ASSESSMENT OF PRESENT ELECTROMAGNETIC TECHNIQUES 

FOR THE LOCATION OF TRAPPED MINERS 

John Durkin 

U.S. Department of Interior 

Bureau of Mines 

Pittsburgh Research Center 

Pittsburgh, PA 15236 

ABSTRACT 

Field studies have been conducted in a large number of coal mines 

throughout the U.S. to determine the effectiveness of electromagnetic 

techniques in locating men trapped underground following a mine disaster. 

Data from these tests have been used to generate models of expected 

signal and noise distributions as found above mines throughout the coal 

fields. These distributions have aided in placing the expected performance of a through-the-earth electromagnetic comnunications technique 

into a probabilistic framework. Results indicate an expected 45% 

probability of detecting a miner's signal from a depth of 1,000 ft, a 

depth which exceeds 903 of the coal mines within the U.S. and a 

90% probability at a depth of 500 ft, a depth which exceeds 50% of 

the mines. 

INTRODUCTION 

Following the Farmington mine disaster in 1968, the Bureau of Mines 

contracted with the National Academy of Engineering (5)* to recomnend 

means to increase the probability of survival and rescue of miners in 

mine disasters. Their report recommended that the Bureau develop new 

comniunication techniques to detect and locate trapped miners. The 

Bureau considered these recommendations as the starting point for 

a continuing concentrated research effort to improve survival and 

rescue capabil i ty. 

The condition of a mine after a disaster is unpredictable, cables 

may be cut and passages blocked. A hardened communication system 

that advances with the working face would be prohibitively expensive 

and might be inoperative when needed. Detection and communication 

with miners trapped underground requires signaling from the surface 

througti the niine workings or directly through the strata. 

* Number in parentheses refer to items in the list of references at 

the end of this paper. 
Two major areas in rescue conmunications have been investigated: 

(a) Electromagnetic (EM) and (b) seismic. This paper examines the 

potential effectiveness of the EM signaling system and is particularly 

concerned with the statistical analysis of experimental magnetic field 

strength data taken at 94 coal mine sites well distributed over the U.S. 

coal fields. The objective of these tests was a data gathering venture 

intended to provide the necessary information for studies of probable 

effectiveness of the EM signaling system prior to initiating the fornulation and promulgation of new regulations bearing on the use of such 

a system. 

During the last decade theoretical studies and field tests indicated that the best chance of success rested in a system that consisted of an underground narrowband transmitter and a surface receiver, 

either used in a helicopter or hand carried by surface personnel, 

that would both detect the signal and locate its origin. Figure 1 

illustrates the principle of operation of the EM trapped miner signaling 

device and associated surface receiving hardware. The transmitter 

and receiver were developed by Coll ins Radio.* (1 ) An improved version 

has recently been built by General Instrument Corp. (GI) (3) and is 

shown in Figure 2. 

In practice, the miner would attach the belt-worn transmitter to a 

cap-lamp battery and deploy 300 ft of #1S copper wire around a coal pi1 lar, 

ideally in a square configuration. If he cannot deploy the loop around a 

coal pillar, it can be deployed along a passageway where attempts are 

made to maximize the 1 oop area. The transmitter del ivers to the 1 oop a 

pulsed square wave voltage of 630, 1050, 1950, or 3030 Hz of 100 ms duration and a 10% duty cycle. The established EM field is then detected by 

surface personnel. Once the signal is detected, the surface crew deploys 

a large loop of wire coupled to a high power audio amp1 ifier that can be 

used for transmitting voice communications to the trapped miner. The underground transmitter contains a baseband receiver to receive the voice signal s from the surface, and the miner can respond in code by keying the 

transmitter off and on. Figure 3 shows the life expectancy of the caplamp battery when operating the transmitter. The range of values was obtained when using an old battery with 8 hr discharge to an upper bound 

where a new battery with no discharge was used. A 2-ohm resistance was used 

as a load on the transmitter for these results. From this it can be seen 

that the transmitter can be expected to operate from approximately 2 to 4 

days. 

The objective of the field tests was twofold: First, to define a 

signal transmi ssion measurement and analysis program to obtain a re1 iabl e 

data base for characterizing the signal transmission properties of overburdens 

in the U.S. coal fields; and second, to use this data base to predict the 

1 i kel ihood of successful performance of the EM trapped niiner signal ing 

* Use of brand names is for identification purposes only and does not 

imply endorsement by the Bureau of Mines. 
system. The mines sampled for these tests were selected from a population of a1 1 coal mines on the basis of both the overburden depth and 

the number of miners employed at the mine. The sample reflected 

concern both for for the physical dependence of signal penetration 

on depth and the number of miners exposed to potential disasters within 

each depth interval. Figure 4 shows the cumulative distribution of 

mines as related to the maximum depth and demonstrates that approximately 90% of the coal mines within the U.S. are less than 1,000 

ft deep. These tests were performed by Westinghouse (8) and Bureau personnel. The analysis of the data as presented in this paper was 

performed by personnel of Arthur D. Little Inc. and the Bureau; details 

of this work can be found in an excellent report by Lagace et a1 . (4) 

EARTH TRANSMISSION LOSS 

In order to predict the surface signal level as produced from 

an underground transmitter, it is necessary to understand the expected 

loss the signal incurs when transmitted through-the-earth. Unfortunately, the geological structure of the overburden above coal mines differs 

from mine to mine which gives rise to differing electrical conducting 

properties. Therefore, for a given mine depth it can be expected that 

signal transmission characteristics will vary significantly from mine 

to nine. Consequently, one must rely on statistical information of 

the earth's transmission loss. A major objective of the 94-mine field 

test was to obtain enough data of the earth's transmission loss, as 

found over a large population of coal mines, to confidently characterize expected signal loss from an underground transmitter. 

To ensure success in obtaining these data during the field tests, 

magnetic moments of the underground transmitter were used that were 

artificially higher than would be expected from a transmitter to be 

used by a miner. Therefore, in most cases, an earth transmission loss 

value for each mine was obtained. A strategy could then be formed to 

predict the expected surface signal strength based upon a given magnetic 

moment . 

The RMS values of the vertical magnetic field, Hz, of all of the data 

taken were normalized to a transmitter magnetic moment of M = 1 ~m~-mz. 

Since the surface field is proportional to the magnetic moment of the 

underground transmitter, the expected level of the surface fie1 d for 

a given transmitter could then be found by accounting for the actual 

moment used. Following this normal ization,, statistical studies were 

performed to relate the surface field strength and mine depth at each 

frequency tested. 

Each normalized data point can be denoted as Si where the subscript 

i represents the specific frequency and the subscrip$'j represents the 

specific depth of test for each mine. Thus, each surface measurement 

Sij taken can be considered as a single observation of signal strength 

at a predetermined frequency and overburden depth level at a particular 
mine. The selection of the mines tested was done on a statistically 

based random sample to assure that Si j could be described by a common 

normal probabil ity law. 

Several linear regression models were hypothesized and tried. The 

model found to best fit the behavior of the data is one in which the mean 

value of the normal ized signal strength Si j is linearly related to the 

logarithm of overburden depth. This is shown in equation 1: 

Sij = cii + Bi log (depth) + &ij (1 

Here Sij is the normal ized vertical magnetic field signal strength 

(expressed in dB re 1pA/m-RMS for the i th frequency and depth j for 

a transmit moment of M = 1 ~m~-m2. 

The parameters cij and bi are parameters to be estimated from the data, 

where depth is known in meters. The parameter ci represents a random 

variable that is normal ly distributed, with expected val ue zero and variance 

oij2 which is the same for all values of j. 

The derived regression lines for each of the four frequencies are 

plotted in Figures 5-8. It is apparent visually that the log-linear relationship is an appropriate one and the RZ statistic, a measure of goodness of 

fit, supports this observation. 

Two types of intervals have been estimated from the data. One is known 

as a confidence interval, which is defined as a range of values computed 

from the sample that can be expected to include the true (but unknown) mean 

val ue with a known probabil i ty. Figures 9-12 display 95% confidence interval s 

with dashed lines. To illustrate this concept using Figure 9, it follows 

from the field experiment that the probability is 0.95 and that the interval 

from -6 dB to -12 dB includes the true mean normal ized signal strength 

for a transmitter of magnetic moment M = 1 ~m~-mZ at 630 Hz and an overburden depth of 190 ft. 

While the confidence interval represents a probabil ity statement 

about a mean value over many trials, it is also of interest to quantify 

the expected outcome of a single trial. For example, what signal strength 

could be expected if a test were conducted at a predetermined frequency 

and overburden depth? This situation is depicted by prediction intervals 

also plotted in Figures 9-12. To illustrate this concept, again using 

Figure 9, the probability is 0.95 that another test performed at 630 tlz 

at a depth of 500 ft would yield a signal strength between -49 dB and 

-22 dB. Also plotted in Figures 9-12, for comparison, is a curve of the 

free space vertical field strength that would be measured on the surface 

in the absence of the lossy overburden media. 

Figure 13 summarizes the normal ized average overburden response as a 

function of depth and frequency by plotting the four regression lines and 

the free space curve on one graph. This figure shows that the frequency 

dependence of signal strength is re1 atively insignificant for depths 
less than 500 ft, and that the change across the band is only about 

10 dB even at the maximum depth of 1500 ft. 

These summary normal ized overburden response plots , together 

with the confidence and prediction levels of this section, can be 

used to generate estimates of signal strength produced on the surface 

above coal mines as a function of overburden depth and operating 

frequency for transmitters having any prescribed magnetic moment versus 

frequency characteristics in the 630 to 3030 band. This is assuming 

for a fixed magnetic moment, the size of the transmitting loop has 

no influence on the surface field. 

GENERAL INSTRUMENTS TRANSMITTER 

Using the results of the previous section on earth transmission 

loss statistics, the expected surface signal strengths produced by the 

recently developed GI transmi tter can be predicted by computing the 

expected magnetic moment at each frequency and then translating each 

of the overburden response curves of Figure 13 upwards by an amount 

equal to the values of the magnetic moment expressed in dB re to 

1 ~mp-m2. 

The GI loop antenna consists of 300 ft of #18 copper wire, arranged 

in the shape af a square. This loop configuration was chosen because it 

best represents the practical implementation of the strategy that the 

miners will be instructed to follow. Figure 14 shows the predicted 

surface field strength when the GI transmitter is used. Also shown are 

the expected magnetic moments for each frequency. 

SURFACE EM NOISE 

Two independent sets of magnetic field noise measurements were 

obtained during the course of the measurement program. The first was 

by Westinghouse using the Collins' man-carried receiver. This set 

of data was found to be in error due to intramodulation problems caused 

by the simultaneous measurement of discrete man-made noise and broadband 

noi se. The second set of data was obtained by a Bureau team performing 

similar tests at 27 of the mines tested. The Bureau's data were 

gathered on tape and later analyzed in the laboratory. 

For purposes of signal detectability, the RMS value of the 

vertical magnetic field is of interest. The statistical distribution 

of this noise, using the Bureau data base, at each frequency for a 

receiver bandwidth of 30 Hz is shown in Figures 15-18. 

SURFACE SIGNAL-TO-NOISE RATIO 

In the previous sections, the behavior of signal data and noise 

data obtained in this study have been characterized by statistical 

re1 ationships. To develop an understanding of detection probabil i ty 

it is necessary to characterize the probability distributions of the 

surface RMS signal -to-noi se ratio (SNR) at each frequency. 
The independence of signal and noise distributions, in addition to 

the property of normality exhibited by each distribution, permit 

straightforward combination of the two distributions to generate SNR 

probabil ity estimates. By the central 1 imit theorem the sum (or 

difference) of two normal ly and independently distributed variabl es 

is a1 so normally distributed. 

The SNR distributions are conveniently plotted using normal probability 

paper. This paper is designed so that the mean is plotted at the 50 percentile point and the mean plus or minus one standard deviation is 

plotted at the 16 or 84 percentile point, respectively. Such normal 

probability plots derived are given in Figures 19-22 for five different 

overburden depths at each of the four frequencies. 

These four figures provide a straightforward method to estimate the 

probability of having various SNR in actual practice. The vertical axis 

represents the area under the normal curve from minus infinity to some 

SNR Ro and provides the probability of achieving a SNR less than or 

equal to R,. 

It is instructive to observe the behavior of probability estimates 

associated with exceeding a given SNR as a function of overburden depth and 

frequency. Figure 23 gives the probability of the RMS signal being 

at least greater than RMS noise. Note that the best performance occurs 

in the upper part of the frequency band even though more loss occurs 

through-the-earth at the higher frequencies and the magnetic moment 

is smaller at the higher frequencies. This can be explained due to 

the rapid decrease in surface noise level as frequency increases. 

SIGNAL DETECTION CRITERIA 

The success of a rescue effort when using a trapped miner transmitter rests on the ability of surface personnel to confidently detect the 

signal from the underground transmitter. The pulsed signals from the 

underground transmitters are detected by searchers carrying rescue 

receivers equipped with a handheld loop antenna and headsets. The mode 

of detection is aural , based on the headset signal s perceived by the 

ear. It is then necessary to establish a relationship between the 

nature of the signal, SNR, and the probability of aural signal detection. 

The aspects of the signal that influence detection are (a) frequency 

(b) signal length, and (c) signal repetition. The primary aspect 

of the noise for detection considerations, besides the level of the 

noise, is the noise bandwidth. How each of these parameters affects 

the signal detection capability must be understood, then their results 

can be combined to generate a probability of detection curve as a 

function of SNR. 

The present receiver mixes the received signal with an internal 

oscil 1 ator to a higher frequency for purposes of narrowband fi 1 tering , then mixes the filtered signal again to present a listening signal 

of 978 Hz to the operator. The ability to detect a tone masked by 

broadband noise as a function of frequency has been studied by Urick (7). 
In this work, an entity known as critical bandwidth evolved. When 

the ear listens for a tone it acts as a narrowband filter centered 

at the signal frequency. The masking of the signal by the noise will 

only be influenced by the noise within this critical band. Noise 

outside this band will have no influence on signal detectability. 

Figure 24 illustrates the critical bandwidth values as a function 

of frequency and shows that the critical bandwidth is approximately 

60 Hz at the 978 Hz 1 istening frequency of the rescue receivers. 

The effect of the length of a pulsed tone is shown in Figure 25. 

Psychoacoustic data taken by a number of investigators are combined 

in this figure to show the "recognition differential" required versus 

pulse length for a 50% probabil ity of detection. The recognition 

differential is the amount in dB by which the signal level needs to 

exceed the measured noise spectrum level (noise level in 1 Hz within 

critical band of interest) to provide a 50% probability of detection. 

The GI transmitters have a fixed pulse duration of 100 ms which 

prescribes a recognition differential of 23 dB to achieve a 50% probability of detection. To determine the significance of the 23 dB 

recognition differential in terms of required SNR, a bandwidth must 

be chosen. The bandwidth used in the receiver is 30 Hz, one-half 

of the critical bandwidth of the ear at the listening frequency. 

It is assumed here that the noise reaching the operator's ear is 

produced solely by the receiver and that the attenuation provided 

by the headset is sufficient to justify disregarding ambient acoustic 

noise in the area of the operator. Studies (6) have shown that systems 

with bandwidths approximately one-half the critical bandwidth will 

behave in the same manner detection-wise as those having a system 

bandwidth equal to the critical bandwidth. Therefore, for purposes 

of the trapped miner system, a SNR of 23 -10 log 30 = I dB is needed 

to yield a 50% probability of detection. 

A final factor affecting detection is the signal repetition rate. 

Garner (2) provides data on the effect of the repetition of a pul sed 

tone on signal detectabil i ty. Figure 26 il lustrates these findings. 

This v~ork shows that as the repetition rate of a 50 ms pulse of a 

1000 Hz signal is changed from 1 in 4 seconds to 1 per second, 2 dB 

less SNR is required. Reviewing Garner's data would indicate that 

an even greater improvement might be expected for the trapped miner 

transmitter, but the lack of data at repetition rates less than 1 

per 4 seconds precludes a guarantee of this. Therefore, a conservative 

2 dB improvement value will be used. The 50% probabil ity of detection 

SNR criterion of (8-2) dB or 6 dB, will be used. 

This work quantifies the necessary SNR to establish a 50% detection 

probability. It is also necessary to extend this work to determine 

detection probabilities at any other SNR. The results of this extension 

are shown in Figure 27. This plot can be used with the earlier established 

expected SNR for the underground GI transmitter to establish signal 

detection probabil ities. 
PROBABILITY OF DETECTION ESTIPIATES 

In an actual mine emergency situation many factors will influence 

the actual a b i l i t y t o rescue the miner. Time of arrival of the 

rescue team, life expectancy of the miner, search times, and operation 

time of the underground transmitter are only a few of the factors that 

have a bearing on the success of the rescue effort. This report 

has not discussed these points but rattler has investigated the detection 

probability for arl existing signal as being measured by a rescue team 

in an area which in general is directly over the trapped miner. 

Even within this measurement there are factors such as geology, noi se, 

and depth that influence the probability of success. However, 

though these factors may not enable us to state the success of this 

measurement in a deterministic manner, we can, as outlined in this 

paper, quantify our chances in a probabilistic framework. 

The probability of detection curve in Figure 27 actually represents 

a conditional probability; that is, the likelihood that detection 

will occur given the presence of a fixed RMS SNR. As a consequence, 

the chance of detecting a signal trarlsmi tted through the earth can 

be calculated according to: 

PID and itk} = PIRk} x PI DIP,^} (2) 

idhere PID and Rk} represents the probability of achieving a SNR of size Rk 

and also detecting the signal embedded in the noise. PIKk} is the probability of the occurrence of a SNR of size Rk and PID/R~} is the conditional 

probability of detecting a signal given a SNR of size Rk. 

Figure 27 gives PIDIR~}, and Figures 19-22 show the SNR probability 

distributions. The latter figures show that these probabilities depend 

on frequency and depth. Using additional subscripts to account for these 

parameters, the probabil ity of achieving a SNR of size Rk and detecting the 

signal transmitted from a depth i at frequency j is 

'ijk ID and Rk} = pij IK~} x PIDIR~} 

However, the primary result is the expected probability of detecting 

a signal transmitted at a specified depth for a known frequency summed 

over all possible Rkls. This is given as 

The results of these calculations present, as shokdn in Figure 28, 

the expected property of detection estimates for the GI transmitter 

signals as measured over all the U.S. coal fields. 
This paper has discussed the results of an extensive field testing 

program to evaluate the performance of the EM trapped miner transmitter. 

Analysis of these data has enabled one to place into a probabilistic 

framework the ability to confidently detect the signal from the 

underground transmitter. Resul ts indicate that the probabil i ty of 

detecting this signal is 45% at a depth of 1,000 ft, a depth which 

exceeds 90% of the mines within the U.S. and a 90% probabil ity at a 

depth of 500 ft, a depth which exceeds 50% of the mines. This information 

is vital for the future formulation and promulgation of new regulations 

written for the use of the EM system. Studies are currently underway 

to improve the detection capability by providing signal processing capability to the receiver. Future work will look at a systems approach when 

using this technique. This study will investigate each element involved in 

a successful rescue effort, such as search strategies, life expectancies, 

etc. Coupled with the results discussed in this paper, a thorough understanding of the effective implementation of the EM system will be obtained. 

REFERENCES 

1 . Col 1 ins Radio, "Waveform Generator--Package and Receivers, " USBPI, 

Final Report, Contract H0242010. 

2. Garner, W. I?. , "/\uditory Thresholds of Short Tones as a Function 

of Repetition Rates," JASA, vol. 19, No. 4, July 1947, pp 600-608. 

3. General Instrument Corporation, "Design to Specification and 

Fabrication of VF Transmitters and Baseband Receivers," USBM contract 

J0395017. 

4. Lagace, K., J. Dobbie, T. Doerfler, 1.1. Hawes and R. Spencer, 

"Technical Support of Through-the-earth Transmission Measurement 

Arthur D. Little, Inc., USBM contract J018COG7, Final Report, 

June 19b0. 

5. National Academy of Engineering, Mine Rescue and Survival, Bureau 

of Mines, Open File Report 4-70, !larch 1970. 

G. Principles and I\pplications of Underwater Sound, Summary Technical 

Report of Division 6, NDRC, vol. 7, Washington, D.C., 1946 (revised 

1968). 

7. Urick, R. J., "Principles of Underwater Sound for Engineers," 

HcGraw-ti i 1 1 , 1967. 

8. Westinghouse, "Re1 iabil i ty and Effectiveness Analysis of the USBM 

Electromagnetic Location System for Coal Mines," USBM contract 

J0166060, Final Report, December 1978. 

1'1'1~ 

SURFACE VERTICAL MAGNETIC FIELD, H1. VERSUS 

10 MOMENT M-1 Amp-m' 

0 - 

- REGRESSION LlNE 

m LOG S, =99.83 -61.97 LOG D 

- R' = 83% 

-20 - 

-30 - 

-40 - 

- ? -OBSERVED DATA VALUES 

-50 - 

DATASOURCE: WESTINGHOUSE - DEPTHfFEETI 

1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 

LOGIDEPTH IN METERS1 

FIG. 5. Uplink normalized overburden signal 

data and linear regression log (dept

at 630 Hz. 

SURFACE VeRTlCALMAGNETIC FIELD, Hz. VERSUS LOG lDEPTHl FOR 

A TRANSMITMOMENT M-1 Amp-m' 

10 -1 \ 

0 - 

-10 - 

- mLOGS, =111.83-88.31 LOGD 

-JO R' =84% - 

-* - 0 -OBSERVED DATA VALUES -50 - b0 - 

-m - 

FIG. 7. Uplink normalized overbu rderl slynai I capv

data and linear regression log (depth) m

at 1950 Hz. 
mh NORMALIZED TO TRANSMIT MOMENT OF M-1 Amg-rn' 4 95% PREDICTION INTERVAL 

REGRESSION LlNE - 

FREE SPACE CURVE 

- 

I 

a - w 

a -40 - 95% PREDICTION 

0 INTERVAL 

w 

Y 

-100 I I I I1 111 11 1IIIIII 

100 150 200 250 300 500 700 lo00 1500 

OVERBURDEN DEPTH(FEET! 

FIG. 9. Uplink regression results for 

630 Hz normalized vertical 

signal strength, Hz, vs. depth. 

I I I 1 1 ' II I I1 IlI,II' 

NORMALIZED TO TRANSMIT MOMENT OF M=l Amp-ma - 

96% PREDICTION INTERVAL 

REGRESSION LlNE - 

FREE SPACE CURVE - 

- 95% PREDICTION INTERVA 

OVERBURDEN DEPTH(FEET) 

FIG. 11. Uplink regression results for 

1950 Hz normalized vert '-cal 

signal strength, Hz, vs. depth. 

)(I NORMALIZED TO TRANSMIT MOMENT OF M=1 Amp-m' -I 

- REGRESSION LlNE 

- 

FREE SPACE CURVE 

- 

95% PREDICTION 

- INTERVAL 

- 

95% CONFIDENCE INTERVAL - 

-100 

100 150 200 250 300 500 700 loo0 1500 

OVERBURDEN DEPTH(FEETI 

FIG. 10. Uplink regression results for 

1050 Hz normalized vertical 

signal strength, Hz, vs.depth. 

FIG. 12. Uplink regression results for 

3030 H> normalized vertical 

signal strength, Hz, vs. depth. 
-lM 

150 2W 250 300 400 500 700 1000 1500 2

OVERBURDEN DErTd IFEETI 

FIG. 13. Normalized overburden respon

curves--uplink regression 

results, average surface 

vertical signal strength, Hzvs. overburden depth by 

frequency. 

L 

40 - FOR MHz RECEIVER BANDWIDTH 30 - 

20 - 

0 - 

lo - 

102 5 10 20304050607080 90 95 9899995999 

CUMULATIVE NORMAL PROBABILITY I%l 

FIG. 15. Statistical distribution of RM 

surface noise at 630 Hz. 
.rl r L 

C, C, 

cn rd .d 

u a, 

cn rl .d 

rd 0 0 C .d 

C, a, cn 0 

4 rd -P 'Ird r C,3 

m cn 

Ro = S/N RATIO IN (dB) 

Ro - S/N RATIO IN (dB1 
CUMULATIVE NORMALPROBABILITY (%) 

Pr ISIN S Ro dB) 

FIG. 21. Cumulative probability distribution of S/N ratios expected above 

U.S. underground coal mines at 

1950 tlz. 

FIG. 23. Probability that mean RMS signal is 

greater than or equal to RMS noise + 

9 dB for the General Instruments 

lo., 

09 

08 

07 

06 

4 

S o 0.5 

0.4 

0.3 

0.2 

01 

0 

0 

CUMULATIVE NORMAL PROBAB

Pr (SIN 4 Ro dB) 

1-w "7 6 8 gS$gS 

, , . 

- FREQUENCY (Hz1 - 

- D.-O 3030 Hz 

- 

- 

- 

- 

- 

- 

- DATA SOURCES 

- - SIGNAL (WESTINGHOUSE) 

- NOISE (BUREAU OF MINES) 

- 

- 

- 

- - 

t"'llLI~IIIrII1lIrlIIIr~~l~~a 

250 500 750 loo0 1250 1500 

FIG. 22. Cumulative proba

tion of S/N ratio

U.S. underground 

3030 Hz. 

OVERBURDEN DEPTH (FEET) 

l 

o NDRC Summ Tech Rept 9, Flg 17, p 

+ Schofer et ol 

HOwk~ns and Stevens 

Froquoncy, HZ 

500 

Y 

I 

5- 200 

SOURCE: R.J. URICK, "PRINCIPLESOF UNDERW

McGRAW HILL - 1967. FIG. 12.10. 

FIG. 24. Measured values 

0 

B Stevens 

X 100. 

A 

- 

.- 

- .- 

L 

u 

- a Zwlcker, Flottorp, ond Stevens 

bandwidth of th

x SwetS, Green, and Tamer 

been, B~rdsal 1, ond Tamer 

- - Eye averoge of values other thon - ?hose of Zw~cker. Flottap,and 

transmitter.