalignment angles differed by a 

few milliradians from the initial ones given in 

the DMI certificate, although they were still 

well within the manufacturers recommended 

specifications. This probably was due to the 

transportation by ship from Spain. 

A second check involved the fluxgate 

sensitivities. Assuming that the magnetic 

field components measured with the δD/δI 

magnetometer are not affected by scale 

factors, preliminary fluxgate scale values 

have been deduced by comparison with this 

vector magnetometer by means of linear 

regressions, Fig. 3. This procedure was 

applied on a daily basis, and the histograms in Fig 4 show the absolute frequencies of 

the values calculated. As it can be seen, our best estimates for the X and Y scale values 

are in good agreement with those given in the DMI certificate, although there is a 

certain disagreement with the Z scale value. This might be due to an additional scale 

factor introduced by the A/D converter. Nevertheless, in all cases the discrepancies are 

less than 1%. 

Fig. 2. Solution adopted for the thermal

insulation of the new Fluxgate sensor. 

Fig. 3. Daily fitting of the DMI FGE output, in volts (horizontal axis), versus δD/δI output, in 

nanoteslas (vertical axis). The δD/δI components have previously been corrected from bias by 

means of the absolute measurements. The example is referred to the Z component. 

157
Fig. 4. Histograms showing the absolute frequencies of the fitted scale values for X, Y and Z 

components. The “experimental optimum value” corresponds to the mean of the distribution, while 

“DMI” correspond to the certificated value. 

4.1. Temperature corrections 

Once in possession of accurate scale values and non-orthogonality angles, the 

problem of correcting the fluxgate data for temperature variations was faced. The 

reference instrument was again the δD/δI variometer, which was corrected using 

absolute measurements. In fact, as proton magnetometers are hardly affected by 

temperature, it is commonly assumed that the δD/δI is more stable with respect to 

temperature than the fluxgate, at least in the short term. 

The lower panel of Fig. 5 shows the measured sensor and electronics 

temperatures. Except for a constant difference, both temperatures roughly coincide, so 

(as we are interested only in variations) we will assume a unique measured temperature 

T. The differences in the Z component between both magnetometers (δD/δI – FGE) are 

displayed in the upper panel of Fig. 5. 

Fig. 5. Sensor and electronics temperatures (lower panel) along with instrument differences in the Z 

component (upper panel). Note the long term resemblance of the curves; on the contrary, rapid 

fluctuations of temperature are not reflected in the differences. 

158
Direct comparison of the plots from both panels suggests that, except for the high 

frequency fluctuations (i.e., periods shorter than 1 day), the instrumental differences 

follow the long term variations of temperature (i.e., periods longer than a few days are 

unmodified). We hereafter assume that the observed residual deviations of the DMI 

FGE with respect to the correct magnetic field values are exclusively due to temperature 

effects, as shown in equation 1 for the case of the Z component, where a linear relation 

is considered as a first approximation: 

FGE − ZZ /δδ +DIfluxID = FGEZ +CTK Z . (1) 

TFGE is the true temperature of the fluxgate sensor, KZ is the fluxgate temperature 

coefficient in nT/oC (referred to the Z component) and CZ is a constant. 

Obviously, assuming TFGE = T we can estimate optimum values for the two 

coefficients in eq. 1; nevertheless, the rapid fluctuations of temperature having no effect 

on the instrument differences (see Fig. 5 again) will never be accounted for. So either 

some of our previous assumptions are incorrect, or simply TFGE is not exactly given by 

T. In particular, the location of the temperature sensor providing T, about 25 cm away 

from the FGE head, may cause a transient temperature difference with respect to the 

fluxgate elements. We are currently investigating the possibility that this difference is 

produced because the fluxgate elements are in contact with a marble cube 12 cm on a 

side whose thermal momentum could influence on their temperature, slowing down the 

fastest temperature variations experienced by the temperature sensor. In order to 

reproduce the fluxgate sensor temperature, a thermal conductivity model was applied 

between the temperature sensor and this element: 

( ) ')'( 1 )0()( /'

0

/ dtetTeTtT tt t t

FGE

τ τ

τ

− −−

∫ = + , (2) 

which is the time-dependent solution of heat conductivity between a source at 

temperature T(t) and a body at temperature TFGE, separated by a thin insulator with 

equivalent thermal resistance R (see, for example, Tipler 1999). Denoting as C the heat 

capacity of the body, τ = RC is the time constant for heat penetration which results in 

the observed filtering of rapid temperature fluctuations. 

Except for the first term on the right hand, equation 2 is a low-pass filter which 

consists of a weighted average of the preceding sensor temperatures T(t). τ represents 

the typical length of time considered for this average. 

Substituting equation 2 into equation 1 we can estimate the best values for the 

model parameters K’s and τ (see Table 1), so that the observed outputs (instrument 

differences) are obtained as closely as possible from the measured input (T(t)). 

Estimated values 

KX (nT/oC) -0.35 ± 0.05 

KY (nT/oC) 0.50 ± 0.05 

KZ (nT/oC) -0.45 ± 0.05 

τ (hours) 10 ± 3 

Table 1. Best estimates of the model parameters along with their uncertainties. 

159
Thus, introducing the above parameters in equations 1 and 2, we can reasonably 

predict the instrument differences for each component (obviously equation 1 refers to 

the Z component, but it can be immediately adapted to X and Y) with a simple thermal 

conductivity model. The yellow graph in the lower panel of Fig. 6 shows the observed Z 

differences (already shown in Fig. 5, with a different scale), along with the results of the 

thermal model, in purple. The upper panel shows the residuals, which in general have 

been reduced to less than 1 nT. 

Fig. 6. Observed (yellow) and modelled (purple) instrument differences in the lower panel, along 

with the residuals in the upper panel. 

5. Conclusions and future work 

LIV Observatory has been upgraded with a DMI Fluxgate magnetometer which 

permits a magnetic field sampling rate of up to 1 Hz. Over the last 8 months the 

equipment has been operating reliably, relaying data back to Ebre Observatory through 

the GOES link from this unmanned site. This successful operation has been due to the 

design and the low power consumption of the new hardware, and the reliability of the 

energy supply provided by the Spanish Station. 

The thermal insulation of the instrument attenuates the daily temperature 

variations down to an acceptable degree. This attenuation of external temperature 

variations and temperature corrections in conjunction with δD/δI data will provide 

baseline control when absolute measurements are not available during winter season. 

Such temperature corrections demand a theoretical support based on realistic parameters 

concerning the thermal properties and the spatial distribution of the materials employed 

within the DMI Fluxgate, which in turn must help to improve the simple thermal 

conductivity model in use. Moreover, longer time series require to be analyzed in order 

to infer a more accurate behaviour of the system and check the validity of our model, 

and in particular the corrections derived from it. This task will be undertaken in the near 

future. 

160
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161
The effect of high-voltage DC power lines on 

the geomagnetic measurements at BFE 

Jürgen Matzka1*, Lars William Pedersen1, Cathrine Fox Maule1, Anne Neska2, Jan 

Reda2

, Anna Nilsson1, Hans-Joachim Linthe3

1

Danish Meteorological Institute, Copenhagen, Denmark 

2

Institute of Geophysics of the Polish Academy of Sciences, Warsaw, Poland 3

Geoforschungszentrum Potsdam, Potsdam, Germany 

*Corresponding author, jmz@dmi.dk, Tel. +4539157475 

Abstract 

A comparison with neighbouring geomagnetic observatories and variometer stations 

revealed temporary shifts in the geomagnetic measurements in Brorfelde (BFE), Denmark, by 

about 6 nT in the horizontal component and 3 nT in the vertical component, occurring 

simultaneously and often at local evening and morning hours. By deploying a mobile 

variometer station at various locations in eastern Denmark, the shifts were found to be caused 

by a single wire 1500 ampere DC power line 25 km SE of BFE, connecting the Scandinavian 

and the Central European power line network. A corresponding return current beneath BFE 

was confirmed by a magnetotelluric station at BFE, and causes the shift in the horizontal 

component. Time series of the transmitted power match with shifts at BFE. A similar, but 

more distant power line was found to cause shifts of 2 nT in the horizontal component at BFE. 

We are currently working on a method to calculate the shift from the power line data of these 

two power lines and correct for it. 

Introduction 

The geomagnetic observatory Brorfelde (IAGA code BFE, lat. 55.625°, long. 11.672°, 

see figure 1) has been operated by the Danish Meteorological Institute (DMI), Copenhagen, 

since 1980 (e.g. Pedersen, 2004). It replaces the geomagnetic observatory in Rude Skov 

(IAGA code RSV, operated from 1907 to 1980, lat 55.843°, long 12.457°). BFE is an 

Intermagnet station and is contributing as one of 13 stations with a weighting factor of 0.5 to 

the international Kp index (Menvielle and Berthelier, 1991). BFE is equipped with 2 

suspended FGE fluxgate variometers (Rasmussen and Kring Lauridsen (1990), version FGE-J 

and FGE-K) on concrete pillars with a separation of 1.5 m in a temperature controlled room. 

About 75 m to the south of the variometer house there is the absolute house with a Zeiss 010B 

DI-flux and a GEMSystems GSM 19 Overhauser magnetometer as absolute instruments. 

In September 2005 DMI started operating a variometer station on the island Roemoe 

(DMI code ROE, lat. 55.173°, long. 8.549°) at the Danish West coast (figure 1). This station 

consists of a suspended FGE fluxgate variometer version FGE-J2 on a concrete pillar in a 

thermally isolated (but not temperature controlled) box of 0.7 m x 0.7 m2

 floor space and 0.9 

m height. Baselines for ROE are determined occasionally by absolute measurements on a 

defined point close to the variometer. Variations are measured in the instrument’s coordinate 

system X’Y’Z’. We estimate from the level of the variation recordings and the calibration 

data of the instrument, that the misalignment between X’Y’Z’ and the local magnetic 

coordinate system HEZ (with being the magnetic East component measured in nT) is not 

larger than 0.4°. The absolute values are used to transform the variations measured in X’Y’Z’ 

into absolute values in the geographic coordinates X, Y and Z. The declination D is small (< 

2°) for both BFE and ROE in the years under consideration here, and for the purpose of this 

paper X’, H and X as well as Y’, E and Y are interchangeable. 

162
Fig. 1. Location of the geomagnetic observatories Brorfelde (BFE), Rude Skov (RSV) and 

Wingst (WNG) and the variometer station Roemoe (ROE). A mobile variometer was deployed at 

the locations R1 (close to BFE), R2 and R3. The high-voltage DC power lines ‘Kontek’ (green 

line) and ‘Baltic Cable’ (blue line) were found to cause significant magnetic fields at the location 

of BFE. 

Figure 2a shows the variations of HEZ on December 3, 2007, for BFE in black and for 

ROE in blue. The Z component for the German observatory Wingst (IAGA code WNG, lat. 

53.743°, long 9.073°, see figure 1) that is operated by Geoforschungszentrum Potsdam (GFZ) 

is shown in red. 

The differences between BFE and ROE for HEZ are shown in figure 2b. The differences 

are not constant in time. The main natural contributions to the observed changes in difference 

likely are the local time difference of quiet variations with ionospheric origin, and induction 

effects. The latter are caused by currents induced in the Earth by magnetospheric and 

ionospheric field contributions. These currents strongly depend on inhomogeneities in the 

distribution of electrical conductivity in the subsurface, e.g. between land and sea, which is 

complex for Denmark (figure 1), and therefore it is not astonishing that these are different at 

BFE and ROE. 

The comparison between BFE and ROE also revealed features that we term ’jumps’ here, 

mainly in H (or X) and Z, and typical examples for these jumps in the H component are 

shown in figure 2b (where a jump is marked at UT 19 to 20). A jump resembles the phases of 

reduced HBFE - HROE seen in Figure 2b from UT 6 to 7, UT 19 to 20 and UT 23 to 24. A jump 

typically consists of a sharp decrease of HBFE - HROE of about 6 nT and a simultaneous 

increase of ZBFE - ZROE of 3 nT within a few minutes times (e.g. at UT 19 in Figure 2a, there 

is also a slight decrease in E of about 1 nT that we neglected in the following). This new level 

is kept for about an hour and then a sharp change within few minutes of time (e.g. at UT 20 in 

figure 2) is restoring the old level approximately. However, a more complex temporal 

behaviour of jumps can also be observed: the jump starting at UT 6 is only nearing its old 

level after sharp changes occurring both at UT 7 and UT 11. The sharp increases or decreases 

163
associated with a jump will be termed ‘ramp’ in this paper (see Figure 2b, where a ramp at UT 

19 and a ramp at UT 20 are marked). 

0 3 6 9 12 15 18 21 24

UT [hours] on Dec. 3, 2007

100

105

110

115

120

HBFE [nT]

30

35

40

45

50

EBFE [nT]

140

145

150

ZBFE [nT]

0

5

10

15

20

HROE [nT]

90

95

100

105

110

EROE [nT]

90

95

100

ZROE, ZWNG [nT]

90

95

100

HBFE - HROE

[nT]

-65

-60

-55

EBFE - EROE [nT]

40

45

50

ZBFE - ZROE

[nT]

-1

0

1

E

X [mV/km]

-3

-2

-1

0

1

2

E

Y [mV/km]

0 3 6 9 12 15 18 21 24 a)

b)

c)

0 3 6 9 12 15 18 21 24

0 3 6 9 12 15 18 21 24

jump

ramps

Fig. 2. Example for typical ‘jumps’ and ‘ramps’ in magnetic and magnetotelluric data for 

December 3, 2007. (a) Comparison of variation in BFE and ROE. (b) Variation at BFE minus 

variation at ROE. (c) Electric field in north direction EX and east direction EY at BFE. 

The ramps in figure 2b (BFE - ROE data) in H with the highest amplitude are at UT 6, 19 

and 20. These are also clearly visible in figure 2a in the variations measured in BFE, but not 

at ROE, indicating that the associated jumps belong to the recordings of BFE. This is 

confirmed by the comparison between the three stations WNG, BFE and ROE in figure 2a for 

the Z-component, which shows that the jumps are a feature that belongs to the geomagnetic 

records at BFE. However, the weekly baseline measurements for BFE (see baseline 

information published on the Intermagnet CD Rom for recent years) are not indicative for any 

problem at the observatory, since the baseline for H is usually stable within very few tenths of 

a nT from one measurement to the next.

Also the project ‘Global quality check of observatory data’ at GFZ (http://www-app3.gfzpotsdam.de/COM/comparison_obs.html , Korte et al., 2007) has identified problems in BFE 

164
hourly mean values for autumn 1995 and early 2003 in precisely the X and Z component. 

However, the temporal characteristics of those were previously difficult to relate to the above 

described jumps. 

This paper describes how the above mentioned jumps at BFE were investigated and how 

finally two single-wire high voltage DC power cables, the Kontek between Denmark and 

Germany and the Baltic Cable between Sweden and Germany (see figure 1), were identified 

as their source. The Kontek is a 1500 A, 400 kV single-cable DC power line in operation 

since 1995. Up to 600 MW are transmitted. The 1335 A, 450 kV Baltic Cable is in operation 

since 1994 and also transmits up to 600 MW. The current in both cables runs from south to 

north, the return currents are not lead through a cable, but from anodes on the Danish and 

Swedish coast through the Baltic Sea to cathodes at the German coast. This gives some 

similarity to DC railways, where the return current also runs in the ground. Magnetic effects 

from DC railways on geomagnetic observatories have been described earlier, e.g. by Pirjola et 

al. (2007). 

The strategy was to first investigate the morphology and the temporal characteristics of 

the jumps with available observatory data. Additional measurements were made in a second 

step to test for a local source of the jumps, which could be an instrument error, or magnetic 

fields from close-by (say, within the observatory grounds or a few hundred meters around it) 

DC currents or a magnetic object that changes magnetisation, position or direction. 

After excluding both natural and local sources, it was assumed that a strong DC current 

(strong enough to cause a magnetic field of several nT at a considerable distance), was 

responsible for the jumps. A remote linear current close to the Earth’s surface (e.g. a power 

transmission cable) can cause a jump in the Z component, but cannot account for the jumps in 

the horizontal components. For the above described jumps that are simultaneous in the X and 

in the Z component, but absent in Y, a simple ‘line plus sheet’ model was developed: An eastwest running current sheet beneath the observatory BFE is the return current of a current in an 

electric power line that can be approximated by a linear, roughly east-west running cable at 

the surface. In such a situation the electric power line must be located south of the observatory 

to cause the observed simultaneous increase in Z and decrease in X. (A power line to the 

north of the observatory would cause a simultaneous increase (or decrease) in both 

components.) Consequently, it was planned to conduct 3-component magnetic field 

recordings with a mobile variometer station at locations south of the observatory, and it was 

expected that the jump amplitude in the Z component would increase towards the power line 

(and eventually change sign once the variometer station is located south of the power line). 

Finally, we have compared published power transmission time series for the Kontek 

power line and the Baltic Cable with BFE time series. 

Methods and results 

Existing geomagnetic data from BFE and WNG were used to indentify the frequency and 

the shape of jumps in BFE data, and to determine since when BFE geomagnetic data were 

affected by these jumps. For this purpose, the difference WNG-BFE for 1985 to 2005 

calculated from Intermagnet definitive data and stored in the Intermagnet binary format (pers. 

comm. Ole Rasmussen) was visually inspected for ramps using the ImagCDViewer 1.2 

(copyright British Geological Survey, written by Simon Flower). Visual inspection is a time 

consuming process, therefore only jumps occurring in January and October months were 

investigated for each year. For some years the timing and duration as well as the amplitude of 

the ramps in X was determined (1985 to 1987, 1994 to 2005). Additionally, the amplitude of 

the Z ramps was determined for a subset of years (1985 to 1987, 1994, 1996, 2002 to 2004). 

For the years 1988 to 1993 only the number of ramps and their date were documented. 

Especially during magnetically active times the difference WNG-BFE can show 

165
comparatively strong variations of natural origin (50 nT variation within a few hours were not 

unusual), and visual inspection becomes a quite subjective process. 

1985 1990 1995 2000 2005

year

0

40

80

120

nr. of ramps

0 5 10 15 20 25 30 35

duration (minutes)

-30

-20

-10

0

10

20

ramp in X [nT]

-20 -10 0 10 20

ramp in X [nT]

-20

-10

0

10

20

ramp in Z [nT]

total 847 ramps 1998 to 2005

650 ramps in

5 minute bins

1998 to 2005, 650 ramps 2002 to 2005, subsample 127 ramps

a) b)

c) d)

0 3 6 9 12 15 18 21 24

hour of the day, UT

0

5

10

15

20

nr. of ramps

example: October 2001

January 2001

Fig. 3. Statistics of jumps in BFE data from comparison with WNG. (a) The number of 

identified ramps for January and October months for 1985 to 2005. (b) Number of ramps within 

5 minute bins versus time of day. (c) Amplitude of ramp versus duration of ramp. (d) 

Comparison of ramp amplitude in X and Z. 

In figure 3a the number of identified ramps is plotted as bar for each January and October 

from 1985 to 2005 in the fashion indicated for the year 2001. A total of 847 ramps in the X 

component were identified. From 1985 to 1996 on average less then 10 ramps for every year 

were identified (figure 3a). A significant increase is observed from 1997 onwards, associated 

with great variability as expressed by particularly low number of ramps in January and 

October 2000 and January 2003; and particularly high numbers in October 2004 and 2005. 

Figure 3b is showing the number of ramps versus the time of day for a total of 650 ramps in 

1998 to 2005 as histogram with a bin size of 5 minutes: ramps occur mainly at the full hour 

and preferentially in the early morning and late evening hours. For the same dataset, the ramp 

amplitude is plotted vs. the ramp duration in figure 3c. Typical ramp amplitudes are from 3 to 

15 nT and typical ramp durations are from 3 minutes to about 20 minutes, but longer ramps 

with higher amplitude have also been observed. In figure 3d the ramp amplitude in Z is 

compared to that in X for a subset of 127 ramps. The amplitude in Z is found to be 

approximately half the size and in 119 out of 127 cases of opposite sign to that of ramps in X.

Seven records of magnetotelluric measurements with a combined length of more than 50 

days have been collected in BFE between November 21, 2007, and January 31, 2008. The 

magnetotelluric equipment made available by the Polish Academy of Sciences consisted of a 

3-component Lemi 03 fluxgate magnetometer, a telluric amplifier model TWG-0501

(Tomczyk, 2007) and a DR-02 data logger (Vaczyova et al., 2001). The data was logged with 

1 Hz or 0.5 Hz and the timing was set manually with a precision of 1 second. Geomagnetic 

variations in HEZ were measured on a pillar in the BFE absolute house. Unpolarisable 

166
copper/copper sulfate electrodes were buried about 0.8 m deep into the ground of the BFE 

observatory. The electrodes to measure the electric field EX in the magnetic north direction 

were separated 135 m and for the electric field EY in the magnetic east direction the separation 

was 75 m. A typical example for the electric field recordings is shown in figure 2c. As 

expected from the fact that jumps mainly occur in the magnetic north-south component, 

jumps in the electric field are observed in the east-west component and not in the north-south 

component (figure 2c). The ramps and jumps in the magnetic field recordings of the 

magnetotelluric equipment (no data shown) from the absolute house (that is location L2 in 

table 1, see next paragraph for description) precisely resemble the magnetic recordings from 

the observatory’s variometer house in timing and amplitude. 

A suspended 3-component fluxgate magnetometer Lemi 008 was made available by GFZ 

for recordings of HEZ outside the observatory grounds. The sensor was usually placed in a 

0.5 m deep hole or next to a tree and covered with a wooden box. In several meters distance 

the electronics and data logger unit was housed in an aluminium trunk together with a car 

battery as power supply. Measurements were first conducted on and close to the BFE 

observatory grounds (local locations L1 and L3 in table 1) and later at places more remote 

from BFE (remote locations R1 to R3). Ramps were identified by calculating the difference 

between the variations at the location and those from ROE. The ramp amplitude was 

compared to that of simultaneous ramps in the BFE recordings and the relative ramp size is 

given for both the H and the Z component in table 1. Even at 1.1 km distance (at R1) no 

change in the ramp amplitude is observed. A significant increase of the ramp amplitude is first 

observed 3.9 km south of BFE at R2 in the Z component (the 5 % increase in ramp at the H 

component indicated in table 1 is close to the noise level of determining ramp amplitudes). 

The 18-fold increase in relative jump size for location R3 indicates the Kontek line, a major 

DC power line between Denmark and Germany, as the likely source for the jumps observed in 

the BFE recordings. 

Table 1 Relative ramp size* for various locations 

location Description ramp in H ramp in Z 

L0 BFE variometer house 1 1 

L1 BFE garden, 25 m south-east of variometer house 1 1 

L2 BFE absolute house, 75 m south of variometer house 1 1 

L3 astronomic domes, 320 m west of BFE 1 1 

R1 gravel pit, 1.1 km east of BFE 1 1 

R2 70 m south of AC power line, 3.9 km south of BFE 1.05 (?) 1.2 

R3 3.4 km south-west of AC/DC transformer Kontek, 27.9 

km south-east of BFE 

2 18 

*Relative ramp size is ramp amplitude at location divided by ramp amplitude at L0. Values are 

approximate except for L0, where they are by definition 1. 

Power transmission data for the Kontek line and the close by Baltic Cable (both DC 

power lines are shown in figure 1) were compared to the jumps identified in HBFE - HROE. The 

power transmission data contains positive and negative values to distinguish between power 

import and power export. (Usually, power is transmitted northward at night and southward 

during the day. This is because Central European coal, oil and nuclear power stations produce 

more power than is used at night, and Scandinavian hydropower stations provide power at 

peak consumption times to Central Europe.) However, independently of power import or 

export, the technical specifications of both Kontek and Baltic Cable are so that the electric 

current runs in the cable only from south to north. Since the magnetic field direction is related 

to the electric current direction, which does not change sign for Kontek and Baltic Cable, the 

absolute values of the power transmissions data is presented in figure 4. (In fact, the power 

167
transmission data presented in figure 4 for the Baltic Cable are minute values calculated from 

hourly mean values by assuming certain power change rates and boundary conditions in the 

Baltic Cable, after Fox Maule et al. (in prep.)). By comparing Baltic Cable transmission data 

(for periods when Kontek was out of operation) with ramp amplitudes in HBFE - HROE it was 

found that a change of approximately 225 MW causes a ramp amplitude of 1 nT at BFE (Fox 

Maule et al. in prep; Neska et al., in prep). This ratio is reflected in figure 4 in the scale for the 

axis for HBFE - HROE (black curve, left axis) and for the axis for Baltic Cable transmitted 

power (blue curve, right blue axis). From HBFE - HROE (black curve) the magnetic effect of the 

Baltic Cable was removed by subtracting 1 nT per 225 MW transmitted power (blue curve) 

and the resulting HBFE - HROE after Baltic Cable correction (red curve, left axis) is plotted in 

figure 4. On top of it is plotted the transmitted power (absolute values of hourly mean values) 

of the Kontek (green curve, right green axis) so that 1 nT corresponds to about 140 MW 

transmitted power. Preliminary comparisons with ZBFE - ZROE show that only the power 

transmission in the Kontek, but not in the Baltic Cable can be seen in this component. 

Discussion and conclusion 

Figure 4 is clear evidence that the described jumps and ramps in the BFE recordings are 

caused by the Kontek and Baltic Cable DC power lines. The jump indicated in figure 2 b in 

BFE data from UT 19 to 20 and originally interpreted as a period of disturbed geomagnetic 

recordings at BFE is in fact the only undisturbed period at BFE during December 3, 2007. 

This can be seen in figure 4, where the period from UT 19 to 20 is the only time with zero 

power transmission on both the Kontek and the Baltic Cable. Consequently, the DC power 

lines do not only cause short term ramps and jumps, they also shift the general level of the 

BFE geomagnetic data by several nT and affect the low frequency signal (e.g. daily, monthly 

and yearly means) from BFE more than previously anticipated. 

The low number of ramps identified in 1985 to 1996 are likely of natural origin, thus 

representing the background noise for our visual inspection method which should be 

subtracted for all years. The increase of identified ramps in 1997 marks likely the start of the 

source causing the jumps. The high variability of ramp numbers after 1996 can be explained 

by the fact that only two months per year have been investigated, and the Kontek with the 

stronger influence is sometimes not operating. In fact in January 2003 the Kontek was not 

operating, leading to the low count of ramps in figure 3 a. The problem that the ‘Global 

quality check of observatory data’ at GFZ identified for BFE X and Z in January 2003 can be 

ascribed to the shift of several nT that are caused by the Kontek, but that is missing in this 

period. It is also likely that the problem identified for autumn 1995 is associated with 

operations of the Kontek, which was in use after an AC cable broke at the end of October1995 

and started normal operations in 1996 (ABB, 2008a). The Baltic Cable has been in operation 

since 1994 (ABB, 2008b), but due to the lower ramp amplitudes associated to it is more 

difficult to detect in the visual detection procedures that led to figure 3. So likely the statistics 

in figure 3 are mainly controlled by the temporal behaviour of the Kontek. It is also likely that 

the few identified ramps with duration of more than approx. 20 minutes (figure 3 c), and the 

few ramps with equal sign in X and Z component (figure 3d) are due to natural variations. 

Despite these shortcomings of the visual inspection, the statistical approach was extremely 

helpful to exclude a natural source by showing that the vast majority of the identified ramps 

occur around the full hour. 

168
0 3 6 9 12 15 18 21 24

UT [hours] on Dec. 3, 2007

90

95

100

HBFE - HROE [nT]

0

300

600

0

300

600

transmitted power [MW]

HBFE - HROE before

Baltic Cable correction

HBFE - HROE after

Baltic Cable correction

transmitted power Baltic Cable

transmitted power Kontek

true level for

BFE recordings

Fig. 4. The jumps at BFE correlate with transmission power in Baltic Cable (blue) and Kontek 

(green). HBFE - HROE (black) is first corrected (red curve) for the Baltic Cable contribution and 

then compared to the Kontek power transmission. 

At first glance, a technical problem of BFE instruments seems to be an unlikely cause to 

the jumps, since the comparison of the variometer readings with the absolute measurements 

showed stable baselines. However, absolute measurements are discrete and often follow a 

schedule (e.g. weekly on afternoons) that might not coincide with the occurrence of sporadic 

technical problems. Since the observed jumps occurred mainly at night time, the baseline 

stability at BFE could not be taken as guarantee that the jumps are not caused by a technical 

problem. Also, the effect on the X component was difficult to explain with a very simple 

model of a DC current in one remote cable alone, and the magnitude and opposite sign of 

ramps in X and Z could just as well be explained with a tilting of the variometers. Therefore, 

independent geomagnetic recordings in the proximity of BFE were conducted and confirm, 

that the jumps are homogenously affecting the geomagnetic field in the area around BFE (at 

locations L1, L2, L3 and R1, see table 1). This excludes both instrument errors and a local 

source (say, within a few hundred meters) for the jumps. 

The magnetotelluric measurements at BFE confirm the presence of an east-west oriented 

electric field, and this is evidence for the underground east-west oriented current system that 

is also detected in the magnetic H (or X) component. The additional measurements of 

geomagnetic variations at the remote locations R1, R2 and R3, all on the same Danish island 

Sealand like BFE, give a similar picture of a regional, east-west oriented current. This is in 

accordance with the return current of our simple ‘line plus sheet’ model. However, the 

associated power line current, that is feeding the east-west running sheet current, was 

expected to be running in the opposite direction, i.e. it was expected to be also east-west 

orientation. As seen in figure 1, this condition is not fulfilled for the north-south oriented 

Kontek, and therefore the initial strategy of deploying mobile variometer stations along a 

profile south of BFE might have even failed to locate the Kontek. The location R3 close to the 

Kontek was finally chosen because the jumps at BFE had temporal patterns similar to the 

169
power transmission data, which can be found on www.nordpool.dk (Raita, pers. comm.). We 

expect that the Z component at BFE is affected by the current running in the cable of the 

Kontek. It is further expected that a large part of the return current is flowing through the 

water of the Baltic Sea rather directly in north-south orientation, too. Since this part of the 

return current is in opposite direction to the current in the cable, it is partly cancelling the 

effect from the current in the cable on the Z component in BFE. The parts of the return 

currents from both Kontek and Baltic Cable, that flow through Sealand beneath BFE, are 

thought to be oriented in east-west orientation. 

Acknowledgements 

Ole Rasmussen provided a dataset based on Intermagnet definitive data, containing 

differences BFE-WNG for 1985 to 2005. WNG is operated by GFZ. BFE and ROE are 

operated by DMI. Nordpool provided transmission data for Baltic Cable. Kontek data were 

obtained from www.energinet.dk. Tero Raita, Johannes Kultima and Gerhard Schwarz are 

thanked for helpful discussion during the Nordic Comparison 2007 in Tromso, Norway. They 

have suggested to study power transmission data for power lines in the region. 

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spx?&opendatabase&v=17ea&e=us&m=100a& 

ABB, 2008b.http://www.abb.com/cawp/GAD02181/C1256D71001E0037C12568330063C68 

6.aspx? 

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investigation of their quality. In: XII IAGA Workshop on Geomagnetic Observatory Instruments, 

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Observatories. DMI Technical Report 04-14, Danish Meteorological Institute, Copenhagen, 93 

pages. 

Pirjola, R., Newitt, L., Boteler, D., Trichtchenko, L., Fernberg, P., McKee, L., 

Danskin, D. and van Beek, G.J, 2007. Modelling the disturbance caused by a dc-electrified railway to 

geomagnetic measurements. Earth Planets Space, 59, 943–949. 

Rasmussen, O. and Kring Lauridsen E., 1990. Improving base-line drift in fluxgate magnetometers 

caused by foundation movements, using band suspended fluxgate sensors. Phys. Earth Planet. 

Inter., 59 (1-2), 78–81. 

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Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, Publs. Inst. 

Geophys. Pol. Acad. Sc., C-99(398), 99–104. 

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Hurbanovo, Contrib. Geophys. Geod., 31, 1, 407–414. 

170
The Utilization of Ground Magnetometer Data in Magnetospheric Physics 

Robert L. McPherron - rmcpherron@igpp.ucla.edu 

Institute of Geophysics and Planetary Physics, University of California Los Angeles, CA 90095-1567 

Abstract 

The Earth’s magnetosphere is a region of distorted dipole magnetic field bounded by a 

current sheet called the magnetopause. Inside the magnetopause there are a variety of 

electrical current systems driven by the solar wind. These include the magnetotail current, 

the ring current, the partial ring current, the Region 1 and 2 field-aligned currents, the 

DP-2 Hall current, the NBZ Hall current, cusp currents, and the DP-1 or substorm current 

wedge. All of these currents change with the state of the solar wind and the conductivity 

of the ionosphere. Effects of these currents are observed on the ground by 

magnetometers. Arrays of magnetometers record effects of these currents and their data 

are used to infer the nature of these currents and the processes that produce them. In this 

paper we discuss a variety of signals present in magnetograms and interpret these signals 

in terms of the current systems thought to produce them. In magnetospheric physics it is 

important to have stable base lines over long intervals of time, many stations, and high 

time resolution. Good calibration and orientation of instruments are necessary but the 

requirements are less severe than they are in the study of the Earth’s main field. 

Introduction 

At the Earth’s surface it is apparent that the Earth’s internal magnetic field is 

complex. The most recent model is a spherical harmonic expansion of order 14 with 224 

coefficients. All of these coefficients vary with time so it is necessary to continuously 

monitor the field both at the surface and in space to update models. Some current models 

of this secular variation use expansions of degree 18, Models of the crustal field use an 

even higher order of 100. A world-wide distribution of magnetic observatories is required 

to acquire the data needed to determine model coefficients. In addition to routine 

measurements of the vector field each observatory must obtain absolute measurements of 

the components and magnitude of the field. These are used to adjust the baselines of the 

instruments that acquire continuous measurements. Even more complex systems are 

needed for spacecraft measuring the vector magnetic field in space. 

The external magnetic field is also complex and less easily represented with models. 

It also varies with time but much more rapidly than the internal field. The configuration 

of the magnetic field above the Earth’s surface is combination on the internal field and 

the magnetic field from a distribution of electrical currents in space. These currents 

depend on the state of the solar wind which changes continuously on a very short time 

scale. The electrical currents are created by the drift of charged particles, mainly 

electrons and protons, in the field generated internal to the Earth and the fields generated 

by their own drifts. To characterize these current systems it is necessary to continuously 

monitor the solar wind. 

The region of magnetic field external to the Earth is called the magnetosphere. This 

region is bounded by a sheet of electrical current called the magnetopause current. This 

current cancels the main field outside the boundary and approximately doubles it 

immediately inside. This boundary is only 10 Re from the Earth on the dayside but on the 

171
night side the field is stretched into a long cylindrical magnetotail bounded by the 

magnetopause current. A strong current called the tail current flows from dawn to dusk 

across the center of the tail and closes as the magnetopause current on its boundary. 

Closer to the Earth positive ions drift westward around the Earth creating a ring current 

that decreases the surface field. A magnetic storm results when there is an increase in the 

total energy of the drifting particles. Several times a day the inner edge of the tail current 

is diverted through the midnight ionosphere, down on the dawn side, west through the 

ionosphere, and up on the dusk side. This current system is called the substorm current 

wedge. Near midnight it increases the horizontal component of the surface field but 

outside the wedge formed by the up and down legs of the current the effect in the 

horizontal component is negative. The east component is dominated by the field-aligned 

portions of this current and pre-midnight under the upward current there is a positive 

deflection and post-midnight under the downward current there is a negative deflection. 

The magnetopause is produced by normal stress of the solar wind. The dynamic 

pressure normal to the boundary compresses the magnetic field. The magnetotail is 

caused by tangential stress. Closed field lines on the dayside are dragged to the nightside 

by a viscous interaction. Other field lines are connected to the solar wind by magnetic 

reconnection and transported over the polar caps into the tail lobes. Both processes 

produce an excess of pressure on the night side as compared to the dayside and field lines 

are returned to the dayside in an internal convection system. The motion of the field lines 

and plasma (the two are frozen together) drives electrical currents along field lines into or 

out of the ionosphere. Two cylindrical shells of current at the edges of the auroral oval 

are called Region 1 and 2 currents. These close either across the polar cap or in magnetic 

meridians across the auroral oval as Pedersen currents parallel to the electric field. Effects 

of these currents are obvious to satellites but are almost invisible on the ground. 

However, a second current is produced at right angles to the first and to the main 

magnetic field. This is the Hall current that is responsible for dawn-dusk magnetic 

perturbations in the polar cap, for southward perturbations in the dawn sector of the 

auroral oval, and northward perturbations in the dusk auroral oval. The Hall current flows 

from midnight towards noon and then back to midnight along the dawn and dusk auroral 

oval. Because of the high conductivity of the oval the return currents are concentrated 

and called auroral electrojets. During magnetic storms the ring current does not develop 

symmetrically. The current is strongest in the dusk sector and connects to the ionosphere 

at the ends of the enhanced region. This excess of ring current is called the partial ring 

current. 

All of the currents described above have complex patterns in the magnetosphere and 

ionosphere that change with the state of the solar wind. The size of the magnetosphere 

depends on dynamic pressure. The stronger the pressure the smaller is the 

magnetosphere. The viscous interaction increases with solar wind velocity but is always 

less important than reconnection. Magnetic reconnection occurs when the interplanetary 

magnetic field is southward. This creates antiparallel magnetic fields at the subsolar 

magnetopause that enables magnetic reconnection. Convection of plasma and magnetic 

flux is enhanced as are all of the other current systems. Thus geomagnetic activity is 

primarily a consequence of a southward turning of the interplanetary magnetic field. 

Although not discussed in this paper magnetic reconnection also occurs for northward 

magnetic field, but with field lines at high latitudes above the polar cusps. Topological 

172
arguments show that this type of reconnection does not affect latitudes below the auroral 

oval although it does create a distinctive current near the magnetic pole called the NBZ 

(northward Bz) current system. 

The purpose of this paper is to illustrate each of these magnetospheric current 

systems with drawings showing the distribution of the currents in space. The effects of 

each current system on ground magnetic recordings are also illustrated or described. Also 

we briefly describe the processes responsible for the currents. Ground measurements 

supplemented by satellite observations of the magnetic field are essential to our 

understanding of these currents and how they change with the solar wind and he internal 

state of the magnetosphere. 

Magnetospheric Current Systems 

The quiet day current 

The Sq (solar quiet) current is a double vortex ionospheric current system 

primarily confined to the dayside of the Earth’s atmosphere as shown in Figure 1 

[Parkinson, 1983]. This current is produced by solar heating and solar and lunar tides. 

The ionosphere at the subsolar point is heated by the Sun developing a high pressure 

region. The electric charges in the ionosphere flow away from the region of high pressure 

across magnetic field lines. Induction generates an electric current which produces 

magnetic variations on the Earth’s surface. The figure shows the current near equinox 

when the system is approximately symmetric about the magnetic equator (note the map 

uses geographic coordinates). Near the equator the current flows eastward from dawn to 

dusk. As the current approaches the dusk terminator it bifurcates and flows towards both 

poles along the conductivity discontinuity at the terminator. It then returns at higher 

latitudes becoming nearly a westerly current. Finally the currents flow equatorward near 

the dawn terminator completing the ionospheric circuit. The magnetic perturbations on 

the ground can be inferred by the right hand rule. (Visualize wrapping the right hand 

around the current vector at the equator.) On the ground beneath the current the 

perturbation is poleward. At high latitudes it is equatorward. Near the terminators the 

perturbations are primarily east or west depending on the hemisphere and time of day. 

The two foci of this system are located near 30° magnetic latitudes. Movement of the foci 

can alter the variation recorded by stations near the foci. The effects of this current 

system are considered noise in studies of magnetospheric currents and must be removed 

from the magnetograms. 

Time variations of external currents induce an electric field inside the conducting 

earth. This field drives current through the earth producing magnetic variations at the 

surface. The effects of these currents can be especially strong at sharp change in 

subsurface conductivity such as ocean shores. Near such boundaries large perturbations 

in the vertical component are observed. The magnetopause current discussed in the next 

section acts like a superconductor confining the earth’s magnetic field inside a volume 

called the magnetosphere. Any change in internal currents is reflected by a change in the 

boundary current so that no field escapes through the boundary. 

173
Figure 2. The quiet day (Sq) current system is primarily located on the day side and at equinoxes is 

nearly symmetric around the magnetic equator. Current circulates around two foci located near 

±30° magnetic latitude. At the equator the current is from dawn to dusk [Parkinson, 1983]. 

Noon

In space physics the Sq variation at a single station is estimated by a variety of 

techniques of increasing complexity. The simplest is to find the nearest quiet day and 

subtract it from the data. Often several quiet days in the same season are averaged and 

subtracted. A more complex method organizes observations from quiet hours in a matrix 

with 24 hours and 365 days. A double Fourier transform is performed and low order 

harmonics are used to interpolate the quiet variation in each component of the station. 

Much more complex procedures are currently used in studies of the main and crustal 

fields [Sabaka, et al., 2004]. These models use quiet-time satellite and ground 

observations acquired over significant intervals of time. In this case the main field, its 

secular variation, the crustal 

field, the ionospheric Sq 

variation, the equatorial 

electrojet and individual 

station biases are 

simultaneously and self 

consistently determined from 

observations. 

An example of the 

effects of the ionospheric 

dynamo near Boulder, CO on 

January 2, 2008 is shown in 

Figure 2. The red dashed 

lines show local daylight at 

this time of year with noon 

labeled. Note that the X 

perturbation is negative and 

roughly symmetric about 

Figure 1. The Sq current system generates magnetic 

perturbations on the ground as shown here in three components 

and magnitude of the surface field. The data shown here were 

recorded at the Boulder, CO magnetic observatory. Local dawn 

and dusk are indicated by vertical dashed lines and noon by an 

arrow (http://www.intermagnet.org/Data_e.html). 

174
noon as expected for a station north of the focus in the northern hemisphere. The Y 

component is anti symmetric about noon and negative near dusk. The peak to peak 

magnitude of the variation at this location and time of year is about 20 nT. There are also 

some effects in the Z component and the total field as well. 

The magnetopause current 

The dynamic pressure of the 

solar wind compresses the magnetic 

field of the Earth on the dayside to a 

roughly ellipsoidal cavity of radius 

10 Re (Earth radius) at the nose and 

15 Re on the sides. The boundary is 

a sheet of electric current as 

illustrated by results of a simulation 

displayed in Figure 3. This is called 

the magnetopause current and also 

known as the Chapman-Ferraro 

current. This current creates the 

boundary of the Earth’s magnetic 

field. There is no field from the Earth 

outside of this current sheet. At any 

external point the sheet current 

creates a magnetic field equal and 

opposite to the Earth’s field. 

Immediately inside it approximately 

doubles the Earth’s field. The form 

of this current system is nearly 

identical to that of the quiet day Sq 

current. In the northern hemisphere is circulates counter clockwise around a focus called 

the neutral point or polar cusp. The current is clockwise in the southern hemisphere. Its 

effect on the ground is very similar to the Sq current system and is most important on the 

dayside. 

K

Figure 3. Near the magnetic equator the 

magnetopause current flows from dawn to dusk. It 

curves around and moves to higher latitude where 

it closes in circles around a location called the 

neutral point or polar cusp). There is another 

current vortex in the southern hemisphere around 

the southern neutral point (Siscoe, http://wwwssc.igpp.ucla.edu/gem/tutorial/2000Siscoeppt/). 

Because the Chapman-Ferraro current flows on a surface that extends past the 

terminators its effects are present on the nightside as well as the dayside. Rotation of the 

Earth produces a slight daily periodicity at a single station. In studies of the ring current 

discussed below the contribution from this current is removed from the Dst index by the 

approximation that it produces a world-wind average perturbation with strength 

proportional to the dynamic pressure of the solar wind, i.e. δ H b Pdyn = where Pdyn is 

the dynamic pressure. The constant b has a value close to 8 nT/sqrt(nP). For typical solar 

wind this produces about 12 nT. However, extreme values of dynamic pressure often 

produce disturbances as large as 60 nT. Since the solar wind is always present its average 

value appears in the baseline for the H component magnetogram and must be determined 

separately. This contribution is of the order 10-20 nT. 

The magnetopause current is created by the motion of ions and electrons in the solar 

wind. As the wind encounters the Earth’s main field the electrons and ions in the wind 

are deflected in opposite directions by the Lorentz force. This creates a sheet of electrical 

175
current flowing in space. When the current is fully developed the pressure of the solar 

wind normal to the surface is exactly balanced by the pressure of the magnetic field just 

inside the boundary. The effect of the current inside the boundary is everywhere an 

increase in the strength of the magnetic field relative to the dipole. The shape of the 

dayside current is nearly hemispherical while the nightside shape is nearly cylindrical. 

Distances to the current are 10 Re along the Earth-sun line, 15 Re at the terminators, and 

a radius of 25 Re in the tail. 

The ring current 

The next external current to consider is the ring current responsible for magnetic 

storms. The ring current is a westward ring of electrical current around the Earth at a 

distance of about 4 Re. From the right hand rule its effect on the ground is a southward 

perturbation that reduces the strength of the main field. The stronger the current the larger 

is the decrease in the surface field. 

The ring current is produced by the westward drift of protons and the eastward drift 

of electrons in the radiation belts. Since the electrons have less energy and are rapidly 

lost to the atmosphere the current is dominated by protons, or in very disturbed times by 

singly ionized oxygen from the ionosphere. As illustrated in Figure 4 ions gyrate around 

the local magnetic field, 

travel along a field line 

toward the ionosphere, 

are mirrored in the 

converging magnetic 

field, and drift 

azimuthally around the 

Earth. In the diagram the 

blue line shows the 

trajectory of a single ion. 

Because the ions occupy 

all latitudes along a field 

line they completely 

engulf the Earth and 

create a solenoid of 

nested rings around the 

Earth. Their effect on the 

ground is a magnetic 

perturbation nearly 

constant and parallel to 

the Earth’s dipole axis. It 

has been shown that the 

magnetic perturbation at the center of the Earth is directly proportional to the total energy 

of all of the drifting particles. The Dst index (disturbance storm time) is designed to 

measure this perturbation and hence is a physical quantity descriptive of what is 

happening in space. 

west

Figure 4. The ring current is produced by the curvature and 

gradient drift of positive ions at distance of 3-10 Re. Charges 

gyrate around field lines as they travel along them towards the 

ionosphere. They are mirrored at the ionosphere and drift 

westward around the Earth. The westward current and the 

magnetic moment of gyration produce a magnetic field at the 

Earth’s surface. 

176
Figure 5. Magnetic perturbations caused by the arrival of an interplanetary shock at the Earth on 

February 15, 1967 are shown for the auroral zone (third panel) and midlatitudes (fourth panel). 

The top two panels show the strength of the interplanetary magnetic field and its component 

parallel to the Earth’s dipole axis. The variations plotted at the bottom and labeled Dst are caused 

by the ring current and partial ring current [Akasofu, 1977]. 

177
Magnetic storms are characterized by a world-wide disturbance in the magnetic field. 

At mid and low latitudes the disturbance in the H component is primarily negative, 

decreasing the surface magnetic field. The traces of H in the bottom panel of Figure 5 

show the variations recorded for two days by a number of stations located at midlatitudes. 

The sudden increase in H of ~40 nT just before 00 UT on Feb 16 is the “storm sudden 

commencement” caused by an increase in dynamic pressure. The initial interval of 

elevated H after this lasting to 09 UT is the “initial phase” of the magnetic storm. The 

subsequent interval of rapid decrease in H is the “main phase”. For this storm the 

average disturbance around the world (called the Dst index) during the initial phase (0-9 

UT ) was about +25 nT. The minimum of the average disturbance at 14 UT was -130 nT. 

During the main phase the disturbance is not symmetric about the Earth indicating that 

the currents responsible for the variations are not a complete ring around the Earth. The 

largest negative perturbations are recorded near dusk and the weakest near dawn. This 

property of the disturbance is called “asymmetry” of the magnetic disturbance. In most 

storms asymmetry (difference between largest and smallest disturbance) during the main 

phase is comparable to or larger than the Dst index. At about 1300 UT on Feb 16 the 

disturbance begins to decay and by 1600 UT it has become symmetric around the Earth. 

This interval of decay of the perturbations is called the “recovery” phase of the storm. 

The durations of the three phases: 8, 10, and 48 hours are fairly typical of moderate 

magnetic storms such as this one. 

Moving upward in the Figure the next set of traces is a superposition of H traces 

from observatories in the auroral zone. The nature of these currents is described in more 

detail below. The positive and negative bays (upward and downward fluctuations of the 

H component are produced by the convection electrojets driven by magnetospheric 

convection, and by the substorm electrojet that develops during the expansion phase of 

substorms. An empirical description of the nature of these disturbances is called the 

“polar magnetic substorm” [Akasofu, 1968]. The association of such disturbances with 

the development of the main phase of a storm is the origin of the name substorm. For a 

very long time it was assumed that substorms inject the particles that create the magnetic 

storm. Note the lower envelope of this set of traces is called AL (auroral lower), the 

upper envelope is called AU (auroral upper), and the distance between the two envelopes 

is AE = AU-AL. These three indices are the auroral electrojet indices. They crudely 

measure the strength of the two electrojets. During this storm the maximum hourly 

average AU index was 340 nT and the minimum hourly AL index was -360 nT. 

The next trace is the IMF Bz component. A close comparison of this trace with the 

auroral zone and midlatitude traces shows that auroral zone activity and the main phase 

of magnetic storms are created by southward (negative) interplanetary magnetic field 

(IMF). The top trace displays the strength of the IMF which indicates that an 

interplanetary shock followed by a CME was the cause of this activity. Before the shock 

the strength of the interplanetary magnetic field was a nominal 5 nT. After the shock the 

field strength was nearly three times larger. 

178
The tail current 

Next in terms of important external currents is the magnetotail current. This current 

can be represented by two solenoids merged together with current circulating in opposite 

directions in the two solenoids as illustrated in Figure 6. In the upper solenoid the 

magnetic field produced by the tail current is toward the Earth and in the lower solenoid 

it is away from the Earth. The pattern of the tail current transverse to the Earth-Sun line 

has the shape of the Greek letter theta. Current flows from dawn to dusk across the center 

of the tail (in the same sense as the ring current). At the boundary the current splits and 

circulates in opposite directions. The region where strong current flow is called the 

plasma sheet, while the regions above and below are called the north and south tail lobes. 

The effect of the tail current on the Earth is a southward perturbation on the Earth’s 

surface similar to the ring current. However, it is somewhat stronger at midnight than 

noon and creates an asymmetry in the surface perturbation. 

The magnitude of the effect of tail current on the ground is a matter of on-going 

controversy. Partly this is a semantic argument 

concerning what is tail current, what is ring current, 

and what is partial ring current. The ring current 

flows on closed drift paths around the Earth. The 

partial ring current is the enhancement in this 

current across the dusk meridian that closes partly 

through the ionosphere and partly on the 

magnetopause. For example, in Figure 6 the inner 

edge of the current is shown as a straight line. If 

instead it is wrapped around the night side of the 

Earth it will close on the magnetopause sunward of 

the terminators. Such closure considerably increases 

the strength of the tail current effects in a manner 

very difficult to separate from the symmetric ring 

current. For the simple model shown in the Figure 

effects at the Earth are of order 5 nT in quiet times 

and perhaps 30-50 nT during magnetic storms. For 

a current sheet wrapping around the earth estimates 

suggest that 25-50% of the disturbance measured in 

Dst index is caused by the tail current, i.e. current 

that does not wrap completely around the Earth. 

Tail Current

Figure 6. The Earth’s magnetotail is 

produced by a tail current system 

with “theta” cross section. Current 

flows from the dawn to dusk side of 

the tail closing above and below 

around the circumference of the tail. 

Magnetic perturbations caused by 

this current are show by 

representative field lines that pass 

around the Earth in a southward 

direction [Olson, 1982]. 

The tail current is created by a frictional interaction (tangential drag) between the 

solar wind and the Earth’s magnetic field. This drag pulls field lines away from the 

dayside stretching them into a long tail behind the Earth. The cause of this drag is 

discussed next. 

Tangential drag 

The magnetopause current is produced by normal stress of the solar wind dynamic 

pressure against the pressure of the internal magnetic field. Normal stress does not exert 

any drag on the magnetosphere so it can not explain the long tail behind the Earth. 

Tangential stress or drag, i.e. a frictional force on the surface of the magnetosphere 

179
creates the tail. There are two forms of tangential drag summarized in Figure 7. The first 

is called the viscous interaction. Momentum from the solar wind is transferred inside the 

magnetopause by waves and particles. This is picked up by the particles attached to 

closed field lines inside the magnetosphere. Since these particles are “frozen” on 

magnetic field lines, the field lines move tailward in the low latitude boundary layer as 

the particles acquire momentum away from the Sun. Eventually the field lines become so 

stretched that they are pulled out of the boundary layer and return to the dayside as shown 

by the Sunward directed arrows. The returning loops move inside the tailward moving 

loops. 

A second and more important 

process is shown by the kinked flux tube 

in the noon-midnight meridian. This 

process is called magnetic 

reconnection. In reconnection a 

southward magnetic field at the subsolar 

magnetopause is antiparallel to the 

Earth’s magnetic field. In this situation 

the two fields are forced together and 

merge. In effect the dipole field lines are 

cut and then reconnected to the solar 

wind field lines. Subsequently the solar 

wind pulls the newly opened field lines 

over the polar cap and out into an 

elongated tail. As time progresses these 

field lines move down through the lobes 

of the tail and are forced together in the 

center of the tail. Here they reconnect 

and move Sunward in the center of the 

tail. 

The equatorial intersection of flux 

tubes move in a two-celled pattern away 

from the Sun on the outside and toward 

the Sun on the inside. The feet of these 

field lines move in a similar pattern in the ionosphere. This is the primary cause of 

ionospheric convection, i.e. motion of the plasma away from the Sun in the polar cap and 

toward the Sun in the auroral oval. 

Figure 7. The magnetic tail is caused by 

tangential drag. Two processes producing drag 

are illustrated. Closed field lines near the 

boundary are drawn backwards in a boundary 

layer where momentum from the solar wind is 

transferred to the magnetosphere. Open field 

lines produced at the subsolar point by magnetic 

reconnection are carried over the polar cap and 

added to the tail. Closed field lines return to the 

dayside as they slip inward from the boundary 

layer. Open field lines must first reconnect and 

then return up the center of the tail [Cowley, 

1982]. 

Region 1 current 

The equatorial cross section of the magnetosphere is shown in Figure 8. The low 

latitude boundary layer (LLBL) is depicted as the region between the magnetopause 

(solid outer line) and the inner dashed line. In this layer the closed magnetospheric flux 

tubes are moving tailward due to the viscous interaction as explained by [Sonnerup, 

1980]. When charges move in a magnetic field they are deflected by the Lorentz force 

polarizing the plasma. Electrons and protons are deflected in opposite directions creating 

an electric field. In the return flow inside the boundary layers the flow and hence the 

180
electric field is reversed. This means there is a divergence of the electric field along this 

boundary or by Coulomb’s law, the accumulation of a volume of an electric charge. 

Magnetic field lines are nearly perfect 

conductors so the electric field in the 

magnetosphere is mapped onto the 

ionosphere. The ionosphere has a finite 

conductivity so the electric field drives a 

Pedersen current (in direction of electric 

field). At the magnetic footprint of the inner 

edge of the dawn boundary layer in the 

ionosphere the electric field is divergent as it 

is in the magnetosphere. This means current 

away from the boundary in the ionosphere. 

Current must be continuous so the only 

possible closure is down field lines from the 

magnetosphere. On the dusk side the electric 

field is convergent so there must be an 

upward current. These two sheets of current 

are called the Region 1 field-aligned 

current [Iijima and Potemra, 1976; 1978]. 

In the dawnside ionosphere the current flows 

both equatorward and across the polar cap. 

Similarly on the dusk side it flows poleward 

and out along field lines. At 800 km altitude 

a spacecraft magnetometer passing through 

these current sheets measures magnetic 

perturbations of order 500 nT. The total 

integrated current into and out of the 

ionosphere is of order 3 million Amps. 

+

+

+ +

+

+

+

+

+

+

Low Latitude 

Boundary Layer Magnetopause

Streamlines

The Pedersen current in the ionosphere produces almost no visible effects in 

magnetograms. The reason is explained by Fukushima’s theorem [Fukushima, 1976]. The 

ground effect of the field-aligned portion almost exactly cancels the effect of the 

ionospheric portion of the current. 

Hall Current 

The electric field in the ionosphere mentioned above drives another current whose 

effects are obvious in ground magnetograms. This is the Hall current shown in Figure 9 

and often referred to as DP-2 (disturbance polar of type 2). The origin of this current is 

again the Lorentz force but it is generally referred to as E cross B drift. In the presence of 

crossed electric and magnetic fields electrons and ions drift in the same direction 

orthogonal to both E and B. In the ionosphere this is from noon to midnight across the 

polar cap and then from midnight to noon along the two halves of the auroral oval. This is 

exactly the motion of the feet of the flux tubes convecting in the magnetosphere. If the 

electrons and ions drift with the same velocity as they do in the magnetosphere there 

would be no current. In the ionosphere, however, the ions collide with neutral atoms 

more frequently than electrons and so drift slower. This means there is a current opposite 

E

V

B

E

V

B

E

V

B

E

V

B

+

+

Figure 8. The Region 1 (R1) current is a 

field-aligned current system created at the 

inner edge of the low-latitude boundary 

layer. It enters the auroral oval on the dawn 

side, diverges with one path across the polar 

cap, and another equatorward across the 

oval around to the other terminator. It 

returns to space from the dusk boundary of 

the polar cap. Arrows show the directions of 

magnetic field, electric field and plasma flow 

in the boundary layer.

181
to the direction of drift. Thus the Hall current flows from midnight to noon and then back 

along the auroral ovals. This is the DP-2 current systems associated with magnetospheric 

convection. 

N

+ -

V+ VJH=ne(v+-v-

)

Figure 9. In the ionosphere the Region 1 current closes from dawn to dusk as a Pedersen current 

(in the direction of the electric field). In the orthogonal electric and magnetic fields the plasma 

drifts toward midnight. Electrons drift faster than electrons because of collisions with neutrals 

creating a current opposite to the direction of drift. This is the Hall current (perpendicular to E 

and B) which flows from midnight to noon across the polar cap and returns to midnight along the 

dawn and dusk auroral oval (adapted from [Crooker and Siscoe, 1981]. 

The DP-2 current 

The Hall current described in the previous section produces the DP-2 ionospheric 

current system that flows in both polar caps and auroral zones [Kokubun, 1971; Nishida, 

1971]. Its pattern in the north is shown in Figure 10. The map presents contour lines of 

the equivalent current in the ionosphere derived from ground magnetometers. Usually the 

pattern is skewed away from the noon-midnight meridian as shown here. Also it is much 

more concentrated in the auroral oval than one might expect. One reason for the 

concentration is the high conductivity of the oval due to particle precipitation from the 

magnetosphere. Another reason is the Region-2 field-aligned current system described in 

a later section. The concentrated currents along the auroral oval are called the auroral 

electrojets. The current along the oval on the dusk meridian is the eastward electrojet. 

The current along the oval on the dawn side is the westward electrojet. The entire system 

consisting of both polar cap currents and the two electrojets is called DP-2 (disturbance 

polar of the second type). Magnetic variations on the ground under the eastward electrojet 

are northward or positive in H or X component. Under the westward electrojet they are 

negative. An example of these effects was presented in the third panel of Figure 5. 

182
Figure 10. The DP-2 (disturbance polar of 

second type) current system is shown here for 

a specific event. The yellow arrow in the center 

of the polar cap represents current flowing 

from midnight towards noon. The red arrow in 

the dawn side auroral oval represents a 

concentration of westward current returning 

to midnight. The blue arrow on the dusk side is 

a concentrated eastward current. The two 

segments in the auroral oval are called the 

auroral electrojets [Clauer and Kamide, 1985].

The deflection of the DP-2 current 

system from alignment with the noon 

midnight meridian is a consequence of two 

factors. The first is the discontinuity in 

conductivity between the polar cap and the 

midnight and noon auroral oval. Because of 

the discontinuity positive charge tends to 

accumulate at the polar edge of the midnight 

oval and negative charge at the poleward 

edge of the noon oval as the Hall current 

tries to complete its circuit. This produces a 

midnight to noon electric field that is added 

to the dawn to dusk electric field of 

convection. The vector superposition of the 

two fields points toward late afternoon 

skewing the ionospheric convection in the 

direction shown. 

A second reason for the deflection is the 

process of magnetic reconnection. When 

there is a substantial dawn-dusk component 

of the interplanetary magnetic field 

reconnection causes convection to be 

dominantly on one or the other side of the 

polar cap depending on the polarity of the 

IMF. The two effects add for one sense of 

the IMF and subtract for the other. 

Figure 11. The DP-1 (disturbance polar of first

type) consists of a concentrated westward current

that flows across midnight [Clauer and Kamide,

1985]. The return currents are equivalent currents

(not real) being effects of field-aligned currents at

the east and west ends of the ionospheric currents.

(See Figure 13 and discussion of substorm current

wedge). 

The DP-1 current 

Careful study of ground 

magnetometer data reveals that 

occasionally the high latitude current 

system takes on a different form as 

illustrated in Figure 11. During active 

aurora a current system called DP-1 

(disturbance polar of first type) flows 

across midnight as illustrated in this figure 

[Clauer and Kamide, 1985; Kamide and 

Kokubun, 1996]. This current is westward 

in the auroral oval as is a portion of the 

DP-2 current discussed earlier. However, 

DP-1 is strongest pre-midnight while DP-2 

is strongest about 0400 local time. DP-1 

only persists during the expansion and 

early recovery phase while DP-2 may be 

present throughout a substorm. Note the 

closure of this current is shown at lower 

183
latitudes and in the polar cap. This is not what actually occurs. This picture is an 

“equivalent ionospheric current” derived from ground perturbations. The actual current is 

three dimensional. This is discussed later in the section on the substorm current wedge. 

The effect of this system in the auroral zone is to suddenly decrease the H (X) 

component for about 30 minutes followed by a slower recovery in the next 90 minutes. In 

Figure 5 the negative spikes below the baseline in the third panel are caused by the 

sudden formation of this current. The eastward electrojet does not respond in a systematic 

way during the development of the DP-1 current. 

Region 2 Current 

Figure 12 is a 3-D 

perspective view from 

above the Earth on the 

dawn side of the tail 

[McPherron, 1991]. It 

shows the Region 1 current 

described above with the 

blue lines connected to the 

inner edge of the low 

latitude boundary layer. 

The orange lines show one 

possible closure of this 

current through the polar 

cusp. Another closure 

might be along the 

magnetopause or through 

the solar wind. The 

magenta lines show the 

Region-2 field-aligned 

currents. These have the 

same shape as the Region-1 

current but the opposite 

polarity. The cause of this 

current is shielding as 

represented schematically by the charge distributions near dawn and dusk. These charges 

are a consequence of the drift of charged particles in the dipole field. Electrons drift 

towards dawn and ions towards dusk. These accumulations of charge produce a dusk to 

dawn electric field that cancels the dusk to dawn convection field inside the inner edge of 

the charged regions. Because the electric field inside is zero and outside it is not, there is 

divergence of electric field at this boundary. Again there must be a field-aligned current 

to feed or drain the divergence. The R-2 current is closed by particle drifts across the 

night side. Effects of the R-2 current and its connection with the R-1 current across the 

auroral oval cancel each other and it can not be seen on the ground. The east-west Hall 

current associated with the Pedersen current flowing north-south across the oval does 

cause ground effects. Note that the sharp inner boundary of the shielding means that the 

Hall current is confined to a narrow channel hence the name electrojet. 

Figure 12. The processes creating the Region 1 and Region 2 

current systems are summarized here [McPherron, 1991]. The 

Region 1 system described in Figure 8 is depicted by the orange 

lines (cusp current) and the blue line at the inner edge of the low 

latitude boundary layer. The Region 2 current is shown 

schematically by the inner current (magenta). Current out of the 

ionosphere at dawn closes through a partial ring current across 

midnight and down along field lines near dusk. It completes the 

circuit continuing as Region 2 current.

184
Substorm wedge current 

The substorm current wedge is shown schematically in Figure 13. The current 

wedge is a “short-circuit” of the tail current through the midnight ionosphere [Clauer and 

Kamide, 1985; McPherron et al., 1973]. The current is down post midnight, westward 

through the ionosphere at midnight, and up pre-midnight. Its equivalent current in the 

ionosphere is shown by the DP-1 pattern, but these are not real currents at low and high 

latitudes. The cause of the short circuit is a subject of current research and there is no 

consensus on its cause. Many believe it is a consequence of the pileup of flow from 

reconnection in the inner tail. 

Figure 13. The substorm current wedge is an approximation representation of the current system 

responsible for the DP-1 current system and for positive perturbations at midlatitudes. A portion 

of the inner edge of the tail current is diverted into the midnight sector of the auroral oval. It 

flows across the auroral bulge during a substorm and returns to space from the westward 

traveling surge. The ground magnetic perturbations can be represented by the DP-1 current 

system [Clauer and Kamide, 1985]. 

In the auroral zone the effect of the substorm current wedge is to produce the DP-1 

current pattern with a strong enhancement of the westward current across midnight as 

illustrated in Figure 11. The effect of this current is sharp negative decreases in the H 

component of the magnetic field of strength between 100-1000 nT. At lower latitudes the 

substorm current wedge produces a positive H perturbation inside the wedge (~50 nT) 

and a weaker negative perturbation outside. The D- component displays a positive 

perturbation to the west of the center of the current and a negative perturbation to the 

east. An example of these effects along a local time profile is presented in Figure 14. The 

scale of the magnetic effects is indicated near the top of the right panel. 

185
Figure 14. Magnetic perturbations at mid and low latitudes caused by the substorm current wedge are 

shown here for midlatitude station distributed in local time near midnight. The positive bay signature 

in H in Russia is characteristic of stations inside the center of the current wedge. The D component in 

the right panel is positive premidnight (west of midnight) and negative to the east [Meng and Akasofu, 

1969]. 

186
Partial ring current 

Another current evident during substorms and the development of the main phase of 

storms is the partial ring current [Horning et al., 1974]. In this diagram the partial ring 

current is shown near dusk at the left side of Figure 15. The substorm current wedge is 

again shown near midnight. The label “ATS” is a satellite located at synchronous orbit 

and emphasizes the point that the current wedge normally occurs on field lines above the 

spacecraft. 

Figure 14. The partial ring current is shown here as a “current wedge” centered on the dusk 

meridian. It is part of the ring current that is not symmetric around the Earth and closes through 

the ionosphere. Its effect is to enhance the negative perturbation of the ring current on the ground 

relative to what is measured at other locations around the Earth. It generally begins development 

before and during the growth of the substorm current wedge [Horning et al., 1974]. 

The representation of the partial ring current is the same as the current wedge except 

that it is reversed. The partial ring current causes a negative perturbation on the ground. 

When this effect is added to the effect of the ring current the perturbation pattern is 

asymmetric with more negative disturbance at dusk than at dawn. In reality the ring 

current and the partial ring current are parts of the same current system. 

The partial ring current is caused by the drift of positive ions around the dusk side. 

As the ions drift from dawn toward dusk in the dipole field they gain energy from the 

cross tail electric field produced by convection. This causes the current to be more 

intense near dusk. 

Discussion and Conclusions 

The magnetosphere is created out of the Earth’s main field by a variety of electric 

currents. These currents are produced by the drift of positive and negative charges of 

187
finite energy. The drift of the charges produce electrical currents whose magnetic fields 

distort the original main field into the configuration observed. The Earth’s magnetosphere 

is very dynamic and controlled by the solar wind reaching the Earth. As the dynamic 

pressure of the wind changes the magnetosphere expands and contracts. High solar wind 

speed increases the viscous interaction carrying more closed magnetic flux to the 

nightside. A southward interplanetary magnetic field enables magnetic reconnection 

between the solar wind and the Earth’s magnetic field. Reconnection opens field lines to 

the solar wind and allows it to transport a significant amount of magnetic flux from the 

dayside to the nightside. Magnetic reconnection at the center of the plasma sheet allows 

the field lines to reconnect and then return to the dayside. The motion of field lines 

creates electric fields in space that are mapped into the ionosphere. In the ionosphere 

electric current flows in the direction of the electric field as Pedersen current. Magnetic 

effects of this current are almost invisible because effects of the field-aligned part of the 

current almost cancel the effects of the ionospheric part. A second ionospheric current 

flows at right angles to the electric and magnetic fields. This is the Hall current whose 

direction is just opposite to the motion of the ionospheric plasma. Magnetic variations on 

the ground are primarily caused by this current. 

In the magnetosphere changes in magnetic configuration are transmitted by magneto 

hydrodynamic waves which travel at very high speeds. When a pressure gradient or 

magnetospheric current causes the magnetospheric plasma to move a kink is created in 

the magnetic field because the equatorial segment has moved while the ionospheric 

segment is stationary. A kink in the field is a perturbation orthogonal to the field which 

requires a field-aligned current. When the wave and its current arrive at the ionosphere 

the wave electric field drives a westward current that exerts a force on the ionospheric 

plasma causing it to move equatorward. As the plasma begins to move it polarizes 

producing an electric field. Because of these waves and currents the magnetosphere and 

ionosphere are a closely coupled system that react everywhere relatively quickly and very 

coherently. In this sense it is very different from the atmospheric system in which many 

discrete events can occur simultaneously without much interaction. Magnetospheric 

processes tend to be global in nature. Space weather exists in the form of substorms and 

magnetic storms which can have dramatic effects on the ionosphere and on human 

systems on the ground, in the ionosphere, and in the magnetosphere. 

Magnetic observatories were originally established to monitor the Earth’s main field 

to enable the creation of magnetic maps to be used in navigation. The records of these 

observatories revealed the complexity of natural variations in the field and demonstrated 

that many of the variations were generated above the Earth. Today magnetic records are 

essential in the study of solar-terrestrial physics and the space weather caused by the Sun. 

Magnetic records from around the world are used to create magnetic indices in near real 

time. These indices are used by many entities and government agencies to plan a variety 

of activities. More detailed observations from networks of observatories are utilized by 

the scientific community to infer the time development of the various current systems in 

space. As the number and quality of these observatories increases we learn additional 

details about the processes responsible for geomagnetic variations. As a user of these data 

we are thankful for the dedication of observatory operators to provide the highest quality 

data possible. 

188
Acknowledgments 

We acknowledge support for this work by the National Science Foundation ATM-0720-

422 and the National Aeronautics and Space Administration NNX07AG16G. We also 

thank the various authors and journals for permission to use previously published figures 

to illustrate the concepts presented here. 

References 

Akasofu, S. I. (1968), Polar and magnetospheric substorm, edited, p. 280, D. Reidel Publishing 

Co., Dordrecht, Netherlands. 

Clauer, C., and Y. Kamide (1985), DP 1 and DP 2 Current Systems for the March 22, 1979 

Substorms, J. Geophys. Res., 90(A2), 1343-1354. 

Fukushima, N. (1976), Generalized theorem for no ground magnetic effect of vertical currents 

connected with Pedersen currents in the uniform-conductivity ionosphere, Rep. Ionos. Space 

Res. Jpn., 30(1-2), 35-40. 

Horning, B., et al. (1974), Application of Linear Inverse Theory to a Line Current Model of 

Substorm Current Systems, J. Geophys. Res., 79(34), 5202-5210. 

Iijima, T., and T. Potemra (1976), The Amplitude Distribution of Field-Aligned Currents at 

Northern High Latitudes Observed by Triad, J. Geophys. Res., 81(13), 2165-2174. 

Iijima, T., and T. Potemra (1978), Large-Scale Characteristics of Field-Aligned Currents 

Associated with Substorms, J. Geophys. Res., 83(A2), 599-615. 

Kamide, Y., and S. Kokubun (1996), Two-component auroral electrojet: Importance for substorm 

studies, J. Geophys. Res., 101(A6), 13027-13046. 

Kokubun, S. (1971), Polar substorm and interplanetary magnetic field, Planetary and Space 

Science, 19(7), 697-704. 

McPherron, R. L., et al. (1973), Satellite studies of magnetospheric substorms on August 15, 

1978, 9. Phenomenological model for substorms, J. Geophys. Res., 78(16), 3131-3149. 

McPherron, R. L. (1991), Physical Processes Producing Magnetospheric Substorms and Magnetic 

Storms, in Geomagnetism, edited by J. Jacobs, pp. 593-739, Academic Press, London. 

Nishida, A. (1971), DP 2 and polar substorm, Planetary and Space Science, 19(2), 205-221. 

Parkinson, W. D. (1983), Introduction To Geomagnetism, Elsevier Sci. pub. Co., Inc, Belfast, 

Ireland. 

Sabaka, T.J., N. Olsen, and M.E. Purucker (2004), Extending comprehensive models of the 

Earth's magnetic field with Orsted and CHAMP data, Geophysical Journal International, 

159(2), 521-47.. 

Sonnerup, B. (1980), Theory of the Low-Latitude Boundary Layer, J. Geophys. Res., 85(A5), 

2017-2026. 

189
Ongoing Geomagnetic field 1-second value measurement by JMA 

MINAMOTO Yasuhiro 

Kakioka Geomagnetic Observatory 

Japan Meteorological Agency 

Abstract 

Geomagnetic field one-second data exchanges have become an important issue in the 

INTERMAGNET community and other concerned. The Japan Meteorological Agency (JMA) 

is one of the pioneers of the one-second measurement of the geomagnetic field on a routine 

basis. We have observed and provided one-second data values of the geomagnetic field for 

about 25 years at Kakioka and for more than 10 years at Memambetsu and Kanoya. This 

report presents the ongoing one-second value measurements at the JMA’s observatories, in 

particular specifications of the magnetometers with a wide frequency range and their 

calibration to share the information on our one-second measurements with the observatory 

community. 

The main instruments for one-second measurements are three-axis highly sensitive 

fluxgate magnetometers. The fluxgate magnetometers’ conditions are monitored by tiltmeters 

and thermometers. One-second total force measurements are being made only at Kakioka, 

because the baseline of the total force calculated from the H and Z components of the 

fluxgate magnetometer data is so stable. 

The fluxgate magnetometers for the one-second measurement are routinely calibrated by 

using an external DC/AC voltage source that generates magnetic fields inside the sensor unit. 

The magnetometers are also calibrated by applying artificial magnetic fields with a set of 

large square Helmholtz coils where each side is 3 meters long and errors in axis alignment are 

less than 6 minutes of arc. 

Introduction

The INTERMAGNET program has been collecting 1-minute data of three vector 

components of geomagnetic fields measured at magnetic observatories worldwide. Those 

data are distributed in near real time and are used by academic and industrial communities 

such as space weather, navigation, and solid Earth studies. Scientific research and other 

applications are increasingly in need of geomagnetic field data, with more frequent sampling 

and higher resolution (Korepanov, 2008). Geomagnetic field one-second data exchanges have 

become an important issue within the INTERMAGNET program. 

One-second data of the geomagnetic field have been observed and provided for about 25 

years at Kakioka (36o

13’56”N, 140o11’11”E), for more than 10 years at Memambetsu 

(43o

54’36”N, 144o11’19”E) and Kanoya (31o25’27”N, 130o52’48”E) by the Japan 

Meteorological Agency (JMA). Kakioka started measuring four components of the 

190
geomagnetic field, declination (D), horizontal force (H), vertical component (Z), and total 

force (F) every three seconds by using the four optical pumping magnetometers in 1970’s and 

first published 3-second values in our 1983 yearbook (Kakioka Magnetic Observatory 1984). 

One-second measurements of the geomagnetic field at Kakioka started in 1984 (Kakioka 

Magnetic Observatory 1985). A fluxgate magnetometer was installed for the one-second 

measurements at Kakioka in 1990 (Tsunomura et.al., 1994). Similar one-second value 

measurements started at the two branch observatories, Kanoya in 1996 (Kakioka Magnetic 

Observatory, 1997) and Memambetsu in 1997 (Kakioka Magnetic Observatory, 1998). 

These one-second data are available via the World Data Center for Geomagnetism, Kyoto 

(http://swdcwww.kugi.kyoto-u.ac.jp/). The data including the one-second values and their 

products of the three observatories are also provided as a yearbook on a CD-ROM entitled 

“Report of The Kakioka Magnetic Observatory” annually. This CD-ROM is available to the 

public from a data providing project of the Japan Meteorological Business Center 

(http://www.jmbsc.or.jp/). 

This report presents the ongoing one-second measurements at the JMA’s observatories. 

The specifications of the magnetometers with a large frequency range are presented along 

with the calibration results of the magnetometer. Technical details of our magnetometer 

systems are given in Owada et.al. (1998) and references are therein. 

Three axis highly sensitive fluxgate magnetometer 

A schematic diagram of the system components to measure one-second values of the 

geomagnetic field at Kakioka is shown in Figure 1. A three axis fluxgate magnetometer is 

the main instrument for the one-second measurement. The magnetometer receives a trigger 

from an external clock every second to start a measurement. Both digital and analogue 

outputs are available from this fluxgate magnetometer. Only the digital output is used to 

obtain the one-second values. It is important to note that the one-second data have not been 

corrected by absolute measurement. 

tiltmeters thermometers

DC/AC ex ernal signals t

External trigger

(clock) Analogue output

Digital output 1-sec value

large Helmholtz coils

(only at Kakioka)

calibration

Highly sensitiv

fluxgate 

magnetomete

e 

r 

 

Fig.1 Procedure for obtaining one-second value 

191
The main specifications of the fluxgate magnetometer at Kakioka are shown in Table 1. 

The magnetometers at Memambetsu and Kanoya are equivalent to that at Kakioka. This 

magnetometer is highly sensitive and stable so that it is capable of providing 10Hz sampling 

data sampling as well. The resolution of 0.01nT is practically enough because we estimate 

natural noise levels at three observatories are about 0.03nT. 

Temperature coefficient <0.5nT/degree 

Stability <±0.1nT/day 

Measuring range -600nT to +600nT 

Error in axis direction <6’ 

Output noise level <0.05nT 

digital output resolution 0.01nT 

Table1. Specifications of a three axis Fluxgate magnetometer installed at KAKIOKA. 

 A timing chart of one-second sampling is shown in Figure 2. External triggers that are 

controlled by a GPS clock are used for time stamps. The three components, H, Z, and D are 

sequentially sampled 32 times each second. The 32 samples for each vector component are 

averaged to output a one-second value. It takes 6 milliseconds to sample one datum. As a 

result, the first 576 milliseconds during a second are used for sampling. Then, the last 200 

milliseconds are used for serial data output. This sampling protocol is restricted by the 

original analogue/digital converter that was employed when the system was designed to 

produce digital output in 1990. The analogue output can be sampled at other sampling rates. 

Currently, the analogue output is highpass-filtered and is digitized into 0.1-second (10Hz) 

values. 

1000msec=1sec

800msec

H

1

Z

1

D

1

H

2

H Z D

32 32 32

output 

RS232c

6msec External trigger 

(GPS)

576ms average for 1-sec value

00:00:00 00:00:01

Fig.2 Timing chart of one-second value sampling 

192
Two tiltmeters and a thermometer are installed on the inside of the outer cover of the 

sensor unit to monitor the condition of the magnetometer (Fig.1). The northward and 

eastward tilt angles and the temperature are measured every second. 

Figure 3 shows one-year segments of the daily mean temperature and tilts angles of the 

fluxgate magnetometer’s sensor unit at Kakioka. The magnetometer sensor is set 5 meters 

below the ground level resulting in a temperature change less than 3 degrees a year. Since 

the temperature coefficient of this magnetometer is less than 0.5nT/degree (Table.1), a 

change due to the temperature in the magnetometer output is less than 1.5nT throughout the 

year. 

The tilt varies rapidly in an irregular manner compared to the temperature. Tiltmeters’ 

data in July and September 2007, which are marked by ovals in Fig.3, show changes in the 

magnetometer level due to heavy rains. Those changes can be seen in one-second values 

measure by the fluxgate magnetometer as well. 

The temperature and tilt variations are used to correct the baseline of the variation 

measurement by the fluxgate magnetometer. Our current observatory is to apply the baseline 

to the One-minute values but not the one-second data. 

20

40

60

80

F eb-07M ay-07A ug-07N ov-07Feb-08

0

4

8

12

16

20

seconds

sensor temperature

tilt N-S

tilt E-W

°C

Fig.3 Daily mean temperatures and tilts of the fluxgate magnetometer’s sensor at Kakioka from Feb.2007 

to Jan. 2008 

1. Continuous total force measurement 

The total force has been observed every second at Kakioka since 1984. An optical 

pumping magnetometer was first used and then replaced with an overhauser magnetometer 

made by GEM, Canada in 1993. The total force is measured every minute at Memambetsu 

and Kanoya by proton precession magnetometers. 

193
The Effects of Missing Data on the Computation 

of Hourly Mean Values and Ranges 

L.R. Newitt 

Boreal Language and Science Services 

310 - 1171 Ambleside Dr 

Ottawa, Canada K2B 8E1 

lnewitt@sympatico.ca 

Abstract 

When hourly mean values and ranges are calculated with less than 60 minute-values, the resultant mean or range is 

likely to be in error. This error depends on the level of magnetic activity, the number of missing values, and whether 

the missing values are all contiguous, or are distributed randomly throughout the hour. Using data for the year 2000 

from four Canadian observatories, we find that the errors, expressed as the RMS differences between the true hourly 

means and those with missing data, vary almost linearly with the number of missing values; they also increase as the 

magnetic activity increases. The differences are larger if the missing data are clustered to form a single large gap. 

These results suggest that the 10% rule, which is used by many observatory operators, is inappropriate. Instead, the 

number of missing data that can be allowed when calculating an hourly mean value should be determined statistically 

for a reasonable error. 

1.0 Introduction

Hourly Mean Values (HMVs) are an important product of the global magnetic observatory network. 

They constitute a record of the magnetic field that, at some observatories, goes back to the midnineteenth century. Prior to digital recording, HMVs were determined from analog magnetograms by 

using a plastic or glass scale to estimate visually the mean value. Since the beginning of digital 

magnetometry, HMVs have been calculated by taking a simple average of one-minute values, starting at 

minute 0 and ending at minute 59 of the hour. (These two statements are, of course, 

oversimplifications. I have ignored the fact that many observatories used to scale spot values on the 

hour instead of hourly means. I have also ignored the controversy that once raged over which data point 

constituted the first point of the hour, and the even more fundamental question of what constitutes a 

minute value.) 

For the observatory operator tasked with computing HMVs, an obvious question to ask is this: what 

should be done when one or more data points are missing during an hour? Although this question may 

seem obvious, during the first 30 years of the digital era it seems that it was seldom asked. The most 

recent guide for observatory practices (Jankowski and Sucksdorff, 1996) does not mention the problem 

at all. Neither does Wienert (1970), which indicates that the effect of missing data was not an issue 

during the analogue era. 

However, since 2001, the question has evoked heated discussion at IAGA conferences and 

magnetic observatory workshops. Opinions are many, but a plurality of observatory operators seem to 

favour a blanket application of the 10% rule: compute an hourly mean unless more than 10% of the data 

are missing. So far, only two papers on the subject have been published (Mandea, 2002; Schott and 

Linthe, 2007). Both of these papers conclude that the error involved depends on the morphology of the 

field variations and the level of activity as well as the length of the data gap. However, Mandea (2002) 

states that the10% rule can be used reliably most of the times. A third paper on the subject (Herzog, 

2009) appears elsewhere in this issue. 

In this paper, I look at three aspects of the problem: how errors in hourly means caused by data 

gaps depend on the number of values that are missing; how errors caused by data gaps depend on 

194
whether these missing values are randomly distributed throughout the hour or are contiguous (i.e, form a 

single gap); how errors caused by missing data depend on the magnetic activity. Finally, I will discuss 

the implications of my findings for the 10% rule. 

Although discussions to date have focussed almost exclusively on hourly mean values, the effect of 

missing data on hourly ranges (HRs) is also important. If hourly ranges contain significant errors, there 

will likely be errors in the indices that are produced from the observatory’s data. 

2.0 The Data 

Data from four Canadian observatories were used in the analysis: Ottawa(OTT), Meanook (MEA), 

Fort Churchill (FCC), and Resolute Bay (RES). All four observatories are operated by the Geological 

Survey of Canada and form part of the INTERMAGNET network. These four observatories cover a 

wide range of magnetic conditions. OTT is a mid-latitude observatory; FCC is auroral; RES is in the 

polar cap. MEA is located just south of the auroral zone and displays characteristics of both a midlatitude and an auroral observatory. 

The data consist of definitive one-minute values of the north (X), east (Y) and vertical (Z) 

components of the magnetic field for the year 2000; in this study, only the X component was used. 

Hourly mean values and ranges were computed for each hour for which all 60 minute-values were 

present. A complete year with no missing data consists of 8784 values. There are very few missing 

hours, as can be seen in Table 1. Each hour was also assigned an activity level (Q) based on the hourly 

range of the X component, as shown in Table 2. The activity levels are those used by the Ottawa 

Regional Warning Centre in the production of its magnetic forecasts and reviews for the sub-auroral 

zone (Hruska and Lam, 1998). Although the Ottawa RWC applies different upper and lower limits to 

each activity level for locations in the auroral zone and polar cap, for purposes of this analysis it makes 

more sense to use the same definition for all observatories. The table also shows the number and 

percentage of hourly ranges that fall within each activity level for the four observatories. The sample 

sizes are far from uniform, but exceed 700 in 14 of the 20 possible cases, so it is certainly possible to 

compute reasonable statistics. In two, perhaps three cases, the sample size is small enough to be of 

concern. At Ottawa, there were only 33 hours in the entire year that could be considered “major storm.” 

Table 1 Number and Percentage of Hourly Mean Values Missing 

Observatory OTT MEA FCC RES 

Number of Missing Values (%) 12 (0.14) 22 (0.25) 32 (0.36) 5 (0.057) 

Table 2 Distribution of Magnetic Activity 

Activity Level (Q) 

1 (quiet) 2 (unsettled) 3 (active) 4 (stormy) 5 (major storm) 

<30 nT 30 to 60 nT 60 to 105 nT 105 to 300 nT >300 nT 

OTT 7651 hours 

(87.22 %) 776 (8.87) 209 (2.38) 101 (1.15) 33 (0.38) 

MEA 5407 (61.71) 1604 (18.35) 759 (8.66) 726 (8.31) 260 (2.97) 

FCC 2456 (28.06) 2020 (23.08) 1696 (19.38) 2113 (24.14) 467 (5.34) 

RES 3106 (35.38) 2605 (29.67) 1712 (19.50) 1218 (13.87) 138 (1.57) 

195
3.0 Data Analysis 

3.1 Hourly Means 

Missing data may consist of single points scattered randomly or semi-randomly throughout an hour. 

The removal of spikes is a common cause of random missing data. Regularly occurring spikes, such as 

those that occur at some Canadian observatories every 12 minutes due to the GOES transmitter are 

examples of semi-random minute data. On the other hand, problems such as power failures or other 

forms of instrument malfunction will result in several contiguous missing values. De Santis has 

expressed (Schott and Linthe, 2007) what I would consider to be the intuitive expectation of most in the 

community: one would expect that under typical magnetic field conditions, an HMV computed with M 

missing data distributed randomly over the hour would be closer to the true value than an HMV 

computed with the same number of missing values clustered in a single block. Both scenarios are 

examined below. 

To quantify the error in hourly mean values calculated with M values missing, the RMS difference 

was calculated : 

where Xp is an hourly mean value with missing data, Xt is the true hourly mean value, and N is the 

number of hourly means for a given observatory at a given activity level (see Table 2). The a priori 

assumption is that E will depend both on M and Q, i.e., E = f(M,Q). The average difference was also 

calculated. 

3.1.1 Random Missing Data 

Figure 1. The RMS differences in nT between the true hourly mean values and the hourly mean values computed 

when M random minute values are missing. The data have been grouped and plotted according to activity level. 

196
A FORTRAN random number generator was used to select M points for deletion for each hour of 

the year. The point selected differed from hour to hour, and care was taken to insure that within one 

hour the same point was not chosen twice. Hourly means were recomputed from the remaining values 

in the hour and were compared to the true hourly mean value. E and the mean difference were 

computed for each value of M, where M ranged from 1to 20, for each activity level Q. 

Figure 1 shows the results for the X 

component at each observatory. The similarity 

in the four plots is striking. Except for the hours 

when Q=5 the variation with M is close to 

linear at each observatory for each activity 

level. In fact, they are almost identical up to the 

Q=4 level. This can be seen more clearly in 

Figure 2 which shows the variation with M for 

the four observatories for Q=3 and for Q=5. 

Only at Q=5 is there a significant difference 

between observatories, most notably at OTT 

which exhibits a different behavior than the 

other observatories. This may mean that the 

morphology of the magnetic field variations that 

occur during major storms is in some way 

different at Ottawa. However, it is more likely 

an indication that the sample of disturbed hours 

at Ottawa is small compared to the sample size 

at the other observatories. Table 2 shows that 

there were only 33 hours during which Q=5, a 

factor of 10 less than MEA and FCC, a factor of 

5 less than RES. 

 The data can also be plotted as a function 

of Q for a given value of M, as in Figure 3. 

Here it can be seen that even on active days 

(Q=3) with 20 data points missing, the error in 

the hourly mean does not exceed 2 nT. 

The average difference between partial 

HMVs and true HMVs was also computed. An 

example is shown in Figure 4. Except when 

Q=5 the average differences are only a fraction 

of a nanotesla. The same is true at the other 

observatories. This is not a surprising result, 

but it confirms that annual mean values can be 

safely calculated from hourly mean values even 

when a large number of data are missing. 

197
Figure 3 RMS differences plotted as a function of 

activity level Q when 5, 10, 15 and 20 data values are 

missing. 

Figure 4 Plot of the average difference between 

the true hourly mean values and the hourly means 

computed when randomly selected data are 

removed 

3.1.2 Contiguous Missing Data 

The analysis was similar to that described in the previous section except that M data were removed 

in a single block at the beginning of each hour. The recomputed HMVs were then compared with the 

true HMVs and E calculated for each value of N for each activity level (Q=1 to 5). Figure 5 show E for 

the different activity levels for MEA. It is similar to the plots shown in Figure 1 except that the RMS 

differences are much larger. As was the case when data were randomly deleted, the average difference 

was small, except when Q=5 (See Fig 6). 

3.2 Hourly Ranges 

An hourly range (HR) is simply the difference between the maximum value and minimum value of 

a magnetic field component recorded during an hour. An HR based on a partial hour must always be 

less than or equal to an HR based on a full hour. 

198
3.2.1 Random Missing Data 

RMS differences between full and partial hourly ranges (E) were calculated in a manner similar to 

that described earlier for hourly mean values (Section 3.1.1). Figure 7, shows the variation of E with the 

number of missing data for the five activity levels at FCC. 

It can be seen that error in calculating hourly range with less than 60 minute values is much larger 

that the error in the corresponding hourly mean. This holds true for all levels of activity and any 

number of missing data. 

3.2.1 Contiguous Missing Data 

Figure 8 shows the effect of removing data in blocks where the blocks range in size from 1 to 

20. In all cases the missing data were at the beginning of the hour. Again we see that the same number 

of missing data grouped in a block causes a larger error than when the missing data are distributed 

randomly throughout the hour. 

4.0 Discussion 

Several interesting and perhaps surprising results emerge from this study. One of these is the nearlinear relationship between E and M. Neither Mandea (2002), nor Schott and Linthe (2007) present their 

results in a form that can be used to confirm or falsify this observation. The paper by Herzog (2009) 

presents results that are somewhat more amenable to comparison. He finds a non-linear relationship. 

There are several reasons why our result may not agree with his. First, the methodologies are different. 

Herzog(2009) selects only a small number of intervals from which data are missing. He then builds a 

composite day from which statistics are computed. Our approach was to compute statistics from all 

data for an entire year. In my opinion, the relative paucity of data in Herzog’s study is the most likely 

cause on the variability. The fact that the only example of extreme variability shown in Figure 1 occurs 

when Q=5 at OTT supports this claim since this plot was based on only 33 hours. 

Figure 5 shows another interesting feature: E is larger when data are missing in blocks, which is 

what would be expected intuitively. The ratio of Eblock to Erandom is roughly the same at each observatory but 

it varies with M, from about 1 when M is small to about 4.5 when M is large. When M=1 there is no 

distinction between the random and contiguous cases, so one would expect the ratio to be 1. Although 

intuition might suggest that the contiguous case should have a larger error than the random case, a 

199
physical model explaining why this should be the case, and why the ratio should be so large, is not selfevident and requires further investigation. 

The fact that the relationship E = f(M,Q) varies little from observatory to observatory is potentially 

the most important finding to come out of this investigation. If this finding is corroborated, it may be 

possible to develop a universal set of guidelines on how to handle missing data. 

Comparing this study with the three other papers that discuss the problem of missing data is 

difficult because each study takes a uniquely different approach to the problem. However, one factor 

common to the three studies is the reliance on the K index as a measure of magnetic activity. In their 

discussions, both Schott and Linthe (2007) and Herzog (2009) wonder whether some other measure of 

magnetic activity might have been better. 

There are several reasons why K indices are inappropriate. First, K is a three-hour index, whereas 

the quantity of interest is a one-hour mean. Second, the actual level of magnetic activity associated with 

a given K level is different for each observatory. Therefore, even though K is the same, the actual level 

of magnetic activity may be quite different, and the response to missing data would likewise be 

different. Third, K does not measure the regular diurnal variation; it is therefore useless on quiet days 

when Sq forms the major part of the magnetic field variations. 

The use of hourly ranges reduces these problems, but it is not the perfect solution. Ranges are 

grouped into only five activity bins whose size increases as magnetic activity increases. The fifth bin is 

open ended. Although some sort of binning is necessary, the bin sizes used may not be optimum. The 

fact that the Q=5 bin is open-ended may in part account for some of the peculiarities observed for that 

activity level. 

The implications of this study for the 10% rule are clear. The rule is an oversimplification that leads 

both to the unnecessary rejection of quiet to moderately active data and the unacceptable inclusion of 

stormy data. What is necessary is to derive a value of M that depends on the level of magnetic activity 

for the hour in which data are missing. To do this, we first need to decide on the maximum permissible 

error for an HMV This is likely to be a controversial subject, but once a value has been agreed upon, it 

should be possible to determine the maximum number of missing data that can be tolerated. 

Table 3 is an example of one possible way of going about this. The table gives the maximum value 

of M for four different levels of accuracy for the five activity levels. Assuming a normal distribution, E 

is one standard deviation; the values given in Table 3 are 2-sigma, which gives a confidence level of 

95%. The table was derived from Meanook data, but given the similarity of the results from each 

observatory, any one of them could be used; an average of all four might be best. As an example of how 

to use the table, suppose it is decided that errors in the HMV of up to 5 nT can be tolerated. Then, 

according to use the table, on active days when missing data are distributed randomly, 20 missing values 

can be tolerated. When the missing data are contiguous, only 5 missing values can be tolerated. 

This procedure suffers from one major drawback. In the relationship E = f(M,Q), Q is related to 

the true hourly range. But if data are missing, the hourly range will also be affected. It was shown in 

Section 3.2 that the error in HR can be extremely large when data are missing. The analysis needs to be 

redone making E = f(M,Q ) where the activity level Q is based on partial HRs. 

200
Table 3 Maximum number of missing data permitted for desired accuracy 

Desired Accuracy (2 sigma) 

Activity Level 1 nT 2 nT 5 nT 10 nT 

1 (quiet) -contiguous 

 -random 

5 

20 

11 

20 

20 

20 

20 

20 

2 (unsettled) -contiguous 

 - random 

2 

7 

4 

19 

10 

20 

20 

20 

3 (active) -contiguous 

 -random 

1 

2 

2 

6 

5 

20 

10 

20 

4 (stormy) -contiguous 

 - random 

0 

0 

1 

1 

2 

8 

4 

20 

5 (major storm) - contiguous 

 -random 

0 

0 

0 

0 

0 

1 

1 

3 

5.0 Conclusions

The results presented in Section 3 lead to the following conclusions: 

1 The error in HMVs caused by missing data depends on the level of magnetic activity. 

2 For a given level of activity the error increases linearly with the number of missing data. 

3 The relationship between error, activity and the number of missing data is essentially the same at 

all observatories. 

4 Blocks of missing data cause larger errors than the same number of missing values distributed 

randomly over the hour. 

Acknowledgements 

I wish to thank the Canadian Hazards Information Service of the Geological Survey of Canada 

for supplying the data used in this study. 

6.0 References 

Coles, R.L. and H.L. Lam, 1998, Geomagnetic forecasting in Canada, Physics in Canada, 54, 327-331. 

Herzog, D.C., 2009, The Effects of missing data on mean hourly values, this volume. 

Jankowski, J., and C. Sucksdorff, 1996, Guide for Magnetic Measurements and Observatory Practice, 

International Association of Geomagnetism and Aeronomy, 237 pp. 

Mandea, M., 202, 60, 69, 58...how many minutes for a reliable hourly mean?, Proceedings of the Xth 

IAGA Workshop, Hermanus, 112-120. 

Schott, J.-J., and H.J. Linthe, 2007, The hourly mean computation problem revisited, XII IAGA 

Workshop on Geomagnetic Observatory Instruments, Data Acquisition and Processing, Publication 

of the Institute of Geophysics Polish Academy of Sciences, C-99 (398), 135-143. 

Wienert, K.A., 1970, Notes of Geomagnetic Observatory and Survey Practice, 217 pp. 

201
New substorm index derived from high-resolution geomagnetic field data 

at low latitude and its comparison with AE and ASY indices 

M. Nosé 1, T. Iyemori 1, M. Takeda 1, H. Toh 1, T. Ookawa 2, G. Cifuentes-Nava 3, J. Matzka 4, 

J. J. Love 5, H. McCreadie 6, M. K. Tunçer 7, and J. J. Curto 8

1

 Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Japan 

2

 Kakioka Magnetic Observatory, Japan Meteorological Agency, Japan 

3

 Instituto de Geofisica, Universidad Nacional Autonoma de Mexico, Mexico 

4

 Remote Sensing and Geomagnetism, Danish Meteorological Institute, Denmark 

5

 United States Geological Survey, United States 

6

 Geophysical Observatory, Department of Earth and Environmental Sciences, Ludwig-Maximilians University, Germany 

7

 Geomagnetism Laboratory, Kandilli Campus, Boğaziçi University, Turkey 

8

 Observatori de L'Ebre, CSIC - Universitat Ramon Llull, Spain 

High-resolution geomagnetic field data (i.e., ≤5 seconds) have recently become more commonly used by space 

physicists. The data permit the identification of Pi2 pulsations, having periods of 40-150 seconds and irregular 

waveforms. Pulsations of this type appear clearly in time series from mid- and low-latitude ground stations on the 

nightside at substorm onset. Therefore, with data from multiple observatories, substorm genesis and evolution can 

be monitored. Here we propose a new substorm index, the Wp index (Wavelet and planetary), which measures Pi2 

spectral power at low-latitude. This index is derived from geomagnetic field data obtained from observatories 

arranged in longitude around the Earth’s circumference. Presently, data from 5 ground stations (Fürstenfeldbruck, 

Iznik, Urumqi, Kakioka, and Teoloyucan) are used, but future work will include data from other sites as well 

(Honolulu, Tucson, San Juan, Tristan da Cunha, and Ebro). Here we compare substorm occurrence estimated from 

the Wp index and those from the AE and ASY indices. We show that Wp index is a good indicator of substorm 

onset. 

1. Introduction 

Substorms are one of the most distinguished phenomena 

in the geospace. Their occurrence can be identified in a 

number of different ways, including auroral breakup, 

energetic particle injection in the inner magnetosphere, 

magnetic field dipolarization, magnetic reconnection in 

the magnetotail, and plasmoid development in the distant 

tail. In terms of ground-base geomagnetic field data, 

substorms can be identified by high-latitude negative bays 

and mid-latitude positive bays. There are geomagnetic 

indices which reflect occurrence of high-latitude negative 

bays and mid-latitude positive bays; the AE index is for 

the former [Davis and Sugiura, 1966] and the ASY index 

is for the latter [Iyemori and Rao, 1996]. These indices are 

derived from multiple stations distributed in longitude 

around the globe. Data from 12 stations and 6 stations 

contribute to the AE index and the ASY index, 

respectively. 

 Pi2 pulsations are defined as geomagnetic variations 

with periods of 40-150 seconds and irregular (damped) 

waveforms [Jacobs et al., 1964]. Previous studies reported 

that Pi2 pulsations can serve as an diagnostic indicator of 

substorm onset. They appear almost simultaneously with 

auroral breakups [e.g., Gelpi et al., 1987], energetic 

particle injection in the inner magnetosphere [e.g., Saka et 

al., 1996], and magnetic field dipolarization [e.g., Yumoto 

et al., 1989]. Since low-latitude Pi2 pulsations have 

dominant power near the midnight, multiple ground 

stations distributing longitudinally are needed for their 

detection. Recently a large number of geomagnetic 

observatories have started recording geomagnetic field 

variations with 1-sec time resolution. This development 

facilitates the routine derivation of a new index measuring 

Pi2 power. In this study, using data from the longitudinal 

network of 5 ground stations, we propose a new substorm 

index, the “Wp index” (Wavelet and planetary). 

2. Longitudinal Network of Ground Stations 

 Figure 1 shows the locations of geomagnetic stations 

used to derive the Wp index. The coordinates of the 

stations are listed in Table 1. Filled red circles in Figure 1 

represent geomagnetic stations which are now under 

operation in acquisition of high time resolution of 

geomagnetic field data. Their names (3-letter abbreviation 

code, sponsoring country) are Fürstenfeldbruck (FUR, 

Germany), Iznik (IZN, Turkey), Urumqi (UMQ, China), 

Kakioka (KAK, Japan), and Teoloyucan (TEO, Mexico). 

Time resolution of data is 1 second, except for Teoloyucan 

which provides 5-second resolution data. The largest 

longitudinal separation between stations is about 120°

(between TEO and KAK/FUR), thus at least one station is 

always positioned on the nightside where low-latitude Pi2 

pulsations can be clearly observed. In the following study, 

we processed data from these 5 stations by wavelet 

analysis and derived the Wp index. Stations which might 

be included in the future are also summarized in Table 1 

and shown with open red circles in Figure 1. 

202
3. Method of Calculation of Wp Index 

3.1. Wavelet analysis 

 Wavelet analysis is similar to Fourier analysis in that a 

time series is decomposed into orthonormal basis 

functions. Here we briefly summarize wavelet analysis 

that is described by Nosé et al. [1998] in more detail. In 

Fourier analysis, harmonic functions (e

i2π ft, where f is 

frequency and t is time) are adopted as orthonormal basis 

functions. Let h(t) and H(f) be a given function in the time 

domain and its Fourier transform in the frequency domain. 

The Fourier transform equations can be expressed by 

hሺtሻൌ ׬ Hሺfሻ·ei2πftdf ∞ , ‐∞

Hሺfሻൌන hሺtሻ·ሺei2πftሻ*dt

∞

‐∞

,

Figure 1. Location of geomagnetic stations in geomagnetic coordinates

used to derive the Wp index. Dotted blue circles indicate geomagnetic

latitudes of 20° and 50°. Filled red circles represent stations measuring 

geomagnetic field variations with a high time resolution. Open red 

circles show stations from which high time resolution data will become 

soon available. 

where the asterisk denotes the complex conjugate. The 

Fourier transform is very popular in analysis of time series 

data, in particular, periodic signals; but it has a limitation 

which comes from the characteristics of the harmonic 

functions. Since the harmonic functions have finite values 

for t → ±∞, Fourier analysis is sometimes not appropriate 

to analyze phenomena localized in time. 

 In wavelet analysis, a time series is decomposed into the 

orthonormal basis functions which are localized in time 

and limited in a specific frequency range (wavelets). Thus 

wavelet analysis is a suitable method for investigating the 

wave power of phenomena which are limited in both time 

and frequency, such as Pi 2 pulsations. The time series is 

mapped to the time-frequency domain, so the wavelet 

transform has two parameters which correspond to time 

and frequen x( the wavelet 

transform is s

cy. For a time series t), 

expre sed as 

xሺtሻ ൌ෍෍α୨,୩ · ψ୨,୩ሺtሻ,

୩୨

α୨,୩ ൌ න xሺtሻ · ψ୨,୩

ሺt כ

ஶ

ିஶ

ሻ dt,

where αi,k is the wavelet coefficient and ψi,k is the discrete 

wavelet set. ψi,k co r lyzing wavelet 

߶ሺݐሻ, which gen e elet set, by 

 is nst ucted from an ana

erates th orthonormal wav

ψ୨,୩ሺtሻ ൌ Ԅ൫2୨t െ k൯,

where j and k are integers. This equation indicates that j is 

related to the dilation or contraction of ߶ሺݐሻ and k is 

related to the shift of ߶ሺݐሻ in the time domain. Thus j and 

k can be considered as parameters of frequency (dilation) 

and time (translation), respectively. A number of analyzing 

wavelets to generate the orthonormal wavelet set have 

been found. For example, the Haar wavelet, the 

(degree) 

Station Code GGLAT GGLON GMLAT1 GMLON1 Status 

San Juan SJG 18.11 293.85 28.31 6.08 Planned 

Tristan da Cunha TDC -37.25 347.50 -31.55 53.42 Planned 

Ebro EBR 40.82 0.50 43.18 81.31 Planned 

Fürstenfeldbruck FUR 48.17 11.28 48.39 94.58 Operating 

Iznik IZN 40.50 29.73 37.74 109.58 Operating 

Urumqi WMQ 43.80 87.70 34.11 162.21 Operating 

Kakioka KAK 36.23 140.19 27.37 208.71 Operating 

Honolulu HON 21.32 202.00 21.64 269.74 Planned 

Tucson TUC 32.17 249.27 39.88 316.11 Planned 

Teoloyucan TEO 19.75 260.82 28.76 330.34 Operating 

1

 Values in January 2005 

Table 1. Coordinates of ground stations used in derivation of the Wp index. GGLAT and GGLON denote geographic latitude and longitude. 

GMLAT and GMLON mean geomagnetic latitude and longitude. 

203
Daubechies wavelet [Daubechies, 1988], and the Meyer 

wavelet [Meyer, 1989] are known. In this study we used the 

Meyer wavelet, because the Meyer wavelet is 

band-limited in frequency [e.g., Sasaki et al., 1992; Sato 

and Yamada, 1994; Yamada and Ohkitani, 1991; 

Yamanaka et al., 1994; Yomogida, 1994] as same as Pi2 

pulsations (i.e., 6.67-25.0 mHz). 

 For actual analysis we use a discrete time series and take 

a finite data segment. Assuming a time series which has a 

sampling rate and a number of data points N (N = 2n

, 

n is integer), we will obtain wavelet coefficients αi,k

confined in 0 ≤ j ≤ n-1 and 0 ≤ k ≤ 2j

-1. The frequency 

band for each j is 2j

/3T ≤ f ≤ 2j+2/3T, where is the data 

length T = Ndt). Note that the widths of time window and 

frequency window covered by αi,k are T/2j

 and 2j/T, 

respectively. This indicates that the wavelet coefficient 

with a large value of j has high resolution in time and low 

resolution in frequency, and vice versa. The Nyquist 

frequency is included in the frequency range supported 

the maximum value of j. 

 by

 Figure 2 shows waveforms of the wavelet functions ψi,k

which are generated from the Meyer wavelet with 

N=1024=210. Note that the Meyer wavelet has a 

symmetric waveform. From Figures 2a-2c which give 

examples of wavelet functions with different values of 

(i.e., j=4-6), we see that the wavelet function with a 

smaller value of is more dilated than that with a larger 

value of j. Figures 2d-2f display examples of wavelet 

functions with different values of k (i.e., j=8, 16, 24). For a 

smaller value of k, the non-zero part of the wavelet 

function appears earlier in time. Therefore we can discuss 

phenomena from the viewpoint of both frequency (j) and 

time (k) at once. Even if more than one wave packets 

having an identical frequency appear at different times, 

these phenomena are identified by wavelet coefficients 

with different values of k. 

3.2. Calculation Procedure 

Using the 1-second data from the 5 stations, we calculate 

the Wp index as follows. (Since TEO data have a time 

resolution of 5 seconds, interpolation was performed to 

create 1-second data.) Schematic figure of calculation 

procedure is shown in Figure 3. 

Figure 2. Wavelet functions j,k(t) which are generated from the

Meyer wavelet with =1024 and (j, k) of (a) (4,8), (b) (5,8), (c) (6,8),

(d) (5,8), (e) (5,16), and (f) (5,24). 

 1. Wavelet analysis is applied to geomagnetic field data 

for a segment of 512 second length. The data segment is 

shifted forwards by 60 seconds and the wavelet analysis is 

repeated (Figure 3a). This process is conducted for both 

the H and D components from individual stations. 

 2. Using the obtained wavelet coefficients, we calculate 

wavelet power with time resolution of 1 minute (Figure 

3b). The frequency bands of wavelet function with j=4 and 

5 are 5.2-20.8 mHz and 10.4-41.7 mHz, respectively, 

which cover a frequency range of Pi2 pulsations 

(6.67-25.0 mHz). Thus the wavelet power ( ) is defined as 

an average of wavelet coefficients of j=4 and 5 (α4,k and

α5,k) in a given 1-minute bin. This process results in a time 

series of 1-minute wavelet power (i.e., 1440 data points/1 

day) for each component and each station. (Careful 

readers may notice that the frequency bands of wavelet 

function are derived in the case of N=1024. This is 

because we added 256 data points artificially to both ends 

of the 512-second data segment before wavelet analysis; 

the additional data are given to be equal to each end of the 

data segment. If the time series is tapered by the data 

window function as done in the Fourier analysis, the 

results become inappropriate around each end.) 

 3. Since amplitude of Pi2 pulsations is dependent on 

geomagnetic latitude [e.g., Osaki et al., 1996], the wavelet 

Figure 3. Schematic figure of calculation procedure of the Wp index.

204
power should be corrected for that (Figure 3c). First we 

compute a baseline of the wavelet power (PBaseline) on 

monthly basis. The baseline is derived by averaging the 

1-minute wavelet power for 1800-0600 magnetic local 

time (MLT) of the 5 international quietest days, that is, 

3600 data points. Then 1-minute wavelet power is 

multiplied by 5/PBaseline and rounded to the nearest integer, 

yielding time series of the corrected wavelet power in 

which the baseline becomes a constant value of 5 for all 

components and all stations. The corrected wavelet power 

is expressed as WABB-C, where ABB is the 3-letter IAGA 

code for geomagnetic observatories and C is the 

component. For example, WKAK-H stands for the corrected 

wavelet power for the H component at Kakioka. 

 4. The Wp index (Wp-H and Wp-D) is derived from 

WABB (WABB-H and WABB-D). Since Pi2 pulsations have 

dominant power on the nightside, we use only WABB from 

stations located at 1800-0600 MLT. The Wp index is 

simply defined as an average of WABB of the stations on 

the nightside (Figure 3d). 

3.3. Example of Calculation for September 28, 2005 

Following the procedure described in the previous 

subsection, we calculated the Wp index for September 28, 

2005, using data of the 5 available stations. Top 5 panels 

of Figure 4 display the corrected wavelet power in the H 

component (WABB-H) derived with Procedures 1-3 for 

each station. A horizontal green bar in each panel indicates 

local nighttime (1800-0600 MLT) of the station. During 

0100-0200 UT, IZN and FUR are on the nightside and 

enhancements of WIZN-H and WFUR-H are seen. Around 

0500 UT and 0615 UT, TEO was on the nightside and 

showed enhancements of WTEO-H, which are also seen at 

KAK though it was in the afternoon sector. Around 1400 

UT, both KAK and WMQ were located on the nightside 

and observed increases of WKAK-H and WWMQ-H. From 

1700 UT to 1715 UT, 4 of 5 stations (KAK, WMQ, IZN, 

and FUR) found large increases of the corrected wavelet 

power at nighttime. All of these enhancements are 

expected be occurrence of Pi2 pulsations, because they are 

found at local nighttime. 

 From time series of the corrected wavelet power, we 

calculate the Wp-H index with Procedure 4. Bottom panel 

of Figure 4 shows the result of calculation, that is, the 

Wp-H index. We can see significant increases of the 

Wp-H index as indicated by vertical arrows around 0045 

UT, 0130 UT, 0145 UT, 0500 UT, 0615 UT, 1330 UT, 

1400 UT, 1700 UT, and 1715 UT, which were already 

identified in the corrected wavelet power (top 5 panels of 

Figure 4). Therefore the Wp index can be thought as “a 

global index”, which reflects nighttime wave power in the 

Pi2 frequency range at any given UT time. 

4. Comparison of Wp Index with Other Substorm 

Indices 

4.1. Case study 

Figure 4. (1st to 5th panels) Corrected wavelet power in the H

component, WABB-H, for 5 available stations for September 28, 2005.

From top to bottom, WFUR-H, WIZN-H, WWMQ-H, WKAK-H, and

WTEO-H are shown. A horizontal green bar in each panel means local

nighttime (1800-0600 magnetic local time) of the station. (6th panel)

Wp-H index for September 28, 2005 calculated from WABB-H shown

in the 1st to 5th panels. Vertical arrows indicate times of clear

enhancements in the Wp index. 

Now we compare substorm signatures found in the Wp 

index and those found in the AE index and the ASY index. 

Enhancement of the Wp index is considered as occurrence 

of Pi2 pulsations at low-latitude, while increases of the AE 

and ASY indices are interpreted as occurrence of 

Figure 5. Stack plots of the Wp-H, Wp-D, AE, AL (-AL), ASY-H,

ASY-D indices for September 28, 2005. For intercomparison among

these indices, vertical dotted lines are drawn at times of the Wp

enhancements, which are the same time as vertical arrows in Figure 4. 

205
high-latitude negative bay and mid-latitude positive bay. 

Top panel of Figure 5 is the Wp-H index which is identical 

to the bottom panel of Figure 4. The Wp-D index is also 

calculated in the same way as the Wp-H index and 

displayed in the second panel of Figure 5. Both indices are 

similar to each other, except for values around 0500 UT. 

For comparison among geomagnetic indices, plots of the 

AE, AL (-AL), ASY-H, and ASY-D indices for September 

28, 2005 are also given in the third to sixth panels of 

Figure 5. 

 For the Wp increase at 0130 UT, the AE, -AL, ASY-H, 

and ASY-D indices were also increased. However, no 

corresponding changes can be found in the AE, -AL, and 

ASY indices for 0045 UT and 0145 UT. A significant 

increase was found in the Wp-H index at 0500 UT, and 

this is accompanied by increases of the AE, -AL, and 

ASY-H indices. At 0615 UT, clear substorm signatures 

appeared only in the Wp index. There are enhancements in 

the Wp and ASY indices for 1330 UT, while clear 

increases are found only in the Wp index at 1400 UT. At 

1700 UT all of indices showed enhancements, confirming 

substorm occurrence, though at 1715 UT no enhancement 

appeared in the AE, -AL, and ASY indices. Substorm 

occurrence can be also identified with energetic particle 

injections at geosynchronous altitude, thus the Wp index is 

compared with data from LANL geosynchronous satellite. 

Only a single satellite (1990-095) was available for 

September 28, 2005. Data plot is not shown here, but can 

be browsed from http://leadbelly.lanl.gov/lanl_ep_data. 

For the first 7 increases of Wp (0045 UT, 0130 UT, 0145 

UT, 0500 UT, 0615 UT, 1330 UT, and 1400 UT), energetic 

electron flux showed clear enhancements. There was an 

electron flux increase around 1700 UT, but it is not clear 

which of the 1700 UT or 1715 UT enhancements of the 

Wp index is associated with the flux enhancement. This is 

because the 1990-095 satellite was on the dayside and the 

flux increase showed energy dispersion. 

 Table 2 summarizes the comparison of the Wp index 

with other substorm signatures. Three of 9 Wp index 

increases (at 0130 UT, 0500UT, and 1700 UT) are 

associated with substorm signatures of the AE, AL, and 

ASY indices. This may be caused by insensitivity of the 

AE, AL, and ASY indices to small substorm onsets or 

pseudo-substorm onsets, while the Wp index, that is, 

low-latitude Pi2 pulsation, is sensitive to such substorm 

onsets [e.g., Sakurai and Saito, 1976]. Moreover, a 

number of stations used to derive the AE and AL indices 

are 9-10 for 0000-2100 UT on September 28, 2005 with 

lack of stations in Russian territory, suggesting that 

substorms cannot be detected with the AE and AL indices 

when Russian stations are near midnight (approximately 

1300-2100 UT) [e.g., Takahashi et al., 2004]. Thus we 

think that the Wp index is still a useful index to detect 

substorm onset, and this idea is supported by a fact that 8 

substorm onsets identified by the Wp index are 

accompanied by energetic electron flux enhancements at 

geosynchronous orbit. 

Wp AE AL ASY-H ASY-D LANL 

Substorm 

Signature 

Low-latitude 

Pi2 

High-latitude Negative Bay Mid-latitude Positive Bay Particle

Injection 

0045 UT Yes No No No No Yes 

0130 UT Yes Yes Yes Yes Yes Yes 

0145 UT Yes No No No No Yes 

0500 UT Yes Yes Yes Yes No Yes 

0615 UT Yes No No No No Yes 

1330 UT Yes No No Yes Yes Yes 

1400 UT Yes No No No No Yes 

1700 UT Yes Yes Yes Yes Yes Yes* 

1715 UT Yes No No No No Yes* 

Table 2. Summary of comparison between the Wp index and other substorm indices/satellite data for September 28, 2005. Detail description is 

found in Section 4.1. “Yes*” means that the particle injection corresponds to either of the two Wp enhancements. 

4.2. Statistical study 

We performed a correlation analysis between the Wp-H 

index and other substorm indices. Results for 1 month 

interval of September 2005 are shown in Figure 6. Each of 

4 panels shows relation between the Wp-H index and the 

AE/-AL/ASY-H/ ASY-D index. Small dots represent 

1-min data points and large circles are average values in 

10-nT bins of the Wp-H index. Correlation coefficients 

Figure 6. Relation between the Wp-H index and the

AE/-AL/ASY-H/ASY-D index for September 2005. Small dots are

1-min data points and large circles are average values in 10-nT bins

of the Wp-H index. Correlation coefficients for both the all data

points (small dots) and the binned-average values (large circles) are

shown in upper right corner of each panel. 

206
were computed for both the all data points (small dots) 

and the binned-average values (large circles), and the 

results were indicated in upper right corner of each panel 

of Figure 6. For all data points correlation coefficients are 

0.36-0.40, while for binned-average data they are as high 

as 0.80-0.92. This suggests that the Wp index is suitable to 

monitor substorm occurrence in addition to the AE and 

ASY indices. 

5. Database of Wp index 

Stack plots of the Wp, AE, and ASY indices in the same 

format as Figure 5 can be obtained from 

http://s-cubed.info. Such plots are useful for users to 

identify substorm onset from three different kinds of view 

points, that is, low-latitude Pi2 pulsation, high-latitude 

negative bay, and mid-latitude positive bay. 

6. Summary 

 Using geomagnetic field data with high time resolution 

from 5 low-latitude stations (i.e., Fürstenfeldbruck, Iznik, 

Urumqi, Kakioka, and Teoloyucan), we proposed a new 

substorm index, the Wp index, which is related to wave 

power of low-latitude Pi2 pulsations. In derivation of the 

Wp index, we adopted wavelet analysis which is a suitable 

method for investigating the power of short-lived waves, 

such as Pi 2 pulsations. The above 5 stations are 

distributed over the globe in longitudinal direction with 

maximum separation between neighboring stations of 

~120°. Thus at least one station is located on the nightside 

where Pi2 pulsations have dominant power. Since we used 

only geomagnetic stations on the nightside, the Wp index 

can be considered as “a global index”, which means an 

index reflecting Pi2 wave power during local nighttime at 

any given UT time. 

 We made a comparison of substorm occurrence 

estimated from the Wp index and those from the AE and 

ASY indices, in terms of a case study and a statistical 

study. The Wp index appeared to be a good indicator for 

substorm onset along with the AE and ASY indices. 

 Plots of the Wp index are available from the WWW 

page (http://s-cubed.info). The plots are supplied with the 

AE and ASY indices, thus it would be easier for users to 

identify substorm occurrence. In future we will also use 

high time resolution data from Honolulu, Tucson, San 

Juan, Tristan da Cunha, and Ebro. This will increase a 

number of stations in the longitudinal network to be 10, 

and result in improvement of the Wp index. 

Acknowledgments. This work was supported by The Kurata 

Memorial Hitachi Science and Technology Foundation (grant 

844), The Japan Securities Scholarship Foundation (grant 1368), 

Inamori Foundation, and The Minitstry of Education, Science, 

Sports, and Culture, Grant-in-Aid for Young Scientists (B) (grant 

19740303). Kakioka Magnetic Observatory, China Earthquake 

Administration, Universidad Nacional Autonoma de Mexico, 

Danish Meteorological Institute, United States Geological 

Survey, Ludwig-Maximilians University, Boğaziçi University, 

and Observatori de L'ebre are acknowledged for their continued 

support of the observatories. 

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511-518, 1994. 

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transform, Geophys. J. Int., 116, 119-130, 1994. 

Yumoto, K., K .Takahashi, T. Saito, F. W. Menk, B. J. Fraser, T. A. 

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207
Models of the geomagnetic field on the territory 

of the Republic of Macedonia 

S. Panovska(1), T. Delipetrov(2), M. Delipetrov(2), B. Delipetrov(1)

(1)Faculty of Computer Sciences, University Goce Delcev, Stip, Macedonia 

(2)Faculty of Mining, Geology and Polytechnics, University Goce Delcev, Stip, 

Macedonia 

Abstract 

The aim of this paper is to present various geomagnetic field models of Macedonia 

based on measurements at repeat stations as well as satellite data covering this region. At 

first a theoretical basis of the geomagnetic field, spherical cap harmonical model and 

polynomial model will be given. Аttention will be paid to input data used in the 

modelling process, in particular the data reduction techniques using neighbouring 

geomagnetic observatories as well as criteria for data selection of Oersted, CHAMP and 

SAC-C satellite missions. The spherical cap harmonical analysis model was developed 

over the Balkan Peninsula with the Republic of Macedonia as central position for a 

spherical cap of 8o. The polynomial model on the other hand was based on ground-based 

data at 15 repeat stations reduced to sea level for epochs 2003.5 and 2004.5. This enabled 

geomagnetic field maps based on model calculations to be obtained for the country. A 

comparative analysis of these two models showed a satisfied degree of correlation, with 

the polynomial model more suitable for the territory of the Republic of Macedonia. 

Key words: geomagnetic model, spherical harmonic analyses, normal field, repeat 

stations, IGRF, geomagnetic maps 

Geomagnetic potential 

The equation for the magnetic potential at a particular location is: 

 ∑

∝

=

+

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ ⎟ + ⎠⎞ ⎜

⎝

⎛ = 1n

i

n

1n

e

n

n

T

r

a T

a

r aV 

a – average radius of the Earth 

r - distance to the point 

where each function Тn with an index “i“ for internal and “е“ for external source, is 

represented as a product of two angle-dependent functions, expressing a dependence on 

latitude and longitude. The next equation represents a spherical harmonical function 

 Тn(θ, λ) : ∑( ) =

= +

n

0m

m

n

m

n

m

n n λ θλ )(PmsinhmcosgT

where and are the expansion coefficients of the magnetic potential, called Gauss 

coefficients [4]. 

m gnm hn

In the present data analysis we used vector component values of the geomagnetic 

field, requiring spatial derivatives of the potential V to represent each component. 

 

Тerrestrial measurements 

After the disintegration of Yugoslavia, Macedonia was without a geomagnetic 

observatory and a network of repeat stations. As data from Yugoslavia as well as 

208
information for the previous repeat stations were not available initial geomagnetic 

measurements were done by Rasson and Delipetrov [2]. The measurements were carried 

out to obtain the best location for a future geomagnetic observatory in the Republic of 

Macedonia. These measurements were done in 2002 on Mt. Galicica, Mt. Plackovica and 

Ponikva. In 2003 a network of 15 repeat stations over Macedonia, was established, 

followed by measurements in 2004 [6]. 

Satellite measurements of geomagnetic field 

For the SCHA model of the Balkan Peninsula with central position the Republic of 

Macedonia, data from the satellite missions Oersted (launch February 1999), CHAMP 

(launch July 2000) and SAC – C (launch November, 2000) were used. This was the same 

dataset that was used to derive the CHAOS (CHAMP, Oersted & SAC-C) model of the 

Earth’s magnetic field [5]. All these satellites employ the same instrumentation and 

perform observations of magnetic field from the space with unprecedented accuracy. 

Because of the different altitude (Table 1) and different local time of observations, 

internal and external magnetic field sources are differently observed by the various 

satellites. 

 

Тable 1. Parameters of satellite missions 

Satellite Inclination Altitude range Launch Instruments 

Ørsted 96.5о 630 – 860 km 02.1999 

CSC flux-gate magnetometer, Overhauser 

magnetometer, Star imager (SIM), GPS 

Turbo-Rogue, Detector of particles 

CHAMP 87о 350 – 450 km 07.2000 

Overhauser magnetometer, 

Acceleratormeter, GPS receiver, Star 

sensor, Laser reflector 

SAC-C 98.2о 700 km 11.2000 Scalar helium magnetometer, GPS TurboRogue 

Selection of satellite data 

In this investigation we used the same satellite data as selected for deriving the 

CHAOS model from the following periods (Nils Olsen and oth. CHAOS): 

- Oersted scalar and vector data between March 2000 and December 2005, 

- CHAMP vector and scalar data between August 2000 and December 2005, 

- SAC-C scalar data between January 2001 and December 2004. 

All  data  are  selected  according  to  quiet  geomagnetic  conditions  as  defined  by  the 

following criteria: for all latitudes the Dst‐index does not change by more than 2 nT/h 

(dDst/dt). At non‐polar latitudes (equatorwards of 60o dipole latitude) Kp ≤ 2o has to 

be  fulfilled.  (Kp ≤  2o  corresponds  to  a  variation  (peak‐to‐peak)  range  of    ≤  7  nT). 

Only  data  from  dark  regions  (sun  10o  below  horizon)  were  used,  to  reduce 

contributions  from  ionospheric  currents.  Vector  data  have  been  taken  for  dipole 

latitudes  equatorwards  of  ±60o,  to  avoid  the  disturbing  effect  of  field‐aligned 

currents, that only influence the vector components but not the total field intensity. 

Only  non‐polar  CHAMP  data  obtained  after  local  midnight  are  used,  to  avoid  the 

influence of the diamagnetic effect of dense plasmas. Due to their higher altitudes, a 

209
corresponding rejection of pre‐midnight data is  ot necessary  f n or Ørsted and SAC‐

C.) 

The  data  sampling  interval  is  60s;  weights  proportional  to  sin  θ (where  θ is 

geographic  colatitude)  are  applied  to  simulate  an  equal  area  distribution. 

Geomagnetic  field  modeling  requires  vector  data  that  are  both  calibrated  and 

aligned. Data calibration,  the conversion of  the raw vector magnetometer readings 

into scaled magnetic field components (in units of nT) in the orthogonal coordinate 

system  of  the  sensor,  is  done  by  comparing  the  output  of  the  Vector  Fluxgate 

Magnetometer (VFM)  with  the  magnetic  field  intensity  measurements  obtained 

simultaneously  with  an  absolute  scalar  Overhauser  magnetometer.  Thus  the 

calibration  is  performed  for  each  satellite  separately  (Olsen  et  al.  2003).  Merging 

these vector data with attitude data and transforming  them  to (Br, Bθ , Bφ) (i.e.  the 

upward,  northward  and  eastward  component)  requires,  however,  one  additional 

calibration  step,  called  data  alignment,  which  is  the  precise  determination  of  the 

transfer  angles  (Euler  angles)  between  the  star  imager  and  the  vector 

magnetometer.  This  requires  models  of  the  star  constellation,  and  of  the  ambient 

magnetic  field.  The  former  model  is  known  with  high  precision  (e.g.  Hipparcos 

catalogue, ESA, 1997). The limiting factor for determining alignment is the accuracy 

of  the  ambient  magnetic  field  to  be  known  at  the  time  and  position  of  each  data 

point.  

When modelling wave lengths of 1000 km or more, high density satellite data are not 

necessary on the surface over which the model is determined. Density (datum on the unit 

surface) is in general not uniform and varies with the longitude and the latitude. This 

density can further be reduced to the required level and simultaneously be uniform, using 

the process of decimation with interval: 

 ( ) eccosfds (θλΔ ) 2 =

where θ and λ are co latitude and longitude, respectively, f (λ) is the distribution of 

data with respect to longitude before the decimation, 2 Δ is the needed average surface 

distance between the points after the decimation. Correction f (λ) make the uniform 

distribution with respect to the longitude, and correction cos ec( ) θ transform the 

distribution with respect to the latitude from uniform to sin(θ ) distribution. Then the 

surface element dθ λ sin/d ( ) θ will subsequently have the necessary uniform or constant 

distribution. Figures (1 – 3) show the spatial distribution of repeat stations, scalar and 

vector satellite data respectively, while and table 2 displays the number of data points. 

Таble 2. Number of data points used in SCHA model 

Data for model Ground data Satellite data

SUM 132 Scalar Vector 

40 129 

210
Fig. 1 – Location of the repeat 

stations in R. Macedonia used for 

making SCHA model 

Fig. 2 –Location of the scalar 

(▲) satellite data used in 

SCHA 

Fig. 3 - Location of the vector 

(♦) satellite data used in 

SCHA 

SCHA geomagnetic field model of the Balkan Peninsula 

SCHA (Spherical Cap Harmonical Analysis) model of the geomagnetic field over 

the Balkan Peninsula with special reference to the Republic of Macedonia was derived 

using the three – component vector measurements obtained from the network of the 

repeat stations in the Republic of Macedonia from 2002 to 2004, as well as the selected 

data from satellite measurements such as Oersted, CHAMP & SAC – C (Table 3). This 

model was derived in INGV, Rome, Italy [3]. 

This model allows X, Y, Z and F values of the main magnetic field to be obtained for 

the epochs 2000.0 to 2007.0. The model uses a half cap angle of 8о and may be employed 

as a reference model for reduction of magnetic field survey data during the period of 

validity of the model. 

 The small territory of the Republic of Macedonia, necessitates the use of half cap 

angle of 8o

 in order to obtain the statistically most important harmonics (the smallest 

degree is close to 12 with maximal spatial index K = 2). Coefficients have been obtained 

applying the least squares principle. The final model has total of 27 coefficients (Table 

3): 

Тable 3. Coefficients of SCHA model 

k m nk(m) m

0,k g m0,k h m1,k g m1,k h m2,k g m2,k h

0 0 0.0000 - 93.083 - 73.851 200.723 

1 0 16.7209 23.744 0.175 - 86.088 

1 1 12.7139 - 30.596 3.340 23.400 - 0.108 15.023 -83.754 

2 0 26.9471 - 11.992 - 1.739 51.014 

2 1 26.9471 13.068 - 0.041 - 11.642 - 7.065 1.250 45.500 

2 2 21.4163 - 5.648 -1.261 27.941 18.470 - 105.194 - 48.877 

Таble 4. RMS values of SCHA and IGRF models (nT) 

Model RMS X RMS Y RMS Z RMS F 

IGRF 

(ground) 57.1 82.7 77.1 71.9 

SCHA 

(ground) 49.9 74.3 74.0 69.9 

211
Table 4 shows the RMS values when comparing SCHA and IGRF models (in nT) for 

Macedonia and the surrounding area with repeat station data and satellite observations. 

After evaluation and testing, parameters which define the best model when fitting the 

input data and their spatial and time behaviour are К = 2, L = 2, covering the period 

between 2000.0 and 2007.0. The reference epoch is 2003.5. The coefficients were 

calculated with codes written in FORTRAN. 

• Parameters of the model: K = 2 , L = 2, o o =θ 8

• 2K;2L,1K;2L,0K 2L, 00 === 11 = = 22 =

• Wave length of the field is in range: 1484 km – 3146 km

Maps of X, Y, Z and F component of the SCHA model in nT for the epoch 2003.5 at 

sea level are given in Figure 4. 

Fig. 4 – Maps of X (upper left), Y (upper right), Z (down left) and F (down right) components of the 

SCHA model in nT for the epoch 2003.5 at sea level 

212
Polynomial analysis of the geomagnetic field on the territory of the Republic of 

Macedonia 

The different components of the geomagnetic field are presented with a second order 

polynomial. The geomagnetic field over Macedonia [1] is calculated using 

measurements of total intensity (F), declination (D) and inclination (I) in 2003 and 2004 

from 15 repeat stations (fig.5). The coefficients are in unit nT and degree for declination 

and inclination respectively. The coefficients for all components of the geomagnetic field 

for epoch 2003.5 on sea level are presented in Table 5: 

Таble 5. Coefficients of normal magnetic field for epoch 2003.5 on the territory of the Republic of 

Macedonia at sea level 

Element 

2003.5 a1 a2 a3 a4 a5 a6

F 46565.19931 360.63040 88.63350 91.70045 34.32665 35.90337 

D 3.208242 0.027133 0.090219 -0.259889 -0.065314 -0.096687

I 58.298752 1.052109 0.076513 0.252788 0.032474 -0.061213

H 24469.90417 -539.60365 -6.61295 138.77884 -4.63379 58.59862 

X 24431.35649 -539.48468 -8.67530 -132.38872 -2.99993 60.70491 

Y 1370.029165 -17.10737 38.06225 -119.54230 -29.11334 -38.39349

Z 39617.69585 756.00120 108.22834 181.50537 43.05518 5.174464 

Fig. 5 – Maps of X (up left), Y (up right), T (down left) and I (down right) components of the 

normal field for the epoch 2003.5 at sea level (Second order polynomial) 

213
Evaluation of the models 

A statistical analysis of differences (Table 6) between the three models, IGRF, 

SCHA and polynomial model and measurements (sign “m”), from 15 repeat stations of 

Republic of Macedonia is made to evaluate which model will best represent the 

geomagnetic field over this region. After comparing standard error, variance and standard 

deviation, the second degree polynomial model based on repeat stations data gave the 

best results. Table 7 presented the following parameters: 

∑=

=

N

1i

i x

N

1

x N

S E

σ = ∑=

= −

N

1i

2

i

2 )xx( N

1

σ ∑== −

N

1i

2

i )xx( N

1

σ 

where x is average value of sample, - standard error, - variance and E S 2 σ σ - standard 

deviation. 

Таble. 6 Differences between IGRF, SCHA and polynomial model AND measured data of F, I and D 

for epoch 2003.5 

Тotal intensity F (nT) Inclination I (o) Declination D (o) 

m-igrf m-scha m-poly m-igrf m-scha m-poly m-igrf m-scha m-poly

-94.2 -125.3 -127.1 0.164 0.103 0.063 -0.345 -0.239 -0.171 

73.6 66.5 33.7 -0.026 -0.061 -0.054 -0.187 -0.029 0.039 

38.4 62.0 42.6 0.061 0.040 -0.009 -0.077 -0.005 -0.016 

-29.0 3.9 -58.3 0.056 0.020 -0.026 -0.127 -0.080 -0.019 

-138.4 -130.6 -100.7 -0.112 -0.140 -0.101 0.341 0.439 0.339 

141.3 110.5 72.1 0.148 0.085 0.013 -0.063 0.069 0.224 

1.0 -20.7 -13.3 0.112 0.039 0.057 -0.053 -0.027 -0.025 

-10.7 15.1 12.3 0.078 0.052 0.047 -0.223 -0.087 -0.157 

-63.5 -78.4 -51.8 -0.040 -0.094 -0.033 -0.121 0.000 -0.043 

31.7 8.4 13.6 0.079 0.012 0.048 -0.490 -0.357 -0.332 

118.8 107.9 151.4 0.094 0.085 0.105 -0.100 -0.023 -0.110 

-19.5 -14.3 -19.4 -0.007 -0.053 -0.006 0.032 0.183 0.156 

-23.7 -29.0 -8.4 0.052 0.019 0.043 -0.013 0.045 -0.016 

46.4 24.4 36.1 -0.014 -0.059 -0.096 0.000 0.049 0.046 

16.4 -11.0 17.0 0.002 -0.055 -0.053 0.037 0.105 0.086 

Table. 7 Statistical analysis of differences between IGRF, SCHA and polynomial model AND 

measured data of F, D, I for epoch 2003.5 

2003.5 MIN MAX Average 

value 

Standard 

error 

Variance Standard 

deviation 

F 

M-IGRF -138.4 141.3 5.9 19.3 5559.5 74.6 

M-SCHA -130.6 110.5 -0.7 18.7 5251.3 72.5 

M-POLY -127.1 151.4 6.3 17.7 4712.7 68.6 

D 

M-IGRF -0.490 0.341 -0.092 0.048 0.035 0.186 

M-SCHA -0.357 0.439 0.003 0.046 0.032 0.178 

M-POLY -0.332 0.339 0.000 0.042 0.027 0.165 

I 

M-IGRF -0.112 0.164 0.043 0.019 0.006 0.075 

M-SCHA -0.140 0.103 0.000 0.019 0.005 0.072 

M-POLY -0.101 0.105 0.000 0.016 0.004 0.061 

214
Conclusions 

Mathematical modeling of Earth’s magnetic field provides an effective means of 

calculating of the different components of the geomagnetic field as a function of space 

and time. The current regional SCHA model is based on an expansion of the magnetic 

potential in terms of spherical harmonics with a half cap angle of 8о

. This model can be 

employed as a reference model for the reduction of magnetic field data and fits the 

measured X, Y, Z and F geomagnetic field components better than the spherical 

harmonic IGRF model by about 10%. However if we compare the SCHA and leastsquares polynomial models, the latter provides the best representative model for the 

Republic of Macedonia. It is envisaged to derive an improved model based on new repeat 

station field survey data in 2007. 

References 

1. Delipetrov T., Basics of geophysics, Faculty of Mining, Geology and Polytechnics, 

Stip, 2003 

2. Delipetrov M., Delipetrov T., Delipetrov B., Panovska S., Geomagnetic Data for the 

Republic of Macedonia Obtained during the Measurements Carried out in 2004, XIIth

IAGA Workshop on geomagnetic observatory instruments, data acquisition and 

processing, Belsk, Poland, June, 2006 

3. De Santis A., J. Miquel Torta, L. R.Gaya-Pique, The First Antarctic Geomagnetic 

Reference Model (ARM), Geophysical Research Letters, Vol 29, No 8, 10.1029/2002 

GL014675, 2002. 

4. Haines, G. V., Spherical Cap Harmonic Analysis, J. Geophys. Res., 90, 2583–2592, 

1985. 

5. Olsen N., Luhr H., Sabaka J. T., Mandea M., Rother M., Tøffner-Clausen L., Choi S., 

CHAOS – A Model of Earth’s Magnetic Field Derived from CHAMP, Ørsted, and 

SAC-C Magnetic Satellite Data, Geophys. J. Int. (2000) 142. 

6. Rasson J. L. and Delipetrov M., Magnetic Repeat Station Network Description, 2004. 

215
A Quasi Absolute Optically Pumped Magnetometer for the Permanent 

Recording of the Earth’s Magnetic Field Vector 

E. Pulz, K.-H. Jäckel, O. Bronkalla 

GeoForschungsZentrum Potsdam, D-14473 Potsdam, Telegrafenberg, Germany, 

Contact E-mail: epulz@gfz-potsdam.de 

Abstract 

Despite the advance of technology, the fully automatic recording of absolute magnetic field variation at

observatories remains an elusive goal. Primary difficulties are the long term stability of sensor orientation, and the

stable operation of the sensor system. In standard practice, definitive data are produced through the combination

of continuous operation of a variometer and the occasional absolute measurements that are used for calibration of

the variometer data. A single, automatic instrument that can continuously acquire absolute vector measurements

with 1-second resolution is desired. We introduce a device based on a Cs, Cs-He tandem magnetometer that will 

fulfil these requirements. Data are acquired using Serson’s method: the ambient magnetic field is modulated by

superposed fields. This method has been applied, mostly in connection with Proton magnetometers, for many

years. In general it requires that the applied fields have a strength on the order of the Earth’s magnetic field. But

the sampling rate is limited for most existing systems. In contrast, our system only requires applied fields of about

5000nT and the switching rate of polarities is 5Hz. This is possible because we use a fast self oscillating

Cs-magnetometer. The self oscillating Cs-magnetometer is calibrated by a Cs-He cell during times without

additional fields (sample & hold). A laser will be used for precise orientation of coils. The absolute accuracy of

the magnetometer is 0.1nT. The sequence of measurements is computer controlled in real-time. 

 Fig. 1 Cs-He magnetometer 

1. Introduction 

DIDD magnetometers have been in use since several years. The aim of this method was to render manually

performed absolute measurements unnecessary or reduce their number. The introduction of the Overhauser Effect

Proton magnetometer for this purpose was a great success. Thereby the bias field could be reduced and the speed

of the measurement sequence could be increased. A further improvement was obtained by the introduction of

suspended coils [1]. However, still only the recordings of total intensity are absolute. The instability of the coil

orientation remains a problem. 

We developed a similar instrument using a He-Cs magnetometer [2,3] and an optically controlled orientation

of the coil system. Here we benefit from the experiences which we have collected during the development of

GAUSS [4]. The instrument is not yet completely finished but the results obtained so far are promising. 

1. The magnetometer 

A Cs-He magnetometer [3] was selected for this 

purpose. The theoretical accuracy of this instrument is about

0.1nT. We have compared our Cs-He magnetometer with our

potassium magnetometers for more than 10 years and we

found that the actual accuracy is even better. The lifetime of a

K-lamp is about 2 to 3 years. The Cs-lamp, which is needed to

run a Cs-He magnetometer, has about 10 years lifetime. 

Apart from the accuracy, two qualities are very

important to reach the target aim: First the noise and second

the resolution of the readings and time. 

In a normal Cs-He magnetometer the response of

magnetic field jumps is delayed. Therefore a new instrument

was developed using the tandem principle (Fig. 1). We made

use of our experiences which we have gained from developing

and manufacturing the Potassium-tandem magnetometers in

the nineties [5]. The self oscillating Cs-magnetometer has a

very fast response, and we had to take care that the noise level

is very low. The magnetic field signal T of the magnetometer

is a frequency f with a strictly linear dependence and contains

only atom physical constants: 

T [nT]= f [Hz] /28.0236888. 

216
2. The measuring method 

In Serson’s method [6, 7], an additional field is added to the component to be measured. The coil axis is

aligned with the component. Three measurements are necessary to determine the field strength in the direction of

coil axes: one without bias fields and two in both polarities. 

The aim is to produce a complete set of readings every second. So, a set of two coils is needed to obtain the

full vector, with two measurements in each direction of coil axis and one measurement without bias fields. That

means 5 measurements per second are needed. So, there are 200ms for each reading. Additionally one must take

into account that it takes time to read out the frequency counter, and the time which the coil current takes to 

overcome the mutual induction. The frequency measurement time per one reading is now 180ms. This time

period is a multiple of 50Hz, so that we always have a full period of our European main power at each reading. It

acts like a filter. The sequence of operation is shown in fig. 2. 

Fig. 2 Measuring sequence for each Second 

The centre lines of both coils for generating the additional magnetic fields are perpendicular to each other

and lie in the horizontal plane (opposite to a DIDD). The system has to be levelled in the horizontal plane and one

axis should be oriented to the true north direction (to measure the magnetic X-component). Both coils are 

connected in series. The advantages of this orientation and connection are the following: 

- only half the current is necessary 

- all 5 readings per second contribute a part to each component 

- the noise level has near the same size in each direction 

- the perpendicular error is eliminated 

All measuring sequences are controlled in real-time by a PC. The coil currents are switched by electronic 

transfer switches. The whole arrangement is shown in fig. 3. We compute the generated bias fields from the

measured frequencies. So, the knowledge of current and coil scale factors are not needed. 

beam similar to the one used for GAUSS [4]. The instrument will never be completely absolute but the time interval 

between manually performed absolute measurements can be increased significantly. 

3. Theoretical estimations and limits 

The required additional field strength depends only on the noise of the arrangement. The noise has different

sources: First, the magnetometer noise and the noise of frequency reference; second, the noise of the bias fields; and

third, the artificial noise at the observatory site. We found that the total noise is less than 5 pT rms by 180ms reading

time (see Fig.5). Based on this result we estimated that the additional field has to be in the order of 5000nT [6]. We

are getting under these prerequisites the same noise of 5pT rms for the magnetic components. Currently a

provisional coil system is in use. It can only generate a magnetic field strength in the order of 3500nT because the

electronic transfer switches have a limited current. A new coil system, which is under construction now, will

improve the results. 

Up to now this instrument was a variometer.

The remaining task to get quasi-absolute

measurements is a continuous control of the exact

orientation. The theoretical estimation of the tilt

errors of the coil system gave the following

results: A deviation of +/- 1’ from the horizontal 

plane leads to an error in the X component of +/-

13nT. A deviation from +/1’ of the azimuth angle 

gives an error in the X component of +/-1E-03nT. 

The coil system (not yet finished) will be 

suspended and will be turnable. The levelling can

be carried out magnetically by the turnable

suspension of the coil system which is very

precise. The suspension should hold the coil

system permanently in position [1]. However, the

direction shall be controlled by means of a LASER

Fig. 3 Measurement arrangement 

217
 

4. Results 

The figures 4 to 8 are shown some actual results from the current setup of the instrument. Fig. 4 is shown a

comparison of second mean scalar values of the NGK K- tandem magnetometers to the Cs-He magnetometer at 

which the reading time is only 180ms. It can be seen clearly that all magnetometers work excellently. A comparison

between two successive readings while the bias magnetic field was switched off respectively was constant, were

used for the estimation of noise (Fig. 5). The next figures 6, 7, 8 show the computed components H, D and Z in 

comparison to the observatory FGE variometer. A day

with strong variation parts was selected deliberately 

(20:00h). During times of high activity some small 

outliers can be seen in the comparison of the 

H-component. The reason could more be the distance 

(about 50m) between the magnetometers than the different

kind of sampling. The sampling rate of the observatory 

FGE is two times per second and the OPC gives a one 

second mean value. As already mentioned, bias fields of

only 3500nT were used. Nevertheless it is obvious that the 

noise of components is similar to the FGE which is the 

standard observatory instrument. The component

comparisons show slight drifts because the used 

provisional coil was not aligned precisely. 

Fig. 4 Total intensity comparison at 2008-09-07 

Fig. 5 Comparison of two successive readings without bias 

fields 

Fig. 6 H-component comparison at 2008-09-14 

218
Fig. 7 D-component comparison at 2008-09-17 Fig. 8 Z-component comparison at 2008-09-14

Conclusion 

We developed and built a very fast and low noise Cs-He-Cs tandem magnetometer with an electronic 

transfer switchgear. A software was written to control both the coils currents and the frequency counter in

real-time, and to transfer the data to the host computer. One second values of the full magnetic field vector are

recorded by this instrument. 

Promising first results were obtained with a preliminary coil system. An improved coil system which will

reduce the noise is currently being manufactured in our workshop. We will take advantage of our experiences

with GAUSS regarding the orientation control in order to tackle this remaining problem. When finished, we will

have a high-accuracy magnetometer with excellent long-term stability, so that the number of manual absolute 

measurements can be reduced significantly. Consequently, one instrument at one place will provide the complete

field information which up to now has to be obtained from three different instruments. 

References: 

 [1] Hegymegy, L. 

 Observatory Instruments: Past, Present and Future’? 

 IAGA Conference (Division V), 2005, Toulouse, France 

[2] Blinov, E. V., Ginzburg, B. L., Zhitnikov, R. A., Kuleshov, P. P. 

 Rubidium- Helium quantum magnetometer

 Sov. Phys. Tech. Phys.* 29 *(12), December 1984 0038-5662/84/12 1362-05 $03.40 

 Copyright 1984, American Institute of Physics 

[3] Kuleshov, P. P., Blinov, E. V., Shilov, A. E., 

 Caesium-Helium Magnetometer with HFPH for High Accuracy Magnetic Measurements, 

 Proceedings of the VIth 

IAGA Workshop on Geomagnetic Observatories Instruments, 

 Data Acquisition and Processing, Dourbes (Belgium), September 1994 

[4] Hemshorn, A., Pulz, E. 

 GAUSS: Improvements to the Geomagnetic AUtomated SyStem 

 Proceedings of the XIIIth 

IAGA Workshop on Geomagnetic Observatories Instruments, 

 Data Acquisition and Processing, Boulder/Golden, USA, 2008 

[5] Pulz, E., Jäckel, K.-H., Linthe, H-J., 

 A New Optically Pumped Tandem Magnetometer: Principles and experiences, 

 Meas. Sci. Technol. 10 (1999) 1025-1031 

[6] Serson, P. H., 

 Method of Making an Electromagnetic measurement, 

 Ottawa, Ontario, Canada, Canadian Patent No. 654, 552, Issued Dec., CLASS 324-1, 1962 

[7] Jankowski, J. – Sucksdorff, C., 

 IAGA Guide for Magnetic Measurements and Observatory Practice, 

 ISBN: 0-9650686-2-5, 1996

 

219
 

Automatic DIflux Measurements with AUTODIF

by Jean Rasson, Sébastien van Loo* and Nabil Berrami 

Institut Royal Météorologique de Belgique Centre de 

Physique du Globe, Dourbes B-5670 Viroinval Belgium 

*ThalesAlenia Space, Marcinelle, Belgium 

Abstract 

We present here an instrument able to perform automatic absolute measurements of the orientation of the geomagnetic

vector: the AUODIF. After a short presentation of its principles of measurement and its mechanical, optical and

electronical concepts, we detail, discuss and critically inspect the measurements made at the Boulder/Golden workshop. 

Automatic absolute measurements: rationale 

In a modern geomagnetic observatory, absolute measurements of declination, inclination and total field are nowadays

manually performed with the DIflux – Proton Precession Magnetometer pair. It is this widely adopted method to measure the

geomagnetic vector strength in SI units with a proton magnetometer and its orientation or attitude in space with the DIflux

theodolite which has become a de facto standard (Kring Lauridsen 1985). Whereas the measurement of the strength in an

automatic way is routinely done nowadays, the task of doing so for the vector's orientation is much more difficult and has not 

been achieved till now in everyday observatory operation, except for a few experimental realizations (Rasson1996, Van Loo

& Rasson 2006, Hemshorn 2006). 

Figure 1. An early version of AUTODIF showing the different sensors and actuators of the automatic theodolite

The success of the DIflux procedure is due to the clever combination of a theodolite (a very precise 3D attitude meter)

and a fluxgate magnetometer (highly sensitive to the orientation of the field). A robust self-correcting measurement protocol

will compensate for almost all theodolite mechanical defects and misalignments of the optical and magnetic axis of the

telescope and fluxgate respectively. Even the magnetisation of the fluxgate element will be eliminated. Therefore in order to

realize the task of unattended measurements of the orientation of the geomagnetic vector in space, it is logical to automate

the DIflux measurement protocol.

220
Figure 2. This shows the AUTODIF fluxgate sensor 

and laser/photocell combination 

Description of the instrument 

Mechanical and optical aspects 

The construction of several prototypes and auxiliary test instruments since this project started in the late nineties has led to 

the design of a mature device dubbed AUTODIF and which is now able to perform the DIflux tasks in an automatic,

computer-controlled way. Some important concepts which have proved successful in the robotisation of the DIflux are listed

below: 

1 Motorisation of the theodolite telescope's plungings are realized by nonmagnetic piezoelectric motors 

2 The motor precision and play free axes act as the theodolite axes 

3 Instead of the telescope - fluxgate setup of the DIflux, AUTODIF uses a laser - photocell - fluxgate setup (see 

Figure 2) 

4 Pointings to target are realized by a solid-state laser pointing to a corner-cube reflector 

5 The reading of the reflected laser beam is analyzed by a split photocell mounted around the laser 

6 The theodolite angle readings are performed by custom electronic optical encoders custom modified for 

nonmagnetic mechanical construction 

7 An electronic bubble level mounted on the alidade provides the horizontal reference 

8 Levelling is performed manually and any drift thereof is monitored by the electronic level 

Electronic and hardware architecture 

The instrument is based on two computers working in tandem: 

• A laptop running visual C++ software in a windows environment performs higher level functions for the

Graphical User Interface and for implementing the DIflux measurement protocol as well as its data processing 

• A 8051 based microcontroller programmed in C++ realises the low-level measurement, action and control 

functions. It communicates with the laptop over a USB interface. The programming of the 8051 occurs over a separate

RS232 line 

The 8051 microcontroller deals with the different sensors and electronics making up AUTODIF. Every sensor physical

board hosts its own ADC and communication with the 8051 is entirely digital over an 8 bit bus. All supply voltages are 

galvanically isolated from the battery and each other. The sensor and actuators list is as such (see Figure 3) 

• Fluxgate 

• Laser 

• Split photocells 

• Electronic level 

• Horizontal and vertical axis motors 

• Horizontal and vertical axis optical encoders 

Figure 3. General schematic of the 

AUTODIF. Analog signals run along the 

curves. All battery signals are 

galvanically isolated. 

221
AUTODIF Validation 

Validation goal 

At the beginning of our project, we set a validation goal of the instrument's accuracy in angular units. Our validation

target was: 0.1' = 6" = 0.0016° accuracy on all angular components describing the geomagnetic field's orientation. 

Figure 4. Declination and inclination baselines of the DOU Lama DFI variometer obtained with AUTODIF 

and the ZEISS116 reference Diflux. 

This validation goal resulted from the accuracy level one expects from an acceptable manual DIflux measurement. We

therefore strive to obtain from AUTODIF the same level of accuracy as from a standard geomagnetic observatory

instrument: the ZEISS020 manually operated DIflux. The latter has an angular reading accuracy of 0.1’. It can be found

worldwide in many geomagnetic observatories. It is considered to give a satisfactory level of accuracy for absolute 

measurements in observatories.

In house Validation 

The validation goal was reached in the Dourbes observatory at the end of January 2008, when a major magnetic hygiene 

check allowed us to identify and remove a slight magnetization in a motor element. This brought the baselines D0 and I0 of 

our LAMA variometer as measured by AUTODIF within the baselines bracket of the observatory instrument ZEISS116. It 

can be seen that after the check in January the baselines agreed within a few thousands of a degree (Figure 4): as expected, 

the removal of the magnetic motor element changed essentially the baseline value of the inclination, displayed in green full 

squares after the removal. 

AUTODIF Measurements in Boulder 

As the in-house measurements were satisfactory, we decided to try a more difficult kind of validation: the participation

in an international intercomparison of absolute instruments at this very IAGA observatories workshop. The difficulty of

packing and transporting a prototype instrument for measurement in foreign surroundings was expected as well as facing

the new conditions at the host observatory. 

The electronics were housed in a pellicase, which also housed the AUTODIF sensor (see Figure 5). The final package 

excluding the laptop and charger was barely larger than a classical DIflux box, and was able to withstand the transport

from Belgium to USA in aircraft cabin environment without damage. 

222
Figure 5. Packing of AUTODIF in a suitcase for transport to the BOU IAGA workshop with author N 

Berrami. Both electronics and robotized theodolite fit into the case. 

The automatic theodolite connects to the electronics in the pellicase via a 50 pins connector. The latter connects to the 

laptop via a normal USB line. The target pointings are a good indicator of the positioning abilities of the theodolite. During the 

7 days that the measurement session lasted, the target readings were made with high resolution and stability as attested on 

Figure 6. We estimate the target reading dispersion during Boulder workshop at less than 0.002°, which is very near to our

validation goal. The stability of the readings is not perfect, but this can be due to a slight drift in the position of the 

tripod-mounted corner-cube, which was subjected to the very strong winds blowing on the Boulder plateau. 

Figure 6. Horizontal circle readings of the target pointings as read during the absolute measurements with

AUTODIF

223
AUTODIF 2008 Workshop Results 

As seen on the target readings diagram in Figure 6, a series of 14 valid measurements was obtained from June 7th to 14th.

The differences with the BOU baselines (Observatory Reference – AUTODIF) as computed by the observatory staff were as 

follows: 

D component 0.4 ± 0.6 ' 

H component 3.1 ± 1.7 nT 

Z component -1.5 ± 0.8 nT 

In this case the baselines for H and Z are given in nanoteslas. We can relate to our validation goal expressed in angular

units by noting that in the field of Boulder, where F is about 53000 nT, a H change of 1 nT would result from an inclination

change of -3.6" and a Z change of 1 nT from an inclination change of 1.5". 

The final results of our AUTODIF observations in BOU are therefore slightly outside of our validation goal for D and

especially for H, where even the dispersion cannot explain the deviation of 3.1*3.6=11.6", almost two times the validation

goal. The results are encouraging however, especially considering the many difficulties which had to be tackled in a short

time. 

Possible issues 

We list here some possible issues able to be the cause of the above deviations indicating a lack of accuracy at the 10" 

level: 

• Corner cube motion (strong winds) - will affect D accuracy only 

• Magnetism of electronics console. Some magnetic elements in the Pellicase could explain the H deviations 

in the baseline 

• Slight diurnal baseline variation. As the BOU baselines were affected by a diurnal variation, some aliasing 

can cause dispersion and also offsets in the baseline intercomparison 

Future improvements on AUTODIF 

The lessons and advances made possible by the participation in the BOU IAGA workshop were many. This led to some

ideas and strategies for improving different aspects of the AUTODIF. They are mentioned below: 

Improve software for motor drives (reliability, longevity) 

Remove cable stress on rotating alidade (accuracy) 

Lower noise in laser and fluxgate electronics (accuracy, resolution, reliability) 

Implement scheduled measurement sessions (operation) 

Package device for robust and user-friendly operation and transport (dissemination; operation) 

 

Acknowledgements 

We wish to thank the organizers of the workshop for the excellent conditions they offered for the participation of

AUTODIF in the workshop. We thank especially Alan Berarducci who went out of his way so many times to help us and

support our endeavours. 

Bibliography 

Kring Lauridsen E., 1985. Experiences with the DI-Fluxgate Magnetometer Inclusive Theory of the Instrument and 

Comparison with Other Methods, Det Danske Meteorologiske Institut, Geofysiske Meddelelser R-71, p1-23, Kobenhavn. 

Rasson J.L., 1996. Progress in the design of an automatic DIflux, in Proceedings of the VIth Workshop on Geomagnetic 

Observatory Instruments, Data Acquisition and Processing (JL Rasson Ed.), Publ. Sci. et Techn. No 003, Institut Royal 

Meteorologique de Belgique, Brussels p190-194. 

Van Loo S.A. and Rasson J.L., 2006. Development of an Automatic Declination Inclination Magnetometer, in Geomagnetics 

for Aeronautical Safety, a case study in and around the Balkans, Edited by Rasson J L and Delipetrov T, Springer, 

ISBN-13 978-1-4020-5024-4, p. 177-186 

Hemshorn A., 2006. Gauss a geomagnetic automated system, Diplomarbeit am Institut für Geophysik und 

extraterrestrische Physik der Technischen Universität Braunschweig 

224
Testing the time-stamp accuracy of a digital variometer and its 

data logger

by

Jean L Rasson

Institut Royal Météorologique de Belgique

Centre de Physique du Globe, Dourbes

B-5670 Viroinval, Belgium

Abstract

How can the timing accuracy of a geomagnetic observatory recording variometer be checked? The task 

may be difficult when the time-stamp error must be much smaller than the sampling time. Here, we consider 

testing a variometer recording data at a 1 Hertz sampling rate and whose timing accuracy should be not 

worse than 0.01s. The proposed method considers the complete data acquisition chain, variometer and 

digital data logger, as a single unit. The unit is seen as a "black box", meaning the only access is to the input 

and output ports. The input is generated by a coil placed around the variometer sensor. The output is the 

standard data file recorded by the data logger. A statistical analysis is performed on the data obtained when 

the test signal was applied. Linear least squares fitting provides a robust estimation of any delays or time 

shifts that the signal suffered when passing through the black box.

Introduction

At the Dourbes INTERMAGNET (www.intermagnet.org) meeting in 2003, a decision was made to launch 

a new standard for recording variometric data with a sampling rate of 1 Hertz. 

A user survey (Love 2005) on 1 second [s] variometric data needs established that 10 milliseconds [ms] is 

the general consensus on timing accuracy for 1s sampling. The procedure addressed in this paper relates to 

the seeming difficulty to establish a timing accuracy two orders of magnitude higher than the sampling rate.

It is considered important that a magnetic observatory be able to autonomously test the timing accuracy of 

its geomagnetic field observations and not solely rely on the manufacturer claims. 

Background

In general, a 1s INTERMAGNET variometer is a triaxial magnetometer providing measurements of the 

variations of the geomagnetic field vector at a sample rate of 1 Hz. The overall timing accuracy with respect 

to UTC of the 1s INTERMAGNET variometer is requested to be not worse than 10 ms as it was specified in 

the minutes of INTERMAGNET Mexico meeting 2006. 

However, a band-limited measurement cannot provide data without introducing a phase-lag and a 1s 

variometer is necessarily band-limited. This phase lag will introduce time delays in the data acquisition chain: 

indeed for a periodic signal of period T, the relationship between timing delay t and phase lag F in degrees is 

given by: t = T×(F/360).

Other factors can introduce delays in the acquisition chain of the recorded channels: clock errors, delays 

in the cables carrying the signal from the timing receivers, digital delays in the data processing hardware, 

unintentional delays (bugs) created by the data logger software.

Due to these delays, the timing accuracy mentioned above is in question. 

As a remedy, and to cancel any constant delays existing in the data acquisition chain, the procedure of 

sampling allows shifting the timestamp on the data. Therefore if the analogue and digital part of the 

variometer use filters of the linear phase type, and the resulting constant delay is a multiple of the sampling 

rate, an adequate timestamp shift can theoretically result in a data acquisition with the required timing 

accuracy.

Please note that the 1s INTERMAGNET sample must refer to the top of the UTC second, i.e. the epoch 

hh:mm:ss.000, and all components measured by the 1s INTERMAGNET variometer must be sampled 

synchronously so that the overall timing accuracy of 0.01 s is maintained. It is useful to ask what the effect of 

phase lag on the PC-1 and PC-2 signals is, bearing in mind that part of the PC1 signals will be low-pass 

filtered but the variometer. The table below indicates how this limits the maximum phase errors on PC1 and 

PC2 waves:

Table 1: how a 0.01 s timestamp error affects the measured phase on PC1 and PC2 waves

Wave frequency range [Hz] phase error range for 0.01s delay [°]

PC1 0.2 - 5 0.72 - 18

PC2 0.1 - 0.2 0.36 - 0.72

225
Practical Requirements resulting from IMO constraints

For the testing method to be universal, the testing should be executable on any geomagnetic variometer. 

Also, the testing procedure should be simple enough to be carried out in a magnetic observatory modestly 

equipped with testing equipment. A voltmeter and oscilloscope may be necessary. The testing procedure 

needs a specialized device for the test to be carried out. INTERMAGNET intends to make this device 

available to IMOs for their testing purposes.

For performing the timing test, there is no necessity to modify the variometer installation, nor to dismantle 

nor stop the magnetometer electronics or data acquisition. Black-box testing is performed by injecting a 

magnetic signal to the variometer sensor and analysing the resulting output 1s INTERMAGNET file

Comments on current practices

Up to now, concerning the standard sampling of minute means, INTERMAGNET has not required testing 

of magnetometers for ascertaining their timing accuracy. The only mention of timing accuracy in time series 

for data acquisition in the INTERMAGNET literature (technical manual) has been the limitation of clock drift 

to 5 s. Testing of INTERMAGNET variometers has mainly regarded their long-term stability affected by 

temperature coefficients and mechanical changes in their geometry. This testing is carried out routinely by 

baseline checks using absolute measurements or by direct intercomparison between two or more 

instruments, one of them possibly subjected to increased levels of perturbations.

Figure 1. The LEMI-018 1s varimometer used in the timing test. An Helmoltz coil has been 

positioned around the variometer sensor in order to produce an artificial magnetic field test signal.

Black-box testing procedure

A black-box device: the 1s INTERMAGNET variometer and data acquisition

The black-box is a device which is accessible only at its input and output. There is no possibility to 

discover its properties or derive its characteristics otherwise by e.g. peering into it or dismantling it.

In this paper, the black-box is the 1s INTERMAGNET variometer plus its data acquisition system, 

considered as a single device. The input is therefore the magnetic field surrounding the variometer sensor 

and the output is the computer file with the magnetic data record sampled at 1 Hz. For this procedure, the 

input will be provided in part by a coil system sitting close to or around the variometer sensor and producing 

a magnetic field there (Figure 1). This field will be sensed by the variometer in addition to the geomagnetic 

field, following the superposition principle.

226
The test signal input to the black box.

The test signal S(t) will be a periodically variable magnetic field of constant period T (frequency f=1/T) and 

amplitude A. The value of the signal period T is taken well within the pass-band of the variometer. The phase 

F of this test signal will be referred to an UTC epoch t0 = hh:mm:ss.000 fixed beforehand in each testing 

experiment. The phase must be a constant, ideally respecting the condition F=0. The timing of this test 

signal S(t) should be at least an order of magnitude better than the 0.01s variometer requirement. Therefore 

t0 and the phase shift requirement above should be known with an error not worse than 0.001 s. The 

waveform of the test signal may be a sine, triangle or square wave. It is expected that generating this test 

signal will be easier in the form of a triangle or square wave. In order to respect the accuracy of the timing 

requirements it is recommended that the test signal be directly derived from a GNSS or UTC related 

terrestrial broadcasted signal. In order to maximize the signal-to-noise ratio, the amplitude of the test signal 

should be made as big as possible. But care should be exercised so as to avoid saturation of the variometer. 

For a typical INTERMAGNET variometer, a pk-pk amplitude of 4000 nT should be satisfactory in this respect. 

Perturbation inflicted by this test on nearby geomagnetic equipment should also be considered when 

determining the maximum amplitude of S(t). The real thing to worry about is to have the current in the coil 

with no delay or phase lags, and this should not be a problem at the millisecond level. Anyway it can be 

monitored with an oscilloscope measuring the voltage across a resistor in series with the coil (Figure 2). In 

case of triangle signal, a high linearity should be obtained. It is recommended that non-square wave test 

signals be tested in the same way as the recorded signal in order to establish any timing inaccuracies of their 

fundamental (first harmonic).

Proposed experiment

A timing receiver (Figure 2) produces 1 Hz pulses synchronized to UTC. They increment a symmetrical 

digital divide-by-M down counter providing an output pulse train at the exact frequency 1/M Hz.

A manual reset switch is operated until a fraction of a second before t0 so that the rising edge of the 

following UTC 1 Hz pulse will change the output state of the counter. Therefore the counter output will show 

a rising edge at t0. The next output rising edge will occur M seconds thereafter and will repeat with that 

pattern. This counter output signal represents a logic signal of symmetrically alternating highs and lows. The 

epoch t0 is thus fixed. It is useful to have access to a reference clock when operating the manual Reset 

switch and write down the observed value of t0.

Figure 2. The test set-up of the variometer timing test.

This signal is then optionally transformed into a triangular wave by an electronic device providing linearly 

increasing voltages during highs and decreasing voltages during lows of the logic signal. A known way to do 

this is by charging or discharging a capacitor at constant current.

It can also be decided to run the test with a square wave signal. In that case the triangle converter is 

bypassed.

The signal is then converted to a current with a conversion factor of K Amperes/Volt by a voltage-tocurrent amplifier. The value of K can be adjusted by a control “Gain adjust” in order to maximize the signalto-noise ratio of the test. This gain adjust may be a series power resistor in its simplest form. The resulting 

current is then sent over a (long) bifilar cable to the coil sitting close or around the variometer under test.

227
In the return line of the cable, a 1 ohm resistor is inserted so that the current in the coil can be 

unobtrusively measured by inspecting the voltage across it. This can easily be done by an oscilloscope.

Analysis of the test record signal

The recording will start with the sample at t0 = hh:mm:ss.000 and will last for a total of N samples, making 

the test duration equal to N seconds. The recording will be in the usual 1s INTERMAGNET format. The 

analysis is based on the idea of linear least squares parameter estimation on a signal having a known 

frequency. The idea is from Earth Tidal analysis techniques where the amplitude and phase estimation of 

periodic signals at the lunisolar frequencies can be performed with high accuracy, even with low sampling 

rates and in presence of noise, provided that a long enough series of data is available1. 

A curve having the form ( ) 











+= +j

p

t

T

s t ba

2

cos is fitted in the least squares sense to the recorded 

data and a (offset), b (amplitude) and f (phase lag) are the parameters to estimate in order to minimise the 

variance. It can be shown2 that the least squares estimation is linear if T is known, making the method robust 

and accurate in all cases: a, b and f can be calculated by a simple formula encompassing the N samples 

record as well as the values N and T. Details of the computations are given below and illustrated in an excel 

file available upon request (jr@oma.be).

As the sinusoidal least squares estimation concerns only the first harmonic of the recorded test signal, it is 

not necessary to inject a pure sine wave as test signal. Indeed any periodic signal would do as long as its 

period is constant and it's fundamental has F=0 or known with sufficient accuracy. Therefore preference is 

given to square or triangular waves, which are quite easy to generate with these characteristics. 

The timing error E is then obtained as E = T*(f /360). The other parameter b gives an estimate of the half 

peak-to-peak amplitude. It can be used to ascertain the amplitude dependence on frequency. Parameter "a"

gives an estimate of the mean value of the component.

Example: Given a test signal period at T = 16s, if f is found to be 2.0° by the least squares computation, 

then E = 16*(2/360) = 0.089s

Of course the natural variations of the field will interfere with the estimation, but as noted above, the 

amplitude b can be made big enough and the series long enough with numerous cycles in the test signal 

until the effect of these variations become negligible by statistical averaging.

The test can be repeated at various frequencies, in order to establish the variometer phase lag 

dependency on frequency.

Formulation for least squares parameter estimation on a sinusoidal signal

The sinusoidal signal curve fit

( ) 











+= +j

p

t

T

s t ba

2

cos

 can be rewritten as 

t

T

t D

T

s t DD

p 2p

sin 2)( cos += 21 + 3

where

sin .

cos

3

2

1

j

j

D b

D b

D a

−=

=

=

The data record of the test signal will contain the N samples:

n n n

t

T

t D

T

s DD

p 2p

sin 2cos += 21 + 3, n = 0,...,N-1 N ≥ 3

 

1

 Melchior P, 1978. The tides of the Planet Earth, Pergamon Press.

2

 Daniel Malacara, Interferogram analysis for Optical Testing, Marcel Dekker Publ., 1998, ISBN 0824799402

228
The best fit in the least squares sense is obtained if the D parameters D1, D2 and D3 are chosen so as to 

minimize the variance e, defined by

2

3

1

0

21

)

2

sin

2

( cos

1

n nn

N

n

t s

T

t D

T

DD

N

= ∑ + + −

−

=

p p

e

Taking the partial derivatives of e with respect to the D parameters leads to the matrix equation:

































=





















































∑

∑

∑

∑∑ ∑

∑ ∑ ∑

∑ ∑

n n

n n

n

n n n n

n n n n

n n

s

T

t

s

T

t

s

D

D

D

t

T

t

T

t

T

t

T

t

T

t

T

t

T

t

T

t

T

t

T

N

p

p

p pp p

p p pp

p p

2

sin

2

cos

2

sin 2sin 2cos

2

sin

2

sin 2cos

2

cos

2

cos

2

sin 2cos

3

2

1

2

2

This general equation can be solved for the Di parameters by inversion of the symmetric 3x3 matrix. Note 

this matrix depends only on the test signal frequency 1/T, the number of samples N and the sampling rate, 

but does not depend on the measured signal sn. It can be computed once and for all for a given experiment 

set-up.

Figure 3. Schematic diagram of the LEMI-018 triaxial 1s variometer

Also the procedure does not call for a constant sampling rate; therefore gaps in a recording – if any - can 

be accommodated.

The final result is obtained as:

o

D

D

atn 90

2

3 −





















 j −= ,

2

3

2

b 2 += DD ,

a = D1.

The value of f, extracted from this equation in degrees, must be corrected by the amount F specified 

above. If the period of the test signal is T, the timing error E is given by:

360

×j

=

T

E .

All the above calculations can be easily implemented in a spreadsheet which is available from jr@oma.be 

upon simple request.

229
Implementation of the timing test on the LEMI-018 variometer

The lemi-018 is a triaxial variometer providing inter alia a 1s data output which has been carefully filtered 

and compensated by the manufacturer (LCISR, Lviv, Ukraine) to provide 0.01 timing accuracy (Figure 3). It is 

set-up in the Dourbes Observatory variometer vault in the XYZ orientation. The IAGA2002 file format is used 

and its timestamps are disciplined by a GPS receiver inside the magnetometer electronics and do not 

depend on the data logger PC clock. All low-pass filters in the LEMI-018 have been designed to exhibit a 

phase-lag behaving linearly with frequency (Figure 4). Therefore any timing error due to filter phase-lags will 

be constant with frequency, and can be compensated en bloc by shifting the time stamp.

The test is organized as in Figure 2 with a Motorola M12 OnCore GPS receiver separate from the LEMI018 providing a 1 Hz UTC pulse. A DCF clock, disciplined by the German Mainflingen standard time signal 

broadcast, provides a time display to the nearest second. 

The test signal is a 12 V square wave whose period can be set to 2M seconds: no saw tooth converter is 

used in this experiment. An appropriate resistor is inserted in series to convert the voltage in a current in 

order to provide suitable square wave amplitude.

The test coil is installed coaxially around the LEMI-018 head with its axis off the vertical so as to apply a 

field on all three components (Figure 1). It is a Helmoltz configuration with the following characteristics:

Table 2: Characteristics of the excitation coil

Type/Serial ASKANIA Nr. 494391

Scale factor 25.8 nT/ma

Ohmic resistance (cable incl.) 1 Ohm

Inductance (cable incl.) 20 μH

Coil diameter 220 mm

Coil inside clearance diameter 194 mm

The test signal generator allows injecting a 12V amplitude symmetrical square wave through a set of 

resistors into the coil. The square wave has its rising edge synchronized with a UTC second pulse derived 

from a GPS receiver. Radio controlled clocks (France-Inter and Mainflingen DCF broadcasts) allow to lift any 

ambiguity in the GPS generated UTC second pulse. The epoch of the first rising edge of the square wave is 

noted as the value t0 (see Figure 2).The period T of the square wave can be set to any value of T=2M

seconds by the binary frequency dividers in the generator. 

Figure 4. The experimentally measured phase response of the variometer under test

230
The duration of the experiment is set to 1000 seconds (so N=1000) generally but any other duration could 

be used.

A special feature of the generator is a programmable delay which can be inserted before the square wave 

rising edge. This effectively delays the whole calibration signal by a known amount adjustable between 0 and 

2000 milliseconds with respect to UTC. This feature is a self test of the generator and will provide an 

estimate of how precisely the test can quantify the timing error. It is not planned to use this feature in routine 

testing of variometers at geomagnetic observatories.

For this test the *.sec file output by the LEMI-018 is used. This file has the file format standard tentatively 

set by INTERMAGNET for recording the 1s data.

Several timing tests were performed during April and May 2008 at wave periods ranging between 4 and 

32 s on the LEMI-018 magnetometer. The half amplitude of the test wave as measured on the 3 recorded 

components ranged between 22 nT and 366 nT. The Table 3 summarizes the results. 

The results for the LEMI-018 show a timing error E which is always less than 10 milliseconds in the test 

cases when the artificially introduced delays are naught. A foreseeable decrease of amplitude with 

increasing frequency is noted.

When an artificial delay is inserted in the excitation square wave signal, the timing test is able to measure 

it with good accuracy. When the timing delay is small (E < 0.05s) errors of a few 0.01s are noted. For bigger 

delays the errors are not worse than 10% of the delay. Those errors show the limitation of the least squares 

method in deriving the delay accurately. They can be reduced by increasing the amount of samples used in 

the analysis: a good rule of thumb is to analyze a record covering at least 100 cycles of the test signal.

No significant skew is noted between the component channels of the magnetometer, although the X 

channel has a slight difference with the Y and Z channels. This skew has been confirmed by the 

manufacturer's independent tests.

Even with relatively low amplitudes of the test signal, down to 44 nT, the test performs well and the delays 

are determined with the stated accuracy.

Table 3. Summary of timing tests of the LEMI-018 1s variometer

Initial Epoch Least squares sinusoidal fit results

t0

Square wave test signal

Component X Component Y Component Z

date time # 

samples

Period

[s]

Artificial

Delay [s]

Delay 

[s]

Ampl.

[nT]

Delay 

[s]

Ampl.

[nT]

Delay 

[s]

Ampl.

[nT]

8/04/08 11:05:00 1000 4 0.00 0.01 288.2 0.00 150.2 0.00 105.3

8/04/08 9:45:00 1000 8 0.00 0.00 344.2 0.00 179.5 0.00 125.8

3/04/08 11:33:00 1000 16 0.00 -0.01 365.6 -0.01 190.6 -0.01 133.7

8/04/08 6:51:00 1000 16 0.00 0.00 362.1 0.00 188.8 0.00 132.3

8/04/08 7:48:00 1000 16 0.00 0.00 362.1 0.00 188.7 0.00 132.3

9/04/08 12:03:00 1000 32 0.00 -0.01 365.4 -0.01 190.5 -0.01 133.6

11/04/08 7:24:00 1000 16 0.03 0.04 315.9 0.04 164.7 0.04 115.4

11/04/08 7:24:00 1000 8 0.03 0.05 171.1 0.04 89.2 0.04 62.5

18/04/08 6:41:00 1000 16 0.00 0.01 176.4 0.00 92.0 0.00 64.5

18/04/08 10:09:00 1000 32 0.00 -0.01 179.4 -0.01 93.6 -0.01 65.6

7/05/08 6:34:00 1000 16 0.00 0.01 44.8 0.00 21.7 0.00 197.8

7/05/08 7:02:00 1000 16 0.02 0.03 44.8 0.02 21.7 0.03 197.8

7/05/08 8:18:00 1000 16 0.56 0.54 45.4 0.54 22.1 0.54 200.8

7/05/08 9:04:00 1000 16 0.58 0.55 45.4 0.54 22.1 0.54 200.7

7/05/08 10:52:00 1000 16 0.66 0.60 45.3 0.59 22.0 0.59 200.3

7/05/08 12:21:00 1000 16 0.77 0.69 45.1 0.69 21.9 0.69 199.4

7/05/08 12:46:00 1000 16 1.31 1.39 45.3 1.39 22.0 1.39 200.1

Conclusion

The timing test for geomagnetic observatory variometer explained in this note is able to evaluate the 

variometers time stamp accuracy to 10 milliseconds or less. 

The "black-box" test is simple to perform and does not perturb the variometer installation and does not 

need to perform specialised measurements on the variometer electronics. A magnetic signal is injected in 

the sensor via a coil and a straightforward statistical procedure extracts the timing errors from the standard 

variometer record.

Bibliography

Love, J.J., 2005. 1-Second Operational Standard for Intermagnet, Minutes of the OPSCOM/EXCON 

INTERMAGNET meeting, Mexico 2005, unpublished

Melchior P., 1978. The Tides of the Planet Earth, Pergamon Press

Malacara D., 1998. Interferogram analysis for Optical Testing, Marcel Dekker Publ., 1998, ISBN 0824799402

231
INDIGO Digital Observatory Project, 2004 – 2008 

John C. Riddick, Jean L Rasson2, Chris. W. Turbitt1, Simon M. Flower1

1

 British Geological Survey, Murchison House, West Mains Road, Edinburgh, EH9 3LA, United Kingdom 2

 Institut Royal Météorologique Centre de Physique du Globe, B-5670 Dourbes, Belgium 

Abstract 

The INDIGO Digital Observatory Project was introduced at the XIth IAGA Workshop in 

Kakioka, 2004. The original objective of the INDIGO project was to make use of fifteen EDA 

fluxgate magnetometers donated to the British Geological Survey (BGS), coupled with a low 

power digitiser to make filtered one-minute digital recordings at observatories where there had 

been no or little history of continuous digital recordings. The first system was deployed in 

October 2006 at Chiripa Observatory (Costa Rica). Since then deployments have followed in 

Arti (Russia), Maputo and Nampula (Mozambique), Pelabuhan Ratu, Kupang, Tuntungan 

(Indonesia) and Karachi (Pakistan). Minute mean data are recorded in INTERMAGNET format 

and are regularly transmitted, along with absolute observations, to BGS for quality control and 

archive. Here we review the progress of the INDIGO project to date and look at whether the 

project is achieving its aim of increasing the number of digital observatories. 

1 Introduction 

This paper is a report on the progress of a project originally introduced at the XIth 

Observatory Workshop in Kakioka (Riddick et al, 2004). At the 2004 workshop, we described 

how 12 tri-axial EDA fluxgate magnetometers, donated to the British Geological Survey (BGS) 

by the instrument pool of the UK Natural Environment Research Council, were renovated and 

tested for geomagnetic observatory use. The magnetometers, along with another three EDA 

fluxgate magnetometers subsequently acquired, were put together with a simple to use digitiser, 

power supply and recording and analysis software, and offered to observatories wishing to make 

digital recordings. This project, which aims to support new and developing overseas 

observatories with INTERMAGNET-standard instrumentation (INTERMAGNET, 2008), has 

become known as INDIGO. 

The object of the INDIGO project is to improve the global coverage of digital observatories, 

by deploying magnetometers in; 

i) Newly established observatories. 

ii) Existing observatories making analog recordings to supplement or replace those recordings 

with digital recordings. 

iii) Existing observatories currently making digital recordings to provide a secondary digital 

system for quality control and redundancy. 

We shall provide a brief description of INDIGO, list the observatories that are currently 

operating INDIGO equipment and give a summary of the current status. 

2 INDIGO Instrumentation 

The majority of the instruments that were donated to BGS were EDA fluxgate variometers 

(of type FM100B or similar). Other, full-field, fluxgate magnetometers (FM100C) were 

converted to variometer to increase resolution such that they could be deployed as observatory 

instruments. As the aim of the INDIGO project is to provide a complete instrumentation set to an 

observatory for digital recording, a digitiser box has also been developed, containing an 

232
Advantech ADAM-4017 16-bit ADC and an 8-bit PIC microprocessor. The microprocessor 

maintains a clock synchronised to around one-second of UTC using an external GPS receiver 

and polls the digitiser every five seconds. The digitiser data are compiled by the microprocessor 

and are output on an RS-232 port for recording by a remote PC. The data link between the 

digitiser and the recording PC can be made over a fibre-optic cable of up to 100m for 

deployments in areas prone to lightning strikes. 

The digitiser box can also be used to sample a proton magnetometer, such as a Geometrics 

G-856 or Gem Systems GSM-90, every ten seconds. The proton magnetometer data are then 

included in the data stream sent to the recording PC. The digitisers, magnetometers, GPS 

receiver and data link are operated from a single battery-backed 12 VDC supply. 

To capture the data on the recording PC, software is provided to receive the serial data 

stream from the digitiser, scale and filter the raw samples to one-minute data, plot the data and 

record them in INTERMAGNET (IMFV1.22 or IAGA-2002) format files that can be directly 

mailed to the Edinburgh GIN. To simplify the initial production of baseline-corrected data, the 

software also allows absolute observations to be entered and processed as well as calculating 

preliminary baseline values. 

Observatories are asked to provide accommodation for the variometer (preferably 

temperature stabilised), a reliable mains supply, a PC for data acquisition and to undertake a 

regular programme of absolute observations. 

3 Project Status 

At present, of the original twelve donated EDA fluxgate magnetometers, six magnetometers 

have been deployed and a further five have been tested, ready for deployment. Twelve digitiser 

boxes have been constructed and, including spares, eleven of these have been deployed. This is 

in support of seven observatories, four of which are running with a single fluxgate magnetometer 

and three are running with an additional proton magnetometer. 

As the aim of INDIGO is to increase the global distribution of INTERMAGNET-standard 

digital observatories, priority is given to new or existing observatories in areas of poor 

observatory coverage. Figure 1 shows the observatories currently supported by the INDIGO 

project: Chiripa Observatory in Costa Rica, Arti Geophysical Observatory in Russia, Maputo and 

Nampula Observatories in Mozambique, Karachi Observatory (Pakistan) and three Indonesian 

Observatories – Kupang in West Timor, Pelabuhan Ratu on Java and Tuntungan on Sumatra. 

Figure 1. Observatories operating INDIGO instrumentation (red triangles) against other geomagnetic 

observatories (blue circles) 

233
3.1 Chiripa Observatory 

Prior to 2006, Chiripa Observatory (CRP, 10.43o N 275.08o E) was making analog 

measurements and absolutes using a QHM. An INDIGO EDA FM100B fluxgate magnetometer 

was installed in March 2006, but has had limited success due to initial magnetometer faults, 

lightning damage and a severe storm in January this year (2008). The high winds of the storm 

severely damaged the observatory buildings and, although the instruments are thought to be 

undamaged, the observatory is currently not operational. The observatory possesses a Bartington 

MAG-01H on a Carl-Zeiss 015 non-magnetic theodolite and the US Geological Survey (USGS) 

provided a replacement set of MAG-01H electronics in October 2007 in order that an absolute 

observations program may be established once the observatory becomes operational again. 

3.2 Arti Geophysical Observatory 

Arti Observatory (ARS, 56.43o N 58.57o E) is currently making digital recordings, along 

with a regular program of absolute observations, and has supplied hourly mean data to the World 

Data Centre (WDC) since 1973. In September 2006, an INDIGO EDA FM105B fluxgate 

magnetometer was installed as a secondary variometer for baseline comparison with the existing 

instruments, along with a Geometrics G-856 proton magnetometer donated by the USGS. The 

fluxgate magnetometer has been installed in a shallow pit for temperature stability; however 

there have been two of instances of water ingress to the pit, the latest in April 2008. The 

instruments, although damaged, are thought to be recoverable, but the INDIGO system in Arti is 

not currently operational. 

3.3 Maputo Observatory 

Maputo Observatory (LMM, -25.92o N 32.58o E) is a well established observatory that has 

supplied hourly mean values to the WDC since 1980 and has made digital recordings since 1997. 

An INDIGO fluxgate magnetometer was installed in June 2006 as a secondary system and, since 

then, data are being regularly sent from both the INDIGO system and the original system to the 

Edinburgh INTERMAGNET GIN. Maputo has proven to be a site with low cultural noise and is 

operated with a program of regular, good quality absolute observations. 

3.4 Nampula Observatory 

Nampula (NMP, -15.08o N 39.25o E) is an historic observatory which produced analog 

recordings until 1984. The observatory was reinstated by Dr. Rasson in October 2006 with the 

installation of an INDIGO fluxgate magnetometer and the provision of a Ruska DIflux theodolite 

and a Geometrics G826 proton magnetometer for absolute observations. However the data 

recorded by the INDIGO system has been plagued with periodic noise, the source of which has 

yet to be determined, but is most likely attributed to the fluxgate magnetometer. In addition, 

there are frequent power cuts causing the data to be intermittent. Despite the problems with the 

digitally recorded data, the observer in Nampula is making good absolute observations on a 

regular basis and sending what digital data that are being recorded to the Edinburgh GIN. Until 

the fluxgate magnetometer is repaired and a more reliable recorder provided, the absolute 

observations are being used to generate annual means. 

3.5 Kupang Observatory 

Kupang Observatory (-10.38o N 123.63o E) was established as a new observatory in 

November 2006 with the installation of an INDIGO digitiser and power supply. The operating 

institute (Geomagnetic & Atmospheric Current Subdivision of the Indonesian Meteorological 

234
and Geophysical Agency) provided the magnetometers (DMI FGE fluxgate magnetometer and 

Gem Systems GSM90 & GSM-19 Overhauser magnetometers) as well as a DI theodolite, 

although recordings were not begun until February 2008 due to damage to the instruments 

caused by problems with the electricity generator. Once the observatory was operational, a 

regular absolute program was established and initial impressions are that the observatory site is 

low in cultural noise. A small amount of data has been transmitted to the Edinburgh GIN, but the 

main problem is that frequent power cuts are on-going and the observatory can be recording for 

less than 5% of the time. 

3.6 Pelabuhan Ratu 

Pelabuhan Ratu (-6.98o N 106.55o E) is another newly established Indonesian observatory 

intended to replace Tangerang Observatory. The instrumentation is similar to that installed at 

Kupang Observatory, again having been supplied by the Indonesian Meteorological and 

Geophysical Agency. The observatory became operational in December 2006. Although initially 

intermittent due to electricity supply problems, the observatory is currently supplying good 

quality data with only infrequent data gaps, although the data are not currently being transmitted 

to the Edinburgh GIN. The observers undertake a program of regular, good quality absolute 

observations and are compiling data into a set of monthly bulletins. 

3.7 Karachi Observatory 

Karachi Observatory (KRC, 24.95o N 67.13o E) has submitted hourly mean values to the 

WDC from 1996-1999 derived from analog recordings. In November 2007, Dr. Rasson installed 

an INDIGO system with an EDA fluxgate magnetometer and he also provided the observatory 

with a Tavistock MkII non-magnetic DI theodolite. Since the installation, observers have made 

regular, good quality, absolute observations and the digital data recorded at Karachi are regularly 

transmitted to the Edinburgh GIN. Unfortunately, the observatory site is likely to become 

unsuitable as a result of other developments nearby and the instruments are shortly to be moved 

to another site within Pakistan to establish a new observatory. 

3.8 Data Transmissions 

All INDIGO observatory data transmitted to Edinburgh are routinely compiled into a 

monthly bulletin. Although these data are not processed prior to compilation (i.e. the bulletins 

are not a finalised data set), they are compiled with any operating records that an observatory 

provides. Hence, these bulletins provide a regular record of the operating state of each 

observatory involved in the INDIGO project. As well as providing a document for future 

reference, the monthly bulletins are a regular review of data quality and operational concerns at 

each observatory, providing valuable feedback to the observers. 

235
Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr

Arti

Chiripa

Maputo

Nampula

Karachi

Pelabuhan Ratu

Kupang

2006 2007 2008

Figure 2. Monthly bulletins produced of INGIGO data between 2006 & 2008 

The monthly bulletins that have been produced by the INDIGO project are shown in Figure 

2. Although the number of bulletins produced is not an indicator or guarantee of data quality, the 

figure clearly shows the increase in number of data bulletins, and the amount of digital data it 

represents, which is indicative of the progress made by the project over the past two years. 

Ultimately, a digital observatory should be able to make their data available to the 

international community and observatories using INDIGO equipment are encouraged to e-mail 

their data to the Edinburgh GIN, albeit on a test basis initially and not for wider publication. The 

chart in Figure 3 shows that the number of observatories and the percentage of data being 

transmitted every month have significantly increased over the last two and a half years. 

Data Received in the Edinburgh GIN

0

10

20

30

40

50

60

70

80

90

100

Nov06Dec06Jan07Feb07Mar07Apr07May07Jun07Jul-07 Aug07Sep07Oct07Nov07Dec07Jan08Feb08Mar08Apr08May08

% Data per Month

Karachi

Nampula

Maputo - INDIGO

Maputo - FlareP

Figure 3. Percentage of INDIGO data transmitted to the Edinburgh INTERMAGNET GIN per month 

236
Also clear in Figure 3 is the decrease in the most recent data transmitted to Edinburgh. This 

highlights the difficulty that most of the INDIGO observatories encounter in transmitting data 

promptly. Due to the remoteness of the INDIGO observatories and, frequently, the lack of 

additional infrastructure on-site, data often have to be transferred to another institute or internet 

café to be e-mailed to Edinburgh, typically causing a delay of a few days to some weeks after 

recording. 

4 Future Developments 

It is anticipated that the damage that caused both Chiripa and Arti observatories to cease 

operating INDIGO systems this year will be rectified in the coming months and these 

observatories should be able to resume recordings. The high level of noise evident in the data 

from Nampula Observatory, which is suspected to be an instrument fault, is likely to be 

addressed during a site visit in October this year. An existing observatory at Tuntungan in 

Indonesia (TUN, 3.52o N 98.57o E), has recently been provided with an INDIGO digitiser to 

assess the state of the magnetometers prior to a full installation in the near future. A successful 

installation in Tuntungan would bring the number of observatories operating INDIGO systems to 

eight. 

A further hardware development, which could solve the cause of intermittent data at some 

observatories, is the design of a microprocessor-based logger capable of capturing the serial 

output stream from the digitiser and writing this, in raw form, to a USB memory stick. This 

logger can be located close to the digitiser in the variometer house, so that the communication 

line between the digitiser and the recording PC can be removed, reducing the risk of lightninginduced damage to the digitiser or the PC. Since the logger is relatively low-powered, it is 

conceivable that the logger, digitiser, instruments and GPS receiver could be operated from a 

battery-backed solar panel, particularly at low-latitude sites, further reducing the risk from 

lightning. As the data recorded on USB are in the same format as the data received by the 

recording PC, existing software can be used to process the data to one-minute INTERMAGNET 

format files. One disadvantage of such a method of recording is that it requires an observer to 

retrieve the data by entering the variometer house to swap USB memory sticks, potentially 

further delaying the onward transmission of data. 

We also hope to further develop the analysis software in the near future to allow observers to 

readily apply preliminary and definitive baselines to the variometer data, such that observatories 

are able to produce the monthly bulletins and finalised data sets autonomously. This is seen as a 

necessary step, along with the regular transmission of data, in preparing INDIGO observatories 

for a future application to INTERMAGNET. 

5 Summary 

Over the two and a half years that equipment has been provided to overseas observatories by 

the INDIGO project, there has been significant progress in the project’s aim to improve the 

global distribution of digitally recording geomagnetic observatories. However, most of the 

observatories operating INDIGO equipment have been beset by problems familiar to any 

operator of remote stations: environmental conditions such as lightning strikes; irregular mains 

electricity supply; and difficulty in providing a reliable, timely method of transmitting data. 

Despite these difficulties, there are five currently operating INDIGO observatories, three of 

which are transmitting to the Edinburgh GIN. All but one observatory has an on-going 

programme of absolute observations and the project is hopeful that, with the assistance of 

237
observatory staff, the remaining recording and transmission problems can be resolved to bring 

one or more observatories to INTERMAGNET standard in the near future. 

6 Acknowledgements 

The work of the INDIGO project is greatly supported by the observers at each observatory, so 

we wish to express thanks to Pavel Borodin, Jorge Brenes, Ivan Monge, Manuel F Kampine, 

Armindo Nhatsave, Antonio Mucussete, Malik Murtaza, Ayyaz Ameen, Madeeha Ashfaque, 

Muhammad Husni, Mahmud Yusuf, Muhammad Hidayat, Noor Efendi and Kadek Oca Santika. 

7 References 

INTERMAGNET Technical Reference Manual V4.4, 2008, INTERMAGNET Operations 

Committee, c/o Geological Survey of Canada, #7 Observatory Crescent, Ottawa, Ontario 

Canada K1A 0Y3 

Riddick, J.C, Turbitt, C.W., Flower, S.M., A Low-cost Digital Observatory System for 

Developing Countries, Proceedings of XIth IAGA Workshop of Geomagnetic Observatory 

Instruments, Data Acquisition and Processing, Kakioka and Tsukuba, Japan, Nov 9–17. 

International Association of Geomagnetism and Aeronomy, 2004 

238
Evaluating the Noise for a Commonly Used Fluxgate 

Magnetometer – for 1-second Data

Shanahan T J G and Turbitt C W 

British Geological Survey 

Murchison House, West Mains Road, Edinburgh, EH9 3LA, UK

E-mail: tjgs@bgs.ac.uk 

Abstract 

Now that the specifications for INTERMAGNET 1-second data are being defined, 

geomagnetic observatories are seeking to develop and improve instrumentation to 

meet the new standard. This study investigates the capabilities of current 

instrumentation, such as frequency response and dynamic noise characteristics with a 

view to achieving the new standards, in particular 0.01 nT resolution data. Also 

discussed are the benefits of delta-sigma digitisation, a technique that can simplify 

both the pre-sampling analog and post-sampling digital filtering whilst providing the 

additional dynamic range that 0.01 nT resolution data requires. The results of noise 

tests of a DMI FGE fluxgate magnetometer and seismic digitiser are presented for 

long and short time periods. 

Introduction 

In 2005 a survey was conducted to assess the future requirements of the scientific 

community for magnetic data (Love, 2005). The response to this poll indicated a 

requirement for 1 Hz data with higher precision to resolve the low amplitude signals 

at these higher frequencies. To establish the capabilities of our current fluxgate 

instruments we developed simple methods for testing noise and frequency response. 

The temporal and spectral information of the noise was analysed to quantify both the 

overall performance and at specific frequencies.

The majority of ground based magnetic observatories around the world employ a 

fluxgate magnetometer for vector measurements of the geomagnetic field. BGS 

operates three magnetic observatories in the UK and four overseas, all of which use 

DMI tri-axial fluxgate magnetometers. In general, fluxgate magnetometers have been 

designed for long-term stability, low frequency applications, and as a result have not 

been widely used for measuring short period variations. If BGS is to meet the 

anticipated standards for 1-second data, the limits of our current instrumentation need 

to be established and sound methods for testing magnetometer noise need to be 

implemented. 

Typical testing of instrument noise is often based on input terminated signals; this is 

not suitable for use with a feedback system such as the fluxgate. One method is to use 

very high permeability materials to isolate the magnetometer from the natural, time 

varying magnetic field. However, this method does not test the dynamic behaviour of 

the fluxgate under normal operation, i.e. with bias fields applied and varying feedback 

current. In this study, the noise is measured under more realistic operating conditions

by running two fluxgates in close proximity to remove the common signal. 

239
Specifications for 1-second Data

For a target instrument noise of 0.01 nT RMS from a DMI FGE version J fluxgate 

with a dynamic range of ± 4000 nT, the Signal to Noise Ratio (SNR) can be 

calculated: 

(1)

(2)

The required resolution of the digitiser can then be found using the well known 

equation to determine the Effective Number of Bits (ENOB) (Equation 3). It is clear 

that the currently used 16-bit ADCs do not provide sufficient dynamic range for 

1-second requirements. 

(3)

Delta-Sigma Modulators 

One of the most practical choices for high resolution ADCs is the Delta-Sigma

converter. The Delta-Sigma is the industry standard for applications that require 

above 20 bits of resolution. In addition to providing large dynamic range, these 

converters have a number of other advantages to typical ADC architectures. SigmaDeltas use the process of oversampling to achieve their high resolution; this process

inherently relaxes the requirements of anti-aliasing filters by shifting the Nyquist 

frequency several orders of magnitude up the spectrum. Signals outside the bandwidth 

of interest are removed by digital filtering (typically Sinc3) which provides highquality stop-band attenuation, low ripple pass-band, fast transition-band and linear 

phase. With the Delta-Sigma, it is possible to acquire 1 Hz data without complex

analog filtering whilst achieving superior performance through digital filtering, 

packaged in a single device.

Noise Measurement Method 

The process for quantifying the instrument noise of the fluxgate is illustrated in 

Figure 1. Here, two magnetometers are run concurrently measuring the ambient field, 

and each fluxgate system is then sampled on two separate ADC channels. The 

cumulative noise from both instruments is derived by taking the difference between 

the two measurement channels. The ADC used for testing the fluxgate noise was a 

Guralp Systems DMG-24 converter used in seismic applications. This ADC has a 

dynamic range of over 137 dB, primary sample rate of 32 kHz and Finite Impulse 

Response (FIR) digital filter with out-of-band attenuation of 140 dB. The converter 

was configured to output at 1 sample per second (1 Hz).

dB

nT

nT SNR 109

01.0

4000 2 log20 10  ≈



 



 =





 



 = )(

)( log20 10 Noise rms

Signal rms SNR

bits SNR ENOB 19

2log20 ≈=

240
Figure 1 Fluxgate Noise Testing Procedure

The output from the testing process with the mean value removed represents the 

combined RMS noise from both fluxgates and a very small amount of noise from the 

ADC. For a single fluxgate, the noise can be approximated by assuming each 

instrument contributes an equal amount of noise (Equation 4 and 5).

(4)

 (5)

The Noise Power Spectral Density (NPSD) is calculated using the WelchPeriodogram method. For the fluxgate data, a total of 14, 2048 point sections were 

averaged with Bartlett windowing applied. The Discrete Fourier Transform (DFT)

was then produced using the FFT algorithm. The final power spectral density plot is 

only plotted for positive frequency and so only represents half the total noise power. 

To show the contribution of the digitiser noise, the ADC input-terminated channel 

noise was also calculated and plotted.

Noise Test Results 

The NPSD shows noise floors of 0.001 nT/√Hz and 0.02 nT/√Hz @ 0.25 Hz for the 

ADC and fluxgate respectively (Figure 2). Total fluxgate RMS noise (up to the 

Nyquist rate of 0.5 Hz) was 0.04 nT. The out-of-band attenuation of the ADC digital 

filter is clearly visible above 0.45 Hz. The narrow transition band is indicative of the 

sophisticated filtering algorithms the Delta Sigma converter provides. 

FLUXGATE FLUXGATE

N1 N2

DIGITISER

1 SPS OUTPUT

∑

MEASURED

MAGNETIC

FIELD INPUT

+ -

CH1 CH2

< 10 m

separation

NT

FLUXGATE FLUXGATE

N1 N2

DIGITISER

1 SPS OUTPUT

∑

MEASURED

MAGNETIC

FIELD INPUT

+ -

CH1 CH2

< 10 m

separation

NT

2 1 2

1 2

22

T

T

N NN

NNN

≈≈

+=

241
Fluxgate and ADC Noise Power Spectral Density - 1 Hz Output Rate

-80.0

-70.0

-60.0

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

Frequency (Hz)

dB rel. nT2 / Hz 

Fluxgate NPSD

ADC NPSD

Fluxgate FFT based on 14 averaged 2048 point sections

giving total R.M.S noise = 0.04 nT (0.06 nT / √2)

Signal with period of approximately

2.6 minutes

ADC FFT based on 100 averaged 2048 point sections

giving total R.M.S noise = 0.0011 nT

Effective noise level to obtain 0.01 nT R.M.S

Action of Sigma-Delta ADC digital filter

Figure 2 Noise Power Spectral Density for Fluxgate and ADC

The total noise for two different channels of the fluxgate over three different 

measurement periods is summarised in Table 1. The temporal information of the total 

RMS noise shows that it is important to consider the length of time the noise 

measurement is made for, as this can produce different results for the noise, perhaps 

due to long term drift or temperature variation. The results in Table 1 also shows the 

additional noise contributed by the biasing circuitry (offset generator) necessary for 

limiting the Z and H components to within the instruments dynamic range of 

± 4000 nT. 

Channel 1 Minute 1 Hour 1 Day

Un-Biased Channel (D) 0.03 nT 0.04 nT 0.09 nT

Biased Channel (Z) 0.04 nT 0.06 nT 0.12 nT

Table 1 Summary of RMS Noise in Fluxgate Channels 

242
Frequency Response 

The method for determining the system transfer functions or frequency response is 

based on the assumption that the fluxgate magnetometer is a linear system, i.e., it 

exhibits homogeneity and additivity. Providing the system is linear, it can be fully 

described by its impulse response, i.e., providing an impulse signal or delta function 

on its input and measuring the response on the output. Since a perfect impulse 

represents all frequencies in the frequency domain, then the frequency response is 

simply the Fourier transform of the system impulse response. The basic apparatus for 

this test is show in Figure 3. Since generation of an impulse is technically more 

challenging, a step function can be used instead. The impulse response is simply the 

first derivative of the step response; digitally this is the first difference or Discrete 

Derivative (DD). The DFT of the impulse response is then used to produce the 

frequency spectrum.

A current generator was designed to deliver an idealised step of around 300 nT 

amplitude in the ambient magnetic field surrounding the fluxgate sensor using a set of 

external Helmholtz coils. The 1 Pulse-Per-Second (1PPS) TTL output of a GPS 

receiver was used to trigger the step precisely on the start of the second and also to 

provide very accurate time stamping for the ADC samples. This allows interpretation 

of the phase response of the fluxgate with out any introduced time offsets in the 

digital domain. The ADC was sampled at 200 Hz to allow analysis of the transfer 

functions up to 100 Hz. The inductance (~ 2 mH) and resistance (~ 74 ohms) of the 

test coils was such that the -3 dB point for the circuit current occurred at 5 kHz; i.e., a 

flat input spectrum with zero phase distortion in the 0 to 100 Hz test bandwidth. 

Figure 3 Frequency Response Test System

The DMI FGE fluxgate has a simple RC analog filter stage (cut-off frequency at

1.6 Hz) on the output that is used for suppressing unwanted high frequency noise. As 

GPS

STEP

GENERATOR

(BLACK BOX)

DELTA-SIGMA

DIGITISER

200 SPS

DFT

1 PPS

FREQUENCY

RESPONSE

DD

B

t

t

V

f

A

FLUXGATE

HELMHOLTZ

COILS

Timing and 1 PPS

STEP INPUT

STEP RESPONSE

GPS

STEP

GENERATOR

(BLACK BOX)

DELTA-SIGMA

DIGITISER

200 SPS

DFT

1 PPS

FREQUENCY

RESPONSE

DD

B

t

t

V

f

A

FLUXGATE

HELMHOLTZ

COILS

Timing and 1 PPS

STEP INPUT

STEP RESPONSE

243
the test system was over-sampled using a Delta-Sigma converter, this final analog 

stage can be omitted as the digital filtering of the converter will adequately suppress 

any signals out of the selected bandwidth of 0 to 0.5 Hz. For this reason the frequency 

response of the fluxgate was tested with and without the fluxgate RC filter for 

comparison (Figures 4 and 5). 

Figure 4 Amplitude Response of Fluxgate With and Without Output Filter

Figure 5 Phase Response of Fluxgate With and Without Output Filter

The amplitude response clearly shows an increase in signal power in the absence of 

the analog filter stage. The cut-off frequency (-3 dB point) is improved by an order of 

magnitude from just over 1 Hz to over 10 Hz. The phase response also shows more 

desirable characteristics when the filter is removed, with highly linear phase

corresponding to a small time correction of 16 ms, up to and beyond 10 Hz. With the 

filter included, the time delays are not constant, i.e., the phase is not linear above 

1 Hz.

0

50

100

150

200

250

300

0.10 1.00 10.00 100.00

Frequency (Hz)

Amplitude (nT)

Amplitude response (without RC)

Amplitude response (with RC)

Flatter spectrum up to 0.5 

Hz with RC filter removed

- 3 dB point is order of 

magnitude higher with RC 

filter removed

Cut-off Frequecny (- 3 dB)

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Frequency (Hz)

Phase (radians)

Phase response (without RC)

Phase response (with RC)

Equivalent time delay of 16 

ms for linear phase 

response up to 10 Hz

Non-linear phase introduced when 

analog

filter is used on output of fluxgate

244
Conclusions

The methods and hardware we developed and applied for establishing noise and

frequency response have proved to be a simple and effective testing procedure and

have delivered valuable results in ascertaining the current capabilities of our

instrumentation. The step generator device we developed is portable and provides a

quick method for establishing the frequency response of a fluxgate system. 

The results of the noise and frequency response tests of the DMI FGE version J

fluxgate were encouraging with a view to the anticipated INTERMAGNET standard

for 1 Hz data. The noise floor for an un-biased channel was as low as 0.02 nT/√Hz

@ 0.25 Hz, demonstrating how close this instrument already is to meeting the

proposed noise level of 0.01 nT/√Hz @ 0.25 Hz. The frequency response showed that

this type of fluxgate instrument is well suited for measurement of 1 Hz data and that

with the removal of the analog output filter, could potentially be used for measuring 

signal variations up to 10 Hz. 

Use of a Delta-Sigma ADC was well suited for testing fluxgate noise due to the

excellent filtering characteristics and high dynamic range. These same characteristics 

make it a good choice for general observatory data acquisition systems designed for

1 Hz data. The use of a Guralp seismic digitiser was convenient for testing purposes,

but provides surplus functionality and a more suitable alternative would be selected

for long term use in observatory systems.

The test procedure we employed to determine the noise floor contains several small

sources of error between instruments such as relative misalignments of axis, local

field differences and non-correlation of frequency dependant temperature coefficients.

For future testing some of these additional errors could be evaluated to give a more

accurate measure of the noise.

References

Love, J.J., 2005, 1-Second Operational Standard for INTERMAGNET, Minutes of the

OPSCOM/EXCOM INTERMAGNET MEETING, Mexico 2005, unpublished. 

245
Observatory Data: a 170-year Sun-Earth Connection 

Leif Svalgaard (leif@leif.org) 

Stanford University, CA 

Introduction 

The discovery of the sunspot cycle and the first 

results of the ‘Magnetic Crusade’ together made it 

clear that solar and geomagnetic activity are 

intimately related and that observing one is 

learning about the other [both ways]. 

Understanding of this magnificent relationship had 

to await more than a century of progress in both 

physics and observations, and only in the last few 

decades have we achieved the elucidation that in 

the middle of the 19th Century was so fervently 

hoped for: The lack of rapid progress so frustrated 

the observers [and their funding agencies] that 

many observatories were shut down or had 

operations severely curtailed, because as von 

Humboldt remarked in vol. 4 of his Cosmos: “they 

have yielded so little return in proportion to the 

labor that had gone into collecting the material”. 

The confirmation by spacecraft measurements of 

what workers in solar-terrestrial relations had so 

long suspected namely that a solar wind connects 

the magnetic regimes of the Sun and the Earth has 

finally brought about an understanding of one half 

of the relationship [activity] while the discovery of 

the ionosphere and measurements of solar 

ultraviolet and X-ray emissions have brought 

understanding of the other half [regular diurnal 

variation]. Today we have a quantitative 

understanding of these phenomena [although the 

microphysics is still debated] allowing us to model 

quantitatively the geomagnetic response to solar 

and interplanetary conditions. The immense 

complexity of geomagnetic variations becomes 

tractable by the introduction of suitable 

geomagnetic indices on a variety of time scales. 

Because different indices respond to different 

combinations of solar wind parameters we can 

invert the response and determine solar wind speed 

and density and interplanetary magnetic field 

strength from simple hourly mean values as far 

back as these are available, as we will show in this 

talk. In addition, the understanding of the 

ionospheric response to solar Far UltraViolet, 

allows us to infer FUV in the past as well, with the 

possibility of checking [and correcting] the 

sunspot number and calculating the Total Solar 

Irradiance. As geomagnetic variations have been 

monitored for ~170 years with [for this purpose] 

constant calibration, we have a data set of 

immense value for understanding long-term 

changes in the Sun. We argue that all efforts must 

be expended to preserve and digitize these national 

and scientific treasure troves. 

The Central Problem of Geomagnetic 

Variations 

The geomagnetic record shows a mixture of 

signatures from different physical processes: the 

regular daily variation, irregular short duration [1-

3 hours] variations, and ‘storms’ typically lasting a 

day or more. Geomagnetic indices have been 

devised to characterize and quantify these three 

types [ignoring special effects like pulsations, 

eclipse effects, etc]. An experienced observer can 

usually distinguish the various types from the 

general character of the curves and from hers/his 

knowledge of the typical variations at the 

observatory. Various computer algorithms more or 

less successfully attempt to supplant the need for a 

human, experienced observer, but in any case, the 

high-frequency part of the record is the necessary 

ingredient in the process. 

The Difficulty with the Regular Daily Variation 

Recognizing and quantifying the regular daily 

variation, what Mayaud called SR, is the main 

problem. The amplitude of this variation varies 

from day to day; near the focus of the current 

system, even the type of the variation changes 

from day to day. And at low latitudes the large 

summer vortex from the other hemisphere intrudes 

into the winter hemisphere. In deriving both the 

Dst index and the K range index, SR must be 

recognized and removed. We all know the 

problems associated with that, with the 

insufficiency of using the ‘5 Quiet Days’ as the 

basis for determining SR, and with the error of 

using an average ‘iron curve’, etc. The patternrecognition capabilities of experienced observers 

cannot be transferred to successors. 

Long-Term Geomagnetic Indices 

Mayaud’s heroic construction [1972] of the aaindex (back to 1868) is unlikely to be duplicated. 

The international cooperation and effort that are 

providing us with the ap (1932-), am (1959-), and 

Dst (1957-) indices cannot be replicated or 

extended into the past. It is difficult to gauge the 

long-term stability of the calibration of the range 

indices. The vast collection of 19th Century 

246
yearbook data seems useless to many people to the 

point where the data is not being preserved or 

digitized for modern processing methods. Here, 

I’ll show how these problems can be overcome and 

provide a rationale for the preservation and 

digitization of the yearbook data. 

IHV-index: Use of Night Hours Only 

Figure 1 shows the variation of the three 

geomagnetic components, H, D, and Z at FRD 

(Fredericksburg) during several days. The regular 

variation is clearly seen on every day including the 

day-to-day variation. Since the ionospheric 

conductivity is down by two orders of magnitude 

during local night, SR is effectively absent during 

the night hours. So, the solution to the problem of 

elimination of SR is simply to construct an index 

using only local night hours; by throwing away 

75% of the data, you remove 99% of the problem. 

Red boxes outline the local night. 

Figure 1: Variation of the geomagnetic elements 

at Fredericksburg (FRD) over several days. Red 

boxes show local night. The first day (May 11, 

1999) is the day when the solar wind famously 

‘disappeared’. 

Svalgaard and Cliver [2004, 2007a] introduced a 

new index based on this approach. The IHV-index 

(InterHourly Variability) is defined as the sum of 

the absolute values of the six differences between 

hourly values of any of the geomagnetic 

components [initially for H] for the seven hours 

spanning local midnight (generally falling within 

the 4th hour). In practice, we determine the number 

of hours to skip from 0h

 UT, before beginning to 

sum the following six hourly absolute differences. 

Local midnight is also the time where the 

correlation with interplanetary parameters 

maximizes. A most important detail is that hourly 

mean values are used, so that no high-resolution 

data is needed, and the vast store of yearbook-style 

data that exists can be brought to bear. 

Correcting IHV from Hourly Values to the 

Level of Hourly Means

Starting in 1905 Adolf Schmidt at Potsdam began 

to use Hourly Means instead of the Hourly point 

Values that had traditionally been reported in 

yearbooks. And soon most observatories adopted 

the new practice. [Some waited long, e.g. the 

French, who held out to 1972, before making the 

switch]. The instantaneous values read once every 

hour have larger variance which results in larger 

IHV. This is easily corrected for, e.g. by 

calculating IHV from hourly means [from the 60 

one-minute values] and from hourly point values 

and comparing the two IHVs. All early observatory 

data must be (and has been) so corrected. 

IHV is Strongly Correlated With the Am-index 

The best global activity index seems to the amindex [Mayaud, 1967] due to its excellent spatial 

coverage. There is a strong correlation (Figure 2) 

between IHV [blue] and the am-index [red]. For 

monthly means for FRD, we can calculate am

from the regression equation amcalc = 0.7475 IHV. 

Figure 2: Monthly means of the InterHourly 

Variability index (IHV) for FRD [blue curve] and 

the am-index for the first 2 intervals of the UTday [red curve]. The thin pink curve shows IHV

scaled by 0.7475 

Monthly means of IHV FRD and Am2

0

10

20

30

40

50

60

70

1970 1971 1972 1973 1974 1975 1976 1977 Year

Index value (nT)

The calculated am-index [pink] is a good proxy for 

am over the same six-hour interval [00-06 UT] as 

was used in the calculation of IHV. Using several 

stations at different longitudes, a global composite 

IHV can now be constructed. The correlation with 

am is very high (R2

=0.96 for monthly or 27-day 

rotation means), which means that we can 

reconstruct the am-index as far back as we can get 

IHV. 

Variation of IHV with Latitude 

For all (~120) stations that had [essentially 

complete] data during 1996-2003, we calculated 

the average IHV for each station over that interval 

and plotted it against corrected geomagnetic 

Am2

IHV

0.7475*IHV

247
latitude and found that IHV increases sharply in 

the auroral zones and we limit ourselves now to 

stations below 55° corrected geomagnetic latitude, 

for which the variation with latitude is very slight. 

Semiannual Variation of (Raw) IHV

IHV exhibits the ‘usual’ equinoctial semiannual 

variation [see e.g. Svalgaard et al., 2002, and 

references therein; O’Brien & McPherron, 2002]. 

This variation is well described by the ‘S’-function 

of the Earth’s dipole tilt, Ψ(doy,UT), against the 

solar wind direction: 

S(Ψ) = 1/(1 + 3 cos2

(Ψ)) 2/3 (1) 

We remove this purely terrestrial effect simply by 

dividing the raw IHV for each station by the Sfunction for that station at the day of year, ‘doy’, 

and UT time for every single IHV value. This 

makes it possible to combine records from stations 

at different longitudes regardless of data gaps. If 

desired, the S-function can be applied in reverse to 

add the variation back in. The fact that IHV shows 

the semiannual (including its UT component) 

variation so well attests to its efficacy and 

accuracy as a measure of global geomagnetic 

activity. 

Stations Used for Construction of IHV-index

As Figure 3 shows, we use 12 independent 

longitude [and North/South] “boxes” plus an 

Equatorial band [blue station symbols]. For each 

box, a reference station is shown in pink. IHV for 

all other stations in the box are normalized to the 

reference station and the average is computed for 

the box. Finally, each box is normalized to the 

European box [Reference station: Niemegk]. From 

now on we shall work with 27-day Bartels rotation 

averages for economy of presentation. The stations 

have been chosen for their long series of hourly 

mean values and the large number used makes IHV

robust and rather insensitive to minor errors in the 

data. 

Figure 3: Stations used in the construction of the IHV-index. In 12 regions distributed in latitude and 

longitude IHV derived for the stations [red dots] are normalized to a reference station [pink]. IHV for an 

equatorial region [blue] is also calculated and found to match the mid-latitude regions. Stations above 55° 

corrected geomagnetic latitude are not to be used. 

Composite Global IHV-index 

By averaging [with equal weight] all the 

normalized ‘box’ composites we arrive at a global

composite IHV-index that covers all UT hours. 

Figure 4(a) shows several years of the individual 

box-series to illustrate the consistent response 

from box to box. Note that there is no clear 

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 

seasonal difference between north [black] and 

south [red]. Using stations back to the First Polar 

Year in 1883 a composite IHV-index since then 

can be constructed. The result is shown in Figure 

4(b) including a 13-rotation running mean. Arrows 

show years with strong high-speed streams. 

248
0

20

40

60

80

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

IHV

IHV Composite

North

South

0

20

40

60

80

1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

IHV Composite

IHV

Figure 4: (a) A section of Bartels 27-day rotation averages of the IHV-index showing all Northern 

Hemisphere [black] and Southern Hemisphere [red] regional IHVs. (b) The composite IHV-index for each 

rotation since 1883 [grey] and the 13-rotation running mean [heavy black]. 

Comparison with Amplitude (Range) Indices 

We wish to compare the long series of composite 

IHV with the classical range indices, am, ap, and 

aa, in order to verify to what degree we have 

succeeded in producing a comparable index. Since 

IHV is freed from the semiannual variation we also 

divide the range indices by the S-function and then 

regress the Bartels rotation means against IHV.

The relationships are slightly non-linear (most so 

for the Ap-index), but are all highly significant 

(coefficients of determination R2

 are in excess of 

0.9). 

Figure 5: Observed [red] and calculated (from regression equations) [blue] 27-day rotation averages (top) of 

Am-index, (middle) of Ap-index, (bottom) of Aa-index. Heavier curves show 13-rotation running means. All 

indices have been freed from the equinoctial effect using eq.(1). 

249
For the Aa-index we have chosen to regress over 

the time since 1980 where there has been no 

change in aa-stations (and, hopefully, neither in 

procedures or calibration) 

We can now use these empirical regression 

equations [e.g. Am = 0.2375 IHV 1.2892, R2

 = 0.96] 

to calculate the classical indices for comparison 

with IHV: The result is shown in Figure 5, where 

heavy lines show 13-rotation running means. As 

expected, the fit to Am is excellent, so IHV is, 

indeed, an excellent proxy for Am. For Ap, there 

are times when the fit is less good. We interpret 

those as indications of inhomogeneities in the Apindex, and note that there is no systematic trend in 

the differences. 

For Aa, the calculated values [Aa = 0.36 IHV 1.1856, 

R2

 = 0.95] match well back to 1957, but before 

that time, the observed values of Aa fall 

consistently 3-4 nT below the values derived from 

IHV. A similar discrepancy has been reported by 

other groups [Jarvis, 2005; Mursula & Martini, 

2006; Rouillard et al., 2007] and must now be 

considered as established. It would thus seem that 

the aa-index is in need of a recalibration. 

Physical Meaning of IHV (and am, aa, ap) 

Geomagnetic activity as given by the three-hour 

am-index has been found [Svalgaard, 1978] to 

depend on solar wind parameters and the geometry 

of their interaction with the Earth as this: 

am = k (nV 2

) 1/3 (BV) q(α, f) S(Ψ) (2) 

where the various factors have meaning of 

Momentum flux, Magnetic Reconnection, and 

Geometric Modulation, and where B is the 

Interplanetary Magnetic Field (IMF) strength, V is 

the Solar Wind Speed, q is a function of the angle 

α between the IMF and the Earth’s magnetic field 

at the ‘nose’ of the magnetopause, and the relative 

variability f is defined as √(σBx2

 + σBy2 + σBz2)/σB. 

Figure 6 shows how good the fit is for individual 

three-hour intervals [red curves = calculated am; 

note the logarithmic-scale]. Only for very small 

values of am [<5 nT] where am is almost 

impossible to measure correctly do we have a 

persistent discrepancy: am, or rather Km, is too 

low. K = 0 is always a problem. 

During geomagnetic activity, magnetospheric 

particles are accelerated and precipitate into the 

upper atmosphere over the Polar Regions where 

the energy thus deposited can be directly measured 

by polar-orbiting satellites (POES). From the 

satellite data, the total energy input (in GigaWatt) 

to each hemisphere, Hp, can be estimated. Such 

estimates exist back to 1978 [Emery et al., 2008]. 

We find that IHV is directly proportional to the 

power input, Hp, to the upper atmosphere, Hp = 

0.68 IHV GW. 

Figure 6: Synthetic individual am 3-hour values 

calculated [red] from solar wind parameters 

using eq.(2) and corresponding observed am

values for six Bartels rotations. The scale is 

logarithmic to show how well calculated and 

observed values match at all scales. The match is 

poor for am < 5 nT where the index is very 

difficult to measure or where the coupling 

function is less valid. 

For intervals longer than three hours the variables 

are weakly correlated and the relation becomes 

slightly modified to am ~ BV 2. We would 

therefore expect a similar relationship for IHV. 

This is indeed what is observed: Figure 7(a): 

BVo

2

 = 4.34 (IHV - 6.5)

R2

 = 0.76

0

100

200

300

400

0 20 40 6 IHV 0 80 100

BVo

2 Rotation Means

Figure 7: (a) Correlation of all rotation means of 

IHV with BVo

2

 (where Vo is a shorthand for V in 

units of 100 km/s) as observed by spacecraft. 

250
that IHV is good proxy for BV 2. It is somewhat 

remarkable that am [based on K indices conceived 

so long ago] also is. 

In Figure 7(b) we show a comparison of observed 

(red] and calculated values (black) of BV 2, using 

the regression equation of Figure 7(a). It is evident 

0

100

200

300

400

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992

BVo

2

 obs

BVo

2

 calc

Figure 7: (b) Detailed comparison of observed and calculated [using the regression equation above] BVo

2

 for 

a twelve year interval, 1980-1992. 

u-measure (in nT, not the original 10 nT units) 

using only one hour (preferably the midnight hour 

if available) for as many stations as possible below 

51° corrected geomagnetic latitude: Figure 8 

shows yearly averages of the u-measure and IDV. 

During their time of overlap, the match is 

excellent. 

The IDV-Index, a Modern Version of the umeasure 

The IHV-index captures activity on a time scale of 

hours. How about on a time scale of days? Julius 

Bartels (building on work by Adolf Schmidt) 

defined the u-measure as the monthly (or yearly) 

mean of the unsigned differences between the 

mean values of the H-component on two 

successive days [Joos et al., 1952]. We found that 

you get essentially the same result using the mean 

over the whole day, a few hours, or only one hour. 

Our InterDiurnal Variability index IDV [Svalgaard 

& Cliver, 2005] is then simply the average 

Note that u and IDV did not register the strong 

high-speed streams in 1910, 1930, 1952, 1974, 

1994, and 2003. This (especially for 1930) was a 

deadly blow to the u-measure, and Bartels 

effectively dropped the index and went on to 

invent his much more successful K-index. 

Figure 8: Yearly average u-measures in 1 nT units [blue] and IDV-index [red] 

What is the IDV-index Measuring? The 

Interplanetary Magnetic Field Strength! u-measure itself have a very useful property: 

response to B only. IDV does not ‘see’ the high-speed solar wind. But 

there is a robust correlation with the IMF 

magnitude, B; see Figure 9(a). This is shown more 

explicitly on an event-by-event basis in Figure 

9(b). So instead of the u-measure being a failure, 

its modern equivalent, IDV, and therefore also the 

Coronal Mass Ejections (CMEs) add (closed) 

magnetic flux to the IMF. CMEs hitting the Earth 

create magnetic storms feeding energy into the 

inner magnetosphere (“ring current”). The Dstindex describes this same phenomenon, but only 

the negative contribution to Dst on the nightside is 

251
effectively involved. We therefore would expect 

(negative) Dst and IDV to be strongly related, and 

they are [R2

 = 0.89 for yearly averages]. We used a 

new derivation of Dst by J. J. Love back to 1905 

[Love, 2007]. Similar results are obtained with the 

Dst series by Karinen & Mursula [2005] (to 1932) 

or with the “official” Dst series (to 1957). Using 

regressions of IDV and Dst (< 0) on IMF B we can 

directly estimate B back to 1872. There is also a 

good correlation between B and the square root of 

the sunspot number, Rz, [Svalgaard et al., 2003; 

B = 0.44 IDV + 2.15

R2

 = 0.8832

V = 1.46 IDV + 431

R2

 = 0.0096

0

1

2

3

4

5

6

7

8

9

10

0 5 10 15 20

0

200

400

600

800

1000

1200

V

km/s

B

nT

nT

IDV Index

IMF B and Solar Wind Speed V as a Function of IDV

(yearly averages) 0

5

10

15

20

1960 1970 1980 1990 2000 2010

0

100

200

300

400

500

600

B

IDV

nT

IDV Independent of Solar Wind Speed

V

Figure 9: (a, left) Correlations between yearly values of B and V versus IDV. It is clear that there is a robust 

(R2

 = 0.88) correlation with B, but none with V. (b, right) Runs of V, B, and IDV since the beginning of the 

spacecraft era. Lack of matching response to V is shown by dark arrows, while matching responses to B are 

shown by pink arrows. 

Karinen & Mursula, 2006], we can infer B from Rz

as well. Can we go further back in time? Schmidt 

and Bartels had determined the u-measure from as 

early as 1836 onwards, but with less confidence 

before 1872. We thus have a measure of u and 

therefore of IDV (and then inferentially B) back to 

1836 [Figure 10]: 

0

1

2

3

4

5

6

7

8

9

10

1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

B nT Inferred IMF B since 1836

Year

From Bartels u-measure From sqrt(Rz) From IDV

Figure 10: IMF B determined by spacecraft [red], from IDV [blue], from Rz [green] and from the u-measure 

[pink]. A wavy, plausible long-term trend is indicated. 

Polar Cap Current and Polar Cap Potential 

Across the Earth’s polar caps flows a current in the 

ionosphere. This is a Hall current basically flowing 

towards the sun. The Earth rotates under this 

current causing the magnetic effect of the current 

to rotate once in 24 hours. This rotating daily 

effect is readily (and has been since 1883, see 

Figure 11(b)) observed as tracing out a circle in 

the X and Y component coordinates at polar cap 

magnetic observatories. The current derives from 

the Polar Cap Electric Potential which is basically 

the electric field (E = -VxB) in the solar wind 

mapped down to the ionosphere. The radius of the 

circle is a measure of the polar cap potential 

[Figure 11(a)] and is essentially the same for all 

stations within the polar cap. For stations near the 

polar cap boundary, the circle is only partial and 

exists only when the station is inside the polar cap. 

From the size of the circle during the spacecraft 

area we can calibrate the variation in terms of the 

product VB [Le Sager & Svalgaard, 2004]. 

IMF B obs S/C

252
-80

-60

-40

-20

0

20

40

60

80

100

-100 -80 -60 -40 -20 0 20 40 60 80

BLC

CBB

ALE

RES

THL

Vector Variation of Horizontal Components 1980-2004

 1882-1883 1911-1912 

Figure 11: (a, left) The average variation [over 1980-2004] of the end point of the vector from hour to hour 

(blue symbols) of the magnetic effect of the overhead current sheet for ALE (Alert), THL (Thule), RES 

(Resolute Bay), CBB (Cambridge Bay), and BLC (Baker Lake). Whenever a station is inside the polar cap it 

feels the effect of the uniform current seen by all. (b, right) Similar vector diagram for quiet days at KinguaFjord during the first Polar Year1882-1883 and for Cape Evans 1911-1912. 

An Over-determined System 

We now have three independent ways of 

estimating solar wind and IMF parameters: 

 1. The IHV-index, estimating BV 2

 2. The IDV-index, estimating B

 3. Polar Cap variation, estimating VB

These indices are readily computed from simple 

hourly means (or values) for which we have 

measurements stretching back into the early 19th

Century. We can thus estimate the solar wind 

speed, Vo = V/100 km/s, e.g. from 1 and 2, where 

V = √[(BV 2) / B] and use that value to calculate VB

for comparison with the estimated VB (green), in 

Figure 12. Although there are several second order 

effects, such as combined Rosenberg-Coleman and 

Russell-McPherron effects [e.g. Cliver et al., 

2004], polar cap conductivity dependence on solar 

activity, and decrease of the geomagnetic dipole 

strength, that contribute to the small discrepancies 

found, the agreement is quite remarkable and 

strongly suggests that the determinations of solar 

wind B and V in the past are well in hand. 

Figure 12: (a) Yearly values of B deduced from IDV and of Vo deduced from IHV (with B from IDV), blue 

curves, compared to spacecraft values, red curves. (b) BVo (blue curve) computed from B and Vo. 

253
The Floor in the Heliospheric Magnetic Field 

We can even do the analysis for a time scale of 

solar rotations, Figure 13. Note the ‘floor’ in B

[Svalgaard & Cliver, 2007b; Owens et al., 2008]. 

A B floor implies the existence of a time-invariant 

component of the open solar flux, suggesting that 

the Heliospheric magnetic flux consists of a 

constant open flux component, with a time-varying 

contribution from the closed flux carried by 

coronal mass ejections (CMEs), which provides 

the solar cycle variation in B. 

The return to the same value of B at each solar 

minimum means that flux added by CMEs must be 

balanced over the solar cycle, either by opening 

the closed flux via reconnection with open flux or 

by disconnecting an equivalent amount of open 

flux. The use of the treasure trove of hourly mean 

values has thus added important observational 

evidence to the modern discussion of Heliospheric 

magnetic field evolution; a point that would have 

delighted the early observers, as well as reminding 

us of the importance of preserving and digitizing 

the geomagnetic record. 

Figure 13 (a, top) Bartels rotation average B deduced from IDV (black) and measured by spacecraft (red). 

Note the ‘floor’. The green line is a 4th order polynomial fit to indicate an approximate smooth trend. Heavy 

curves show 7-rotation running means. (b, bottom) Same, but for Vo. 

0

2

4

6

8

10

12

14

1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

IMF B Inferred from IDV-Index (Rotation Avg.)

Using the Dayside Data 

It was known already to Rudolf Wolf in the 1850s 

that the amplitude of the diurnal variation of the 

Declination was a sensitive function of the sunspot 

number that he had just introduced [Wolf, 1859]. 

Figure 14(a) shows the clear difference between 

the variation of D at Praha (PRU) for sunspot 

maximum years (1957-1959) and for sunspot 

minimum years (1964-1965). As Figure 14(b) 

shows, this variation was well observed even back 

in 1840-1849. Wolf used this relationship between 

the amplitude of the variation and the sunspot 

number as an aid in calibrating the sunspot number 

calculated from observations by other observers 

for times before his own observations started in 

1849, and marveled: “Wer hätte noch vor wenigen 

Jahren an die Möglichkeit gedacht, aus den 

Sonnenflecken-beobachtungen ein terrestrisches 

Phänomen zu berechnen?”1

 This calculation can, 

of course, also be done in reverse and serve as a 

cross-check on the sunspot number calibration. 

The origin of these variations is the combined 

magnetic effects of ionospheric current vortices 

flowing in the E-region and of corresponding 

induced ‘telluric’ currents, created by dynamo 

action. Along the ‘flanks’ of the (external) 

vortices, the current flow is equatorwards on the 

morning side and polewards on the afternoon side. 

The magnetic effect at mid-latitudes of these 

currents at a right angle to the current flow is thus 

 

1 “Who would have thought just a few years ago about the 

possibility of computing a terrestrial phenomenon from 

observations of sunspots?” 

B obs

Floor

B = 1.67 IDV 0.6 nT 7-rot means

0

1

2

3

4

5

6

7

1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Solar Wind Speed Inferred from IHV and IDV

Vo 100 km/s

Vo = 1.6 [(IHV - 5) / IDV 0.6]

 1/2

7-rot means

Vo obs

254
East-West. As the “ring current” and the auroral 

electrojets and their return currents that are 

responsible for geomagnetic activity have 

generally North-South directed magnetic effects 

(strongest at night), the daytime variation of the Y 

or East component is a suitable proxy for the 

strength of the SR ionospheric current system. 

-10

-8

-6

-4

-2

0

2

4

6

8 Diurnal Variation of Declination at Praha

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

dD' 1840-1849

rD

-10

-8

-6

-4

-2

0

2

4

6

8 Diurnal Variation of Declination at Praha (Pruhonice)

dD' 1957-1959

1964-1965

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Year

Figure 14: (a) Diurnal and seasonal variation of the Declination at Praha (PRU). For each month the graph 

shows the local time variation (blue curves) and for the whole year (red). Sunspot maximum [1957-1959] is 

shown by darker colored curves, while sunspot minimum [1964-1965] is shown by lighter colored curves. (b) 

Same, but for the interval 1840-1849. The definition of the full range, rY, is shown by the arrow. 

The Declination can be converted to the East 

component using Y = H sin (D). The diurnal 

variation of Y is almost constant over a wide 

latitude range (20°-60°) and can readily be 

determined from hourly means. Using a large 

number of stations [Olso, Greenwich, Milan, 

Helsinki, Zi-Ka-Wei, etc] we can construct a 

composite series of the amplitude, rY, of the daily 

variation of Y from the 1840s until today, see 

Figure 15. The slight upwards trend is expected 

from the increase in ionospheric conductance due 

to the decrease of the geomagnetic dipole moment, 

and can be corrected for. The fact that the expected 

trend can even be detected, attests to the accuracy 

of the determination of rY. 

Trend = 0.0245 nT/year

R2

 = 0.79

0

10

20

30

40

50

60

1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

rY (nT)

Global Daily Range of Variation of East component for each Year Since 1841

Year

Observed

Figure 15: The variation since 1841 of rY derived from several stations as described in the text. The solar 

cycle effect is clearly seen. The minimum values (when no spots are present) show a slight increasing trend 

consistent with the increase of ionospheric conductance due to the declining geomagnetic dipole moment. 

Removing the trend results in the red curve. 

Corrected for Trend

255
Calibrating the Sunspot Number 

It is well-known that the strength of the SR current 

system is a sensitive function of the conductance 

of the ionosphere which in turn can be welldescribed by the 10.7 cm solar radio flux. In fact, 

we can translate rY directly into an equivalent 

f10.7 flux as shown in Figure 16(a), and plot the 

flux calculated from the regression equation for 

comparison with the observed flux in Figure 16(b). 

Figure 16: (a, top) Correlation between yearly 

averages of f10.7 radio flux and the diurnal 

range, rY, over the interval 1947-2005. (b, 

bottom) Comparison of observed f10.7 radio flux 

(red) and flux calculated from the above 

regression equation (blue). 

Because the f10.7 radio flux depends on the 

sunspot number we can turn the calculated flux 

into an equivalent sunspot number (Figure 17): 

Figure 17: Calculated (blue curve) yearly average 

International Sunspot Number, RI, and of 

Observed yearly average of RI, or RZ (red) and 

Group Sunspot Number (gray) RG, are shown 

from rY since 1841. Note the overlap between 

cycles. 

and discover that there are indications that the 

calibration of even the venerable sunspot number 

before ~1945 is questionable. Both the Zürich and 

the Group Sunspot Number are too low before 

1945 to account for the observed values of rY. The 

discrepancies correlate with Wolfer’s change of 

sunspot counting method at Wolf’s death in 1893 

and the beginning of the inexperienced Max 

Waldmeier’s tenure (1945) as the official keeper of 

the sunspot number. The impersonal and objective 

determination of rY overcomes the subjective 

element in determination of the sunspot number 

and can safeguard its long-term calibration, as 

Wolf so rightly realized. The implications of a 

reassessment of the sunspot series are wide 

ranging. At the time of writing this is ongoing 

work. Space does not permit further elaboration 

here, but a preliminary report can be found in 

Svalgaard [2009]. 

y = 5.9511x - 127.74

R2

 = 0.9726

0

50

100

150

200

250

0 10 20 30 40 50 60 70

F10.7

rY

1947-2005

0

50

100

150

200

250

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Reconstruction of Total Solar Irradiance 

As the sunspot number is often used as primary 

input to reconstructions of TSI, the Total Solar 

Irradiance, any re-calibration of the sunspot 

number series will impact TSI, and thus, through 

its use as a driver in climate models, the debate 

over climate change. Figure 18 shows a possible 

re-construction using a revised sunspot number 

series and compares it to several other current (and 

superseded – but still in use!) reconstructions. 

Figure 18: Several reconstruction of TSI (Total 

Solar Irradiance) from 1993 [Hoyt & Schatten] 

onwards. There is a progressive decrease with 

time of publication of the amplitude of the 

estimated variation. Modern reconstructions 

keep the variation of TSI within about 1 W/m2

. 

It is noteworthy that our reconstruction closely 

matches that of Preminger & Walton [2004] based 

on sunspot areas rather than sunspot numbers, and 

that the reconstructions over time have converged 

and now show a much smaller variation than 

initially thought, suggesting a much smaller 

impact on climate, unless the climate system is 

1362

1363

1364

1365

1366

1367

1700 1750 1800 1850 1900 1950 2000

Hoyt Lean Wang Preminger PMOD ACRIM TIM DIARAD

TSI Reconstructions & Composites

W/m2

Svalgaard Kri

F10.7 obs.

F10.7 calc from rY

0

50

100

150

200

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

R calc

Rz,i

Rg

18 19 20 21 22 23

v

0

1890

50

100

150

200

1895 1900 1905 1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960

R calc

Rg

13 14 15 16 17 18 19

Rz

0

1840

50

100

150

200

1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 1895 1900 1905 1910

R c

Rz

Rg

9 10 11 12 13 14

cal

256
implausibly hypersensitive to changes in solar 

output. 

Conclusions 

1: The hourly values in yearbooks are an 

extremely valuable data resource that allows us to 

calibrate our long-term geomagnetic and solar 

indices as far back as the geomagnetic record 

reaches. 

2: By combinations of newly derived geomagnetic 

indices we can infer the physical properties of the 

solar wind in the past. 

3. The availability of almost two centuries of 

reliable geomagnetic data has led to possible 

reassessments of several often-used indices of 

solar activity. 

4: Every effort should be expended to preserve and 

digitize the treasure trove of 19th and early 20th

Century hourly data. 

References 

Cliver, E.W., L. Svalgaard, & A. G. Ling, Origins of the 

semiannual variation of geomagnetic activity in 1954 and 1996. 

Annales Geophysicae, 22(1), 93-100, 2004. 

Emery, B. A., V. Coumans, D. S. Evans, G. A. Germany, M. S. 

Greer, E. Holeman, K. Kadinsky-Cade, F. J. Rich, & W. Xu, 

Seasonal, Kp, solar wind, and solar flux variations in long-term 

single-pass satellite estimates of electron and ion auroral 

hemispheric power, Journal of Geophysical Research, 113, 

A06311, doi:10.1029/2007JA012866, 2008. 

Joos, G., J. Bartels, & P. Ten Bruggencate, Landolt-Börnstein: 

Zahlenwerte und Funktionen aus Physik, Chemie, Astronomie, 

Geophysik und Technik, Astronomie und Geophysik, XVIII, 

795 pp., 331, Springer-Verlag, Berlin Heidelberg New York, 

1952. 

Jarvis, M. J., Observed tidal variation in the lower 

thermosphere through the 20th century and the possible 

implication of ozone depletion, Journal of Geophysical 

Research, 110, A04303, doi:10.1029/2004JA010921, 2005. 

Karinen, A. & K. Mursula, A new reconstruction of the Dst 

index for 1932–2002, Annales Geophysicae, 23, 475–485, 

SRef-ID: 1432-0576/ag/2005-23-475, 2005. 

Le Sager, P. & L. Svalgaard, No increase of the interplanetary 

electric field since 1926, Journal of Geophysical Research, 

109, A07106, doi:10.1029/2004JA010411, 2004. 

Love, J. J., Personal communication, 2007. 

Mayaud, P. N., Calcul preliminaire d’indices Km, Kn et Ks ou 

Am, An, et As, mesures de l’activité magnétique a l’échelle 

mondiale et dans les hémispheres Nord et Sud, Annales 

Geophysicae, 23, 585, 1967. 

Mayaud, P. N., The aa index: a 100-year series characterizing 

the geomagnetic activity, Journal of Geophysical Research, 77, 

6870, 1972. 

Mursula, K. & D. Martini, Centennial increase in geomagnetic 

activity: Latitudinal differences and global estimates, Journal 

of Geophysical Research, 111, A08209, doi:10.1029/ 

2005JA011549, 2006. 

O’Brien, T. P. & R. L. McPherron, Seasonal and diurnal 

variation of Dst dynamics, Journal of Geophysical Research, 

107(A11), 1341, doi:10.1029/2002JA009435, 2002. 

Owens, M. J., N. U. Crooker, N. A. Schwadron, T. S. Horbury, 

S. Yashiro, H. Xie, O. C. St. Cyr, & N. Gopalswamy, 

Conservation of open solar magnetic flux and the floor in the 

heliospheric magnetic field, Geophysical Research Letters, 35, 

L20108, doi:10.1029/2008GL035813, 2008. 

Preminger, D. G. & S. R. Walton, Inferring Total and Spectral 

Solar Irradiance From Sunspot Areas Only, American 

Geophysical Union, Fall 2004, abstract #SH53A-0304, 2004. 

Rouillard, A. P., M. Lockwood, & I. Finch, Centennial changes 

in the solar wind speed and in the open solar flux, Journal of 

Geophysical Research, 112, A05103, doi:10.1029/ 

2006JA012130, 2007. 

Svalgaard, L., Recalibration of the Sunspot Number and 

Consequences for Predictions of Future Activity and 

Reconstructions of Past Solar, Solar activity during the Onset 

of Solar Cycle 24, Dec. 8, 2008, Napa, California; 

http://sprg.ssl.berkeley.edu/RHESSI/napa2008/talks/MonI_Sval

gaard.pdf, 2009. 

Svalgaard, L., E. W. Cliver, & A. G. Ling, The semiannual 

variation of great geomagnetic storms, Geophysical Research 

Letters, 29(16), 1765, doi:10.1029/2001GL014145, 2002. 

Svalgaard, L., E. W. Cliver, & P. Le Sager, Determination of 

Interplanetary Magnetic Field Strength, Solar Wind Speed, and 

EUV Irradiance, 1890-Present, International Solar Cycle 

Studies Symposium, June 23-28, 2003, Tatranska Lomnica, 

Slovak Republic, Proceedings (ESA SP-535), 15, ed. A. 

Wilson. 2003. 

Svalgaard, L., E. W. Cliver, & P. Le Sager, IHV: A new 

geomagnetic index, Advances in Space Research, 34(2), p.436-

439, 2004. 

Svalgaard, L. & E. W. Cliver, The IDV-Index: Its Derivation 

and Use in Inferring Long-term Variations of the Interplanetary 

Magnetic Field Strength, Journal of Geophysical Research, 

110, A12103, doi:10.1029/2005JA011203, 2005. 

Svalgaard, L. & E. W. Cliver, The InterHourly-Variability 

(IHV) Index of Geomagnetic Activity and its Use in Deriving 

the Long-term Variation of Solar Wind Speed, Journal of 

Geophysical Research, 112, A10111, doi:10.1029/ 

2007JA012437, 2007a. 

Svalgaard, L. & E. W. Cliver, A Floor in the Solar Wind 

Magnetic Field, The Astrophysical Journal (Letters), 661, 

L203-L206, doi:10.1086/518786, 2007b. 

Wolf, R., Schreiben des Herrn Prof. R. Wolf an den 

Herausgeber, Nr. 1185, Astronomische Nachrichten, 50, 141-

144, 1859. 

Several of the above papers, as well as related material, can be 

found at the author’s website at http://www.leif.org/research

257
MAGIS: THE INFORMATION SYSTEM OF IPGP MAGNETIC 

OBSERVATORIES 

François TRUONG1, Xavier LALANNE1, Arnaud CHULLIAT1. 

1

Institut de Physique du Globe de Paris, 4 place Jussieu, 75005 Paris, France 

ABSTRACT 

The IPGP is in charge of twelve magnetic observatories, including 6 observatories run in 

cooperation with foreign institutions. These observatories are scattered all over the world. In order 

to improve collaboration between observers, daily maintenance and magnetic data processing, we 

created an information system where all technical information relevant to the network and all 

magnetic data in various stages of processing are archived. This system works like a community 

content sharing platform. It provides a simple way to access the technical information such as 

instrument characteristics, calibration data, observatory documentation, pictures, etc. The platform 

is now our only working tool for data loggers monitoring, routine scheduling, incidents logging, 

maintenance logging and absolute measurements collecting and pre-processing. The platform is 

based on web technology. This makes it independent from the local computer system of the user, 

simplifies software's updates and provides instant access to all technical information and magnetic 

data wherever the user is located. 

MOTIVATION AND GOALS 

IPGP observatories (including observatories in cooperation with other institutions) are 

distributed over five continents (Europe, Asia, Africa, South America and Oceania). About 30 

persons (observers) are performing absolute measurements and on site routine observatory work, 

involving more than hundred pieces of equipment such as magnetometers, data loggers, DI-flux and 

electronics boards. In order to share and easily access the technical data and the absolute 

measurement results, both by the technical team in the Chambon la Forêt observatory and by distant 

observatory teams, it was decided to create an information system based on web technology. 

The goals of this project are: 

- To monitor the observatory network in real-time 

- To keep track of regular maintenance, events, incidents and technical interventions 

- To store in one location all the relevant technical information 

- To share the data and the technical database 

- To streamline data processing 

TECHNICAL SOLUTION 

The starting point of MAGIS was WEBOVS, an information system developed by François 

Beauducel for the needs of IPGP’s Volcano and Seismological Observatory in Guadeloupe, an 

island located in the Eastern Caribbean Sea (Beauducel et al., 2004). WEBOVS scripts, which were 

originally designed for managing data feeds and technical data from hundreds of geophysical / 

geochemical sensors distributed over a single volcano, were adapted to the case of a network of 

magnetic observatories. 

The MAGIS website is opened only for authorized persons. It is hosted on a Linux server 

(Fedora Core) and scripts are written in PERL, SHELL and MATLAB. 

The magnetic data (variometer data and absolute measurements) as well as the technical data 

(on observatories, instruments, people, maintenance logs, etc.) are archived on a unique computer 

server: it confers simplicity to the system and makes it easier to correlate information. The platform 

is based on web technology. It makes it independent from the local computer of the user, simplifies 

software updates and provides user-friendly instant access. 

PRESENT FUNCTIONALITIES 

The MAGIS website is still under development. Its currents functionalities include: 

258
1. A technical database which can easily be updated, both by local observers and IPGP staff. The 

database provides access to the characteristics of the observatories and equipments in various 

formats such as texts, measurements, plots, pictures, schemes, manuals (Figure 1). There is one 

data sheet per observatory, one per magnetometer, one per theodolite, etc., related via hyperlinks. 

For example, the data sheet for a given magnetometer includes the results of its latest calibration. 

Every data sheet includes a maintenance / change log, which is regularly updated by users in 

distant observatories and in Chambon la Forêt. This way, everybody has access to full technical 

information at any time. 

Figure1: Main technical data sheet for the Addis Ababa (AAE) magnetic observatory (hyperlinks in green). 

259
2. A data bank containing 

all the magnetic data 

produced by the 

network, in the various 

stages of their 

processing (Figure 2). 

It also includes 

temporary baselines 

and environmental and 

technical parameters 

(temperature, power, 

etc.) 

3. A tool for entering absolute measurement results into the system and automatically computing 

temporary baselines (Figure 3). This proved to be very useful for quickly detecting anomalies in 

absolute measurements. 

Figure 2: Data bank of magnetic observatories. 

Figure 3: Absolute measurement form. 

260
Figure 4: Example of automatically computed statistics: 

Histograms of differences between absolute measurements and 

calculated baseline values. 

4. Various tools for monitoring the 

data quality in distant 

observatories, such as 

automatically computed statistics 

on absolute measurements 

(Figure 4), a table indicating the 

date of the last absolute 

measurement and that of the last 

data transmission. Such tools help 

the Chambon la Forêt team to 

quickly react in the case of a 

lowering of the data quality and / 

or transmission rate at one 

observatory. 

CONCLUSION AND PERSPECTIVES

The MAGIS website is step by step becoming the central working tool of the observatory 

network: observers process absolute measurements, and share maintenance logging on the website; 

baselines and provisional magnetic data are automatically computed. Our aim is now to transfer the 

whole magnetic data processing on MAGIS, including data checking and cleaning, calculation of 

definitive data, production of K-indices and data distribution to INTERMAGNET. 

ACKNOWLEDGEMENTS 

We are very grateful to François Beauducel for hosting one of us (François Truong) in OVSG in 

January 2007 and sharing the WEBOVS scripts and architecture. 

IPGP contribution 2477. 

REFERENCES 

Beauducel, F., C. Anténor-Habazac and D. Mallarino (2004), WEBOVS: Integrated monitoring 

system interface for volcano observatories, IAVCEI General Assembly, Chile, Nov 2004. 

261
Analysis of USGS One-Second Data 

E. W. Worthington1, E. A. Sauter2, and J. J. Love2

1

Geomagnetism Program, U. S. Geological Survey, College Observatory, 590 University Ave., Ste. D, 

Fairbanks, AK, USA 2

Geomagnetism Program, U. S. Geological Survey, PO Box 25046, MS 966, Denver, CO, USA 

One-second fluxgate magnetometer data are now being collected at most U. S. Geological Survey (USGS) 

geomagnetic observatories. As part of a process of validation, it is necessary to determine the resolution and 

noise content of the new data. Two different analysis methods are used in this study. The first method 

consists of analysis of second-to-second differences (first differences) in the data collected from a given 

magnetometer. The root mean square (RMS) of the differences is calculated for short running durations over 

the course of a day. The second method consists of side-by-side comparisons of data at observatories where 

two magnetometers are operated in parallel. Here, again, a running RMS of the difference is calculated over 

the course of a day. Results show that most of the data have a resolution of 0.01 to 0.02 nT. 

Introduction 

For the past 30 to 40 years geomagnetic 

observatory data have been reported as oneminute values. These data have been of great use 

to the scientific community, especially in the field 

of space physics. More recently, however, some 

of the focus of research has shifted towards the 

analysis of data with a higher time resolution. 

These data have mostly been obtained by 

magnetometer networks deployed during 

temporary campaigns pursued by academic 

scientists at universities or research consortiums. 

With the exception of a few observatories, such as 

Kakioka, Japan, most of the global observatory 

community has only begun to focus on the routine 

and long-term acquisition of one-second data. A 

survey of the space physics community, done for 

Intermagnet (Love, 2005), demonstrated a need 

for one-second data having a resolution of about 

0.01 nT and a timing accuracy of about 0.01 

seconds. 

This paper examines one-second data collected at 

USGS observatories where a new one-second 

acquisition system is used. Our analysis of the 

one-second data evaluates the resolution and 

noise level at the observatories where the data is 

being recorded. 

Data Description 

To better meet the needs of the space physics 

community, the USGS developed and deployed a 

new data acquisition system capable of recording 

one-second data. In order to obtain a combination 

of time-stamp accuracy and resolution, the Narod 

ring-core fluxgate magnetometer analog output is 

oversampled at 100 Hz. The analog outputs are 

passed through an anti-aliasing three-pole 

Butterworth filter having a corner frequency of 50 

Hz. This introduces only very minor frequencydependent-phase distortion to the signal and 

allows for the assignment of a time stamp with 

about 0.01 seconds accuracy. These samples are 

then digitized using an Ethernet-based, 24-bit 

A/D converter and passed through a Gaussian 

digital filter having a width of about two seconds. 

The result is a one-second average datum. Onesecond values are obtained for all three 

components of the magnetic field and are stored 

on an on-site computer. 

This acquisition system has been deployed at 13 

of the 14 USGS magnetic observatories with two 

at Boulder Observatory (BOU). At the College 

Observatory (CMO), the USGS system operates 

in parallel with a one-second system operated by 

another agency. This permits two different 

techniques for examining the noise and resolution 

of the USGS one-second data. 

Noise Analysis 

At observatories where there is a single fluxgate 

magnetometer in operation we examine the first 

difference to determine the root-mean-square 

(RMS) error. As a first step, the data for each 

component is plotted to check the integrity of the 

data. We want to make sure that there are no 

external spikes, offsets, or artificial disturbances 

in the data. The first difference (δH) or second262
to-second difference is computed between 

successive data points, of an individual 

component, over an entire day, 

 

δH(ti) = H(ti) – H(ti+1), for i = 1 to 86399. 

The mean and standard deviation of the first 

difference are computed over successive five 

minute intervals (600 points) throughout the day 

to determine the RMS error as a function of time 

of day (figure 1). 

Figure 1. Results of a first difference calculation 

for the H component from Honolulu for May 17, 

2008. This shows the raw data (top), the first 

difference (middle), and the RMS error of the first 

difference δH (bottom) as a function of time of 

day. In this example the noise level is about 0.01 

nT at its lowest value. 

At observatories with two one-second 

magnetometer acquisition systems in operation 

we can use a different technique to analyze the 

noise. At Boulder Observatory we have the 

primary system plus an identical secondary 

system operating on-site. At College Observatory 

there is an acquisition system operated by another 

agency that collects one-second fluxgate data of 

similar resolution. 

The data from the two systems are first checked 

for spikes, offsets, and artificial disturbances and 

to make sure the two data sets coincide over the 

same time interval. We then compute the 

differences between each fluxgate component 

from the two systems, 

ΔH(ti) = H1(ti) – H2(ti), for i = 1 to 86400. 

The standard deviation of these differences is 

computed to obtain an estimate of the RMS error 

for each component. Similar to the single 

magnetometer technique, we computed the RMS 

error over successive two minute (120 point) 

windows for the entire day (figure 2). 

Figure 2. This example shows data from the two 

data collection systems at Boulder, CO for May 

16, 2008. This shows the H component for 

Boulder (BOU) and the Boulder Secondary 

system (BDT) (top), the difference between the 

two systems, ΔH (middle), and the RMS error of 

ΔH computed over two-minute intervals (bottom). 

This analysis technique has the advantage that the 

regular variations of the magnetic field are 

removed and will not affect the results. What 

remains is the combination of the system noise of 

both data acquisition systems. 

263
Discussion 

Results using the single magnetometer analysis 

technique show that most of the USGS 

observatories have noise levels of about 0.01 nT 

RMS (table 1). We performed the analysis using 

several quiet days of data at each observatory to 

obtain an average value for the horizontal (H) 

component. The results for the D and Z 

components were similar to the H component. 

The first difference technique fails when the field 

is rapidly changing, with large amplitude 

changes. Large amplitude field changes, because 

of increased magnetic activity, obscure the 

amplitude of the magnetometer noise. 

Table 1. Noise test results for the H component 

for the USGS observatories 

Obsy RMS Noise (nT) 

Single Mag. Two Mags. 

BOU 0.010 0.017 

BDT .012 

BSL .009 

BRW .010 

CMO .012 .023 

FRD .018 

FRN .019 

GUA .014 

HON .011 

NEW .011 

SHU .013 

SIT .022 

SJG .015 

TUC .012 

The dual magnetometer analysis gives RMS noise 

levels that are larger than the single 

magnetometer technique. These RMS noise 

results contain the noise from both recording 

systems. In each case we first used the single 

magnetometer analysis technique to look at the 

noise from each individual system. The RMS 

noise level measured between the two systems 

should be the square root of the sum of the 

squares of the noise level from each individual 

system, 

 N1-2 = (N1

2

+ N2

2

)

1/2. 

Comparisons of data at Boulder are relatively 

easy to compare because the two systems are 

identical. The results from the comparison of the 

H components at Boulder give good results 

(figure 2). The RMS noise level between the two 

systems gave a result of 0.017 nT. Analysis of 

the two systems at Boulder using the single 

magnetometer technique resulted in noise levels 

of 0.010 RMS and 0.012 nT RMS (figures 3 and 

4). The combination of these two noise levels 

results in 0.016 nT which matches well with the 

dual magnetometer technique. 

Figure 3. Results of a first difference calculation 

for the H component from Boulder for May 16, 

2008. This shows the raw data (top), the first 

difference (middle), and the RMS error of the first 

difference δH (bottom) as a function of time of 

day. In this example the noise level is about 

0.010 nT at its lowest value. 

The comparison at College Observatory (CMO) 

requires more care. The second system at College 

Observatory is operated by the University of 

Alaska Fairbanks, Geophysical Institute (GI). 

They use the same fluxgate magnetometer that the 

USGS uses, but their data recording system is 

different than the USGS system. The GI samples 

the analog output of the magnetometer at 8 Hz 

and uses a boxcar average to determine an 

average one-second datum. The boxcar average 

is not centered on the minute. We applied a 1 to 

300 mHz band-pass filter to both data sets to 

compensate for different filter settings and 

acquisition parameters between the two data 

recording systems. 

264
Figure 4. Results of a first difference calculation 

for the H component from Boulder secondary 

system (BDT) for May 16, 2008. This shows the 

raw data (top), the first difference (middle), and 

the RMS error of the first difference δH (bottom) 

as a function of time of day. In this example the 

noise level is about 0.012 nT at its lowest value. 

The comparison at College Observatory, for the H 

component, produced a noise level of 0.026 nT 

after accounting for a one-second time difference 

between the two data sets (figure 5). The random 

spikes seen in the H difference are of unknown 

origin. Analysis of each individual system shows 

that the USGS data has a lower RMS noise level 

than the GI system, 0.012 nT and 0.021 nT 

respectively. 

Conclusion 

The two analysis techniques were successful in 

estimating the noise levels of our one-second 

data. The single magnetometer analysis works 

well when the magnetic field is in a quiet, 

undisturbed condition. When the magnetic field 

is in a disturbed condition, this technique breaks 

down. At all but two of our sites, this is the only 

analysis technique presently available because 

there is a single magnetometer in operation. 

Figure 5. This example shows data from the two 

systems at College, AK for May 17, 2008. This 

shows the H component for USGS College 

magnetometer (CMO) and the UAF-GI College 

magnetometer (CIGO) (top), the difference 

between the two systems, ΔH (middle), and the 

RMS error of ΔF computed over two-minute 

intervals (bottom). 

There was a concern that the RMS error 

determined from this analysis method might be 

latitude-dependent. We plotted the RMS error for 

each observatory in a stack plot as a function of 

latitude (figure 6). In looking at the noise levels 

we see that the results have little variation with 

latitude. At the two high latitude observatories, 

College and Barrow (BRW), the noise level is on 

the order of 0.01 nT which is very similar to the 

noise level at most of the other observatories. 

The difference is that the field is more active at 

the higher latitudes but when the field is in a quiet 

state the first difference technique can produce 

satisfactory noise level determinations. 

265
The dual magnetometer technique is successful 

when comparing data from identical systems. It 

is probably a better measure of the noise level 

because it removes magnetic field variations from 

the analysis. When comparing data from different 

acquisition systems the technique is not as useful 

because of differences between the acquisition 

systems and magnetometers. 

The RMS noise levels from all of the reported 

observatories are on the order of 0.02 nT or less 

(Table 1). About half of the observatories have 

noise levels of about 0.01 nT. 

Acknowledgements 

We wish to thank the University of Alaska, 

Fairbanks, Geophysical Institute for supplying 

data from their magnetometer at the College 

Observatory. 

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Figure 6. A stack plot of the RMS error of the H 

component for eight USGS observatories on May 

17, 2008. The graphs are arranged from high 

magnetic latitude at the top to low latitude at the 

bottom. 

266
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