Prepared in cooperation with U.S. Bureau of Reclamation and National Park Service

Airborne Electromagnetic and Magnetic Survey Data of 

the Paradox and San Luis Valleys, Colorado

By Lyndsay B. Ball, Benjamin R. Bloss, Paul. A. Bedrosian, V.J.S. Grauch, and Bruce D. Smith

Open-File Report 2015–1024

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2015 

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its natural and living resources, natural hazards, and the environment—visit 

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Suggested citation:

Ball, L.B., Bloss, B.R., Bedrosian, P.A., Grauch, V.J.S., and Smith, B.D., 2015, Airborne electromagnetic and 

magnetic survey data of the Paradox and San Luis Valleys, Colorado: U.S. Geological Survey Open-File Report 

2015–1024, 19 p., http://dx.doi.org/10.3133/ofr20151024. 

ISSN 2331-1258 (online)

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

Abstract ...................................................................................................................................................................... 1

Introduction................................................................................................................................................................. 1

Paradox Valley........................................................................................................................................................ 2

San Luis Valley ....................................................................................................................................................... 2

Purpose and Scope .................................................................................................................................................... 3

Airborne Electromagnetic Survey ............................................................................................................................... 8

Data Processing...................................................................................................................................................... 8

Inversion of Electromagnetic Data ........................................................................................................................ 10

Depth-of-Investigation........................................................................................................................................... 10

Aeromagnetic Survey ............................................................................................................................................... 10

Data Processing.................................................................................................................................................... 11

Geophysical Digital Data Overview........................................................................................................................... 11

Flight Lines............................................................................................................................................................ 12

Grids ..................................................................................................................................................................... 12

Line Data............................................................................................................................................................... 13

Section Data ......................................................................................................................................................... 14

Section Plots ......................................................................................................................................................... 14

Acknowledgments..................................................................................................................................................... 18

References Cited...................................................................................................................................................... 18

Appendix 1A ............................................................................................................................................................link

Appendix 1B ............................................................................................................................................................link

Appendix 1C............................................................................................................................................................link

Appendix 1D............................................................................................................................................................link

Figures

1. Maps showing the location of geophysical surveys for the Paradox Valley and the San Luis Valley............. 5

2. Map of the Paradox Valley survey area......................................................................................................... 6

3. Map of the San Luis Valley survey area ........................................................................................................ 7

4. An example of the oscillating noise signal ..................................................................................................... 9

5. Example inverted resistivity depth slice between 21 and 28 m depth from the 19-layer SCI inversion 

for the Paradox Valley ................................................................................................................................. 13

6. Example inverted resistivity depth slice between 46 and 57 m depth from the 19-layer SCI inversion 

for the San Luis Valley................................................................................................................................. 14

7. Example of inverted resistivity section plot for the Paradox Valley .............................................................. 16

8. Example of inverted resistivity section plot for the San Luis Valley.............................................................. 17

Tables

1. Line number organization for various survey line types ................................................................................. 4 

2. Digital data organization and description for files and folders...................................................................... 12
iv

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

square meter (m2) 0.0002471 acre

square kilometer (km2) 247.1 acre

nanotesla (nT) 1 gamma

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Electrical conductivity is given in millisiemens per centimeter (mS/cm) unless otherwise specified.

Electrical resistivity is given in ohm-meters (ohm-m) unless otherwise specified.

Electrical resistivity (ρ, ohm-m) can be converted to conductivity (σ, mS/cm) as follows:

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD88).

Horizontal coordinate information is referenced to the North American Datum of 1983, Universal Transverse Mercator projection, Zones 12 

north (Paradox Valley; UTM12N) and 13 north (San Luis Valley; UTM13N) .

Initialisms Used in This Report

AEM airborne electromagnetic

DEM digital elevation model

DOI depth-of-investigation

EM electromagnetic

FEM frequency-domain electromagnetic

GPS global positioning system

GRSA Great Sand Dunes National Park and Preserve

IGRF International Geomagnetic Reference Field

LCI laterally constrained inversion

NPS National Park Service

SCI spatially constrained inversion

TDS total dissolved solids

TEM time-domain electromagnetic

USBR U.S. Bureau of Reclamation

USGS U.S. Geological Survey

Abbreviations Used in This Report

Rx receiver

Tx transmitter

ρ

σ

10 =
1

Airborne Electromagnetic and Magnetic Survey Data 

of the Paradox and San Luis Valleys, Colorado

By Lyndsay B. Ball, Benjamin R. Bloss, Paul A. Bedrosian, V.J.S. Grauch, and Bruce D. Smith

Abstract 

In October 2011, the U.S. Geological Survey (USGS) contracted airborne magnetic and 

electromagnetic surveys of the Paradox and San Luis Valleys in southern Colorado, United States. 

These airborne geophysical surveys provide high-resolution and spatially comprehensive datasets 

characterizing the resistivity structure of the shallow subsurface of each survey region, accompanied by 

magnetic-field information over matching areas. These data were collected to provide insight into the 

distribution of groundwater brine in the Paradox Valley, the extent of clay aquitards in the San Luis 

Valley, and to improve our understanding of the geologic framework for both regions. This report 

describes these contracted surveys and releases digital data supplied under contract to the USGS.

Introduction 

In October 2011, the U.S. Geological Survey (USGS) contracted airborne electromagnetic

(AEM) and magnetic surveys over portions of the Paradox and San Luis Valleys in southern Colorado, 

United States (fig. 1). The two survey areas were chosen to dovetail with ongoing geologic research and

water-resource management activities at agencies within the U.S. Department of Interior, including the 

USGS, U.S. Bureau of Reclamation (USBR), and the National Park Service (NPS). 

Developing accurate conceptual subsurface geologic and hydrogeologic models is challenging 

but necessary to responsibly manage groundwater resources. Direct-sampling techniques, such as 

drilling, provide invaluable information about subsurface hydrogeologic properties. However, these 

techniques are inherently one-dimensional and lend limited insight to the three-dimensional geologic 

framework or the spatial extent of sampled hydrogeologic conditions. Geophysical methods are 

important tools for continuing geologic interpretations between drill holes and below surficial sampling 

locations. Airborne geophysical methods, in particular, provide greater aperture and spatial continuity

than is practically feasible with direct sampling or ground-based geophysical surveys. The airborne 

platform also allows collection over environmentally sensitive areas or difficult-to-access terrain. 

The AEM geophysical methods used in these surveys characterize the electrical resistivity of 

subsurface materials to depths of up to 300 m, as well as the local variability in the Earth’s magnetic 

field. Variations in electrical resistivity are highly sensitive to variations in the concentration of total 

dissolved solids (TDS) in groundwater; small percentages of which can dramatically decrease bulk 

resistivity. Resistivity is also sensitive to the presence of clay or other conductive minerals and to 

variations in grain-size distribution. Because of these sensitivities, electrical resistivity data are 

especially advantageous for mapping groundwater salinity and the distributions of clay-rich formations

within the shallow subsurface. Additional constraints on interpretations of geologic structures that may 

have hydrogeologic significance, such as the locations of faults, are provided by the magnetic field 

variations. 
2

Paradox Valley

The Paradox Valley in western Colorado is one of several northwest-trending, elongated, 

collapsed salt anticlines within the Paradox Basin, a region defined by extensive Pennsylvanian 

evaporites of the Paradox Formation of the Hermosa Group (Weir and others, 1983) (figs. 1A and 1B). 

The inflow of relatively fresh groundwater through the overlying unconsolidated Quaternary deposits 

dissolves the Paradox Formation salt, which comprises the core of the Paradox Valley anticline. This 

dissolution has led to the development of a sodium-chloride-dominated brine plume in the central part 

of the Paradox Valley, where groundwater TDS concentrations exceed 250,000 milligrams per liter 

(mg/L) (Watts, 2000; Chafin, 2002).

The Dolores River, a tributary to the Colorado River, flows across the axis of the Paradox Valley

and acts as a natural groundwater discharge location. The Dolores River experiences substantial 

increases in salinity as it intercepts the brine, with historical TDS loads estimated to be about 115,000 

tons/year (Chafin, 2002). Salinity is a primary water-quality concern in the Colorado River Basin, 

resulting in costly damages to industrial, municipal, and agricultural systems each year. 

Under the Colorado River Basin Salinity Control Act, the USBR constructed and operates a

salinity control project, the Paradox Valley Unit, to reduce salinity loads to the Dolores River. The 

project consists of a series of shallow pumping wells designed to intercept the brine before it flows into 

the river. The produced brine is treated and injected into confined Precambrian and Paleozoic rocks at 

around 4.5 km depth. The control project began production operation in 1996 and has succeeded in 

reducing river salinity, with an estimated 90-percent reduction in annual TDS loads as of 2001 (Chafin, 

2002). However, the injection-well system is nearing the end of its useful life, and the USBR is 

exploring alternative strategies to reduce the salinity loads to the Dolores as well as alternative disposal 

methods. The USGS is assisting the USBR in these efforts through the development of a variabledensity groundwater flow model and supporting hydrogeologic characterization. 

The sensitivities of AEM data to changes in TDS and lithologic composition make resistivitybased interpretations ideal for defining the shallow geometry of the brine plume and refining the 

geometry of hydrostratigraphic units in freshwater regions of the valley. In October 2011, the USGS 

contracted an AEM and aeromagnetic survey of the Paradox Valley. Data were collected over 1,200 line 

km through a series of northwest-trending block lines paralleling the axis of the valley with a nominal 

spacing of 150 m and a nominal altitude of 40 m above the ground (fig. 2). Tie lines were collected 

perpendicular to these block lines with 2-km spacing. Two smaller blocks were flown in addition to the 

main valley block: northwest of the valley surrounding Buckeye Reservoir and extending into eastern 

Utah, and a small area near the downstream reach of the Dolores River on the northeastern margin of 

the Paradox Valley. This dataset can be used to define the electrical resistivity of the subsurface to 

depths approaching 300 m in parts of the basin. These data provide a new and spatially comprehensive 

view into the hydrogeology of the Paradox Valley and are intended to help improve the predictive 

certainty of the groundwater flow model. 

San Luis Valley

Agricultural and ranching communities in San Luis Valley, south-central Colorado, depend on 

groundwater that resides in a geologically complex alluvial basin that is part of the northern Rio Grande 

rift (figs. 1A and 1C). This area of the valley is a particular focus because the development of effective 

water management strategies requires an improved understanding of the local hydrogeology and 

geology, particularly in the context of continued drought conditions and recent legal actions to obtain 

water rights at Great Sand Dunes National Park and Preserve (GRSA). The GRSA and surrounding area 

were chosen as an airborne geophysical survey site to investigate several aspects of the hydrogeologic 
3

framework controlling groundwater flow: (1) regionally extensive clay aquitards, (2) uplifted basement 

blocks that obstruct regional groundwater flow, (3) buried volcanic layers that can impede or facilitate 

groundwater flow, and (4) intrabasin faults that offset the clay or volcanic layers. 

The primary objective of the airborne survey is to map the extent and depth of regionally 

extensive clay aquitards that separate basin sediments into upper unconfined and lower confined 

aquifers. Wells drilled into the lower aquifer are strictly regulated, and knowledge of the extent and 

depth of the clay layers is limited. In addition to its hydrogeologic significance as a regional confining 

unit, the clay layers are important markers in studying the interaction between sedimentation of Lake 

Alamosa and faulting of the Rio Grande rift. The clay layers were deposited within Lake Alamosa, a 

Pliocene and Pleistocene lake that covered much of the modern San Luis Valley and experienced 

multiple transgressions and regressions over its roughly 3-million-year lifetime (Machette and others, 

2013). 

The airborne platform is particularly advantageous for surveying the GRSA where the sand 

dunes make ground surveys infeasible. AEM data were collected over 1,600 line km in a series of block, 

tie, and reconnaissance lines (fig. 3). The majority of data (1,494 line km) were collected over a 36.5- 

by 20.5-km area with nominal 300-m line spacing within the block and 3-km spacing between tie lines. 

An additional 105 line km were flown as reconnaissance lines collected south of the San Luis Valley 

block (fig. 3). Lines were nominally flown 30 m above ground. 

The AEM survey was designed to follow up on ground geophysical results and supplement other 

airborne geophysical surveys conducted by the USGS in the area (Bankey and others, 2004; Drenth and 

others, 2009, 2013; Fitterman and Grauch, 2010). In particular, resistivity models derived from groundbased time-domain electromagnetic (TEM) data collected prior to the AEM survey revealed a pervasive, 

electrically conductive layer corresponding to a distinctive section of massive clay found in water wells 

in the area (Fitterman and de Souza Filho, 2009). The main block of the present AEM survey was 

designed to develop a more comprehensive view of this electrically conductive layer and tie the 

information to the few deep water and oil-exploration wells located in the area. The one north-south and 

four east-west reconnaissance lines, located south of the main block (fig. 3), were designed to connect 

the main block to several geologic and geophysical features of interest, including a well-studied core 

hole located at the west end of the southernmost east-west line (Rogers and others, 1992). 

Purpose and Scope

This report releases digital geophysical data provided to the USGS for the Paradox and San Luis 

Valleys. The methodology used to collect and process these data is presented in the Airborne 

Electromagnetic Survey and Aeromagnetic Survey sections. The data and file structure are explained in 

the Geophysical Data Overview section. Geologic or hydrogeologic interpretations of the AEM or 

ancillary data are beyond the scope of this data release and are not presented in this report. 
4

Table 1. Line number organization for various survey 

line types for the Paradox and San Luis Valleys 

airborne geophysical surveys.

Line type Line number range

Paradox Valley

Block line (main valley) 100202–106302

Block line (Buckeye Reservoir) 150101–152401

Tie line (main valley) 200101–200801

Tie line (Buckeye Reservoir) 250101–250301

Block line (Dolores Canyon) 350101–351301

Reconnaissance line 600100–600103

Calibration line 990101–990106

San Luis Valley

Block line 100101–110801

Tie line 200201–200401

Reconnaissance line 300101–300501

Calibration line 990101–990106
5

Figure 1. Maps showing (A) location of geophysical surveys for (B) Paradox Valley and (C) San Luis Valley and 

Great Sand Dunes National Park and Preserve. Paradox Valley bedrock geology summarized by K. Watts (USGS, 

written commun., November 2013).
6

Figure 2. Map of the Paradox Valley survey area.
7

Figure 3. Map of the San Luis Valley survey area.
8

Airborne Electromagnetic Survey

Subsurface resistivity structure can be estimated using a variety of approaches, including 

electromagnetic (EM) methods. EM systems transmit an electromagnetic field from a transmitter (Tx) 

loop or coil, inducing an electric current within the Earth. The interaction of the Earth with the induced 

current is dependent on the subsurface resistivity and gives rise to magnetic and electric fields that can 

be sensed by one or more receiver (Rx) loops or coils. EM systems can be deployed from aircraft by 

mounting Tx and Rx loops to a rigid frame. Ancillary positional information is obtained via additional 

sensors on the air frame and aircraft, including one or more high-precision global positioning systems 

(GPS) for geospatial positioning, a laser altimeter for altitude, and one or more inclinometers for 

attitude of the air frame. 

Two basic types of AEM systems, both operating on the same physical principle, are commonly 

used for hydrogeophysical investigations. Frequency-domain electromagnetic (FEM) systems transmit a 

continuous sinusoidal current and measure the earth response in the “on-time,” while current is passing 

through the Tx loop. TEM systems transmit pulses of current and measure the earth response in the “offtime” over a series of time gates following the end of current transmission, when no current is present in 

the Tx loop. Generally speaking, FEM systems have superior near-surface resolution, whereas TEM 

systems have a greater depth of investigation. For the Paradox and San Luis Valleys, the depth of the 

targeted hydrogeologic features led to the selection of a TEM system. The helicopter-borne SkyTEM

system was flown under contract by SkyTEM Surveys ApS (Aarhus, Denmark) in October 2011. A 

detailed description of data acquisition and SkyTEM system parameters are documented in the SkyTEM 

Acquisition Report (appendix 1A). 

Data Processing

AEM data for both survey areas have undergone contractor-performed data processing and 

inversion to generate resistivity models of the subsurface (discussed in the next section). The contractor 

base-station corrected and filtered the ancillary positioning data. These data were used to calculate the

Tx and Rx geographic location and height above ground, to correct for frame tilt, and to generate a 

digital elevation model (DEM). Raw EM data were stacked to a 10-hertz (Hz) sample rate, normalized 

with respect to system parameters, noise filtered, and merged with the processed positioning data. 

Processed EM and magnetic data are provided in the LINEDATA folder for each survey area. These 

processing steps are described in detail in the SkyTEM Acquisition Report (appendix 1A). 

In addition to the standard processing workflow described above, SkyTEM performed surveyspecific processing for both the San Luis and Paradox Valleys. During inspection and processing of the 

AEM data, a detrimental and time-varying noise signal was observed in the high-moment EM data for 

both survey areas. This noise can be observed as an oscillating behavior in the normalized Rx response 

across all high-moment time gates with a variable period ranging over several seconds (fig. 4). SkyTEM 

has attributed this noise to 30-Hz mechanical vibrations from the generator transferred to the Rx coil 

through carrier ropes (discussed in detail in the SkyTEM Acquisition Report and supplemental SkyTEM 

Technical Note, appendixes 1A and 1B). The broader periodicity of this 30-Hz noise signal is likely the 

artifact of generator power variations over time and changes in rope tension with movement of the air 

frame. The effect of this noise on resistivity inverse models is to create geologically unrealistic 

alternating resistors and conductors at depth. SkyTEM developed a survey-specific processing routine to 

attempt to remove this noise from the high-moment Z-coil data. The following excerpt from the 

SkyTEM Acquisition Report (p. 33, appendix 1A) describes this routine: 
9

“In an effort to determine and subtract the time varying, noise-generated gate offsets, an 

automated sinusoid fitting and subtraction tool was introduced and applied to the individual 

soundings prior to stacking. Assuming a constant noise frequency of 30 Hz, while using only 

the last four gates to calculate least squares fit (in terms of amplitude and phase), it was 

possible to remove a significant part of the problematic oscillations. The most important 

prerequisite for the successful application of this noise compensation technique is that the 30 

Hz noise signal must be the dominating signal contribution for the last 4 gates. This requires 

that the earth response is negligible in order for the sinusoid fit to be unbiased, while 

unrelated noise contributions will likewise tend to worsen the fit.” 

Although this technique improved data quality and effectively compensated for the obvious 

noise impacts on the inversion models (discussed in the Inversion of Electromagnetic Data section), 

there is also the potential for distortion of late-time data, particularly in conductive areas where the 30-

Hz noise may not be the only contribution to the measured response at late-time gates. This is 

particularly relevant with respect to the electrically conductive Paradox Valley brine plume and the 

Lake Alamosa clay deposits of the San Luis Valley, leading to potential uncertainty in the interpretation 

of brine plume and clay layers using these noise-corrected data.

As an alternative to SkyTEM’s noise-correction procedure, Aarhus Geophysics ApS performed a 

separate processing and inversion schematic (Aarhus Geophysics Report, appendix 1C). The Aarhus 

approach involved a combination of automated and manual filtering and averaging, followed by heavy 

manual culling of the affected late-time data with iterative evaluation of the result through inversion. 

Although this technique avoids the possibility for distortion of some late-time data and may produce 

more reliable inverse models in conductive regions than the correction technique used by SkyTEM, it 

also results in a reduction in the total depth-of-investigation (DOI) (defined in the Inversion of 

Electromagnetic Data section) of the inverted model section. This processing routine is described in 

detail in the Aarhus geophysical reports for Paradox and San Luis Valleys (appendixes 1C and 1D).

Figure 4. An example of the oscillating noise signal observable between all time gates (from figure 14 of the 

SkyTEM Acquisition Report, appendix 1A). pV/Am4, picovolts per Amps-meter4
10 

Inversion of Electromagnetic Data

Inversion is used to estimate the distribution of resistivity with depth at each processed data 

location, or sounding. Inversions for each survey area were performed using two datasets under the 

contract: SkyTEM inverted the noise-compensated Z-coil data and Aarhus Geophysics (Aarhus, 

Denmark) inverted the uncompensated but culled Z-coil data. The inversion program Aarhus 

Workbench (HydroGeophysics Group, University of Aarhus, Denmark) was used by both contractors. 

Workbench performs a regularized, damped, least-squares inversion for one-dimensional (1-D) 

multilayer data. SkyTEM used the laterally constrained inversion (LCI) approach of Auken and others 

(2005) using a 30-layer model. The LCI approach links 1-D models at neighboring soundings together 

along flight lines via regularization that enforces lateral continuity. Aarhus Geophysics used a spatially 

constrained inversion (SCI) approach with a 19-layer model where model parameters for a given 

sounding are spatially dependent on their neighbors; the regularization constraint between neighboring 

soundings is scaled according to the inverse distance between neighboring models. Each inversion 

approach is described in detail by the contractors in their respective reports (appendixes 1A, 1C, and 

1D). These two data processing and inversion approaches have resulted in differences in resistivity 

structure that may influence interpretation. Both inversion models are released with this report 

(SECTIONDATA directory for each survey area). The 19-layer SCI inversion based off the 30-Hz 

noise-culled data is recommended as the most applicable inversion for both study areas, and these 

inverted section data are also released as depth-slice grids (GRIDS directory) and visualized section data 

(SECTIONPLOTS directory). However, the most effective processing and inversion strategy for a given 

part of the survey area is dependent upon the resistivity structure, the local area of interest, the original 

quality of the data at those locations, and the intended interpretive use. 

Depth-of-Investigation

The strength of measured earth response, and therefore the quality of data, is a function of dataacquisition parameters, flight altitude, recording time following current turn-off, and subsurface 

resistivity. Calculated depth-of-investigation is a valuable tool for evaluating the reliable depth to which 

an inverse model is well constrained and can thus be used for geologic interpretation. In the context of 

regularized inversion, calculated DOI helps discriminate between parts of the inverse model that are 

determined by the data and those that simply reflect the regularization applied to the inversion. A linear 

sensitivity-based DOI was calculated for each sounding (Christiansen and Auken, 2012). In this 

approach, a column-wise sum of the sensitivity matrix is calculated for each layer resistivity within the 

model. The cumulative sum of the values for each layer resistivity, starting at the bottom of the model, 

is then used to estimate the DOI. 

Aeromagnetic Survey

Aeromagnetic surveys use a magnetometer attached to an aircraft to measure the variability in 

the intensity of the Earth’s magnetic field over the surveyed area. The relative strength of the magnetic

field at a given location depends on the volume and distribution of magnetic minerals in the subsurface, 

the nature of the magnetic properties of the minerals, and the distance and direction of these minerals

relative to the magnetometer. Different geologic formations may vary in mineral composition, and as 

such, some geologic units have measurable magnetic contrasts. Aeromagnetic data can be useful for 

mapping surficial and subsurface geology, especially in locating concealed faults and detecting buried 

igneous and metamorphic rocks. 
11

The ability to resolve geologic features using gridded aeromagnetic data is largely a function of 

survey design. Reid (1980) determined that a ratio of line spacing to flight height of 2:1 or less is ideal 

for properly resolving most near-surface geologic features. If this ratio is not met, the high-frequency 

information produced by near-surface magnetic sources is lost between flight lines. Grids constructed 

from such line data improperly connect the data between the flight lines leading to aliasing of highfrequency information into low frequencies and a general noisy appearance.

Defining the resistivity structure was most important to meeting the objectives of airborne 

geophysical surveys. As such, the surveys were optimized for the EM system, and survey designs do not 

meet the ideal criteria for resolving near-surface geologic features in gridded magnetic data, despite the 

low flight heights. The ratios of line spacing to flight height for Paradox Valley and San Luis Valley 

surveys are 3.75 and 10.0, respectively. The noisy character typical of aliasing problems is most evident 

in the magnetic grid for the San Luis Valley survey, which has the highest ratio of line spacing to flight 

height. A previously flown aeromagnetic survey provides better resolution over most of the same area 

(Drenth and others, 2009). The aliasing problem is not as severe in the Paradox Valley magnetic grid. 

However, isolated noise spikes are more prevalent and are caused by interference from anthropogenic 

metal structures.

Data Processing

Raw total-field magnetic data were interpolated to a 10-Hz sample rate, merged with processed 

positioning data, corrected for diurnal variations in the magnetic field, heading effects, and the Earth’s 

magnetic field defined by the International Geomagnetic Reference Field (IGRF). Standard tie-line 

leveling and micro-levelling were then applied to obtain the final residual magnetic data. The IGRF was 

added back to the residual magnetic data to represent total-field magnetic data. Processed EM and 

magnetic data are provided in the LINEDATA folder for each survey area. These processing steps are 

described in detail in the SkyTEM Acquisition Report (appendix 1A). 

Geophysical Digital Data Overview

Digital data provided to the USGS under contract from SkyTEM and Aarhus Geophysics are 

released with this report for the Paradox and San Luis Valleys study areas. In some instances, the USGS 

has reformatted these data for consistency, accessibility, and to avoid proprietary data formats. Data are 

provided in separate directories for each survey area with identical subdirectory structures. Several 

digital products and data formats have been provided, in addition to the contractor reports (appendix 1). 

The file structures have been altered from those described by the contractors in appendix 1 for 

simplicity and file size. These digital data are summarized in table 2 and the following sections. 

README files in each data subdirectory provide explanations to file naming conventions, projection 

and datum, and descriptions of channels for tabular data. 
12

Table 2. Digital data organization and description for files and folders within each survey area subdirectory. 

[AEM, airborne electromagnetic; SCI, spatially constrained inversion; Hz, hertz] 

Folder Description

FLIGHTLINE Geospatial datasets containing line locations and numbering. Subfolders exist for AutoCAD 

files (*.dxf), Esri shape files (*.shp), and GoogleEarth Keyhole Markup Language (*.kml) 

formats.

GRIDS Selected contractor-produced grids of inverted AEM, magnetic and elevation data. All grids 

are provided in Geosoft Oasis Montaj (*.grd) format. Grids are also provided as 

georeferenced images in geoTIFF (*.tif) and zipped Keyhole Markup Language (*.kmz) 

formats.

LINEDATA Corrected and culled AEM and aeromagnetic data in ASCII standard (*.xyz) format.

SECTIONPLOTS Images of inverted resistivity sections from the 19-layer SCI inversion of 30-Hz noise culled 

AEM data (*.pdf). 

SECTIONDATA Databases of the corrected and culled inverted resistivity depth models (INV_CORRECTED 

and INV_CULLED, respectively) in ASCII standard (*.xyz) format.

Flight Lines

The FLIGHTLINE folder contains geospatial datasets of the flight-line paths. The flight-line 

location files are formatted in AutoCAD (*.dxf) format, Esri Shapefile (*.shp and associated files) 

format, and in GoogleEarth Keyhole Markup Language (*.kml) format.

Grids

The GRIDS folder contains interpolated grids of various channels of magnetic and ancillary

data: residual magnetic field, total magnetic field intensity (GRID_MAG folder), and the DEM

developed using GPS and altimeter data (GRID_DEM folder). Gridded depth slices through inverted 

resistivity models have also been generated by the USGS for the 19-layer SCI inversion and 30-layer 

LCI inversion above the depth at which the majority of the study area falls below the DOI calculated 

during the inversion process (GRID_19LInvSCI and GRID_30LInvSCI folders). These depth slices 

were generated using the minimum-curvature application (Webring, 1981) with 50-m grid-cell lengths 

and are blanked where the layer falls below the depth of investigation. Figures 5 and 6 show examples 

of these inverted resistivity depth-slice grids for each survey area.

Grids are provided in three formats: numerical data are provided in Geosoft grid (*.grd) format; 

images of these grids are provided in georeferenced geoTIFF (*.tif) and zipped Keyhole Markup 

Language (*.kmz) formats. The nomenclature for the grid names is given in the README file within 

the GRIDS folder. The numerical Geosoft grids can be viewed and analyzed in free software distributed 

by Geosoft (http://www.geosoft.com/support/downloads/viewers/oasis-montaj-viewer) or in various 

other mapping software packages using free plug-ins provided by Geosoft 

(http://www.geosoft.com/downloads). For example, the Geosoft-formatted grids can be viewed directly 

in the Esri ArcMap application with the Geosoft ArcGIS plug-in 

(http://www.geosoft.com/support/downloads/plug-ins/plug-arcgis). Once the plug-in is installed and 

loaded in ArcMap, Geosoft grids can be handled within ArcMap in a similar manner to other types of 

raster data. GeoTIFF images can be viewed in most standard geographic information system software.
13

Keyhole Markup Language images can be viewed using GoogleEarth software freely distributed by 

Google (http://www.google.com/earth/). 

Line Data

The LINEDATA folder contains AEM and aeromagnetic survey data. With respect to the EM 

data, three databases are present for each survey area representing the raw data, the 30-Hz noise 

compensated data, and the noise-culled data. The data are presented in ASCII (*.XYZ) format with 

column headings as described in the README file within the LINEDATA folder. The SkyTEM 

acquisition report (appendix 1A) also describes the digital flight-line data.

Figure 5. Example inverted resistivity depth slice between 21 and 28 m depth from the 19-layer SCI (spatially 

constrained inversion) for the Paradox Valley. Depth slices from additional layers are provided in multiple formats in 

the GRIDS subdirectory.
14

Figure 6. Example inverted resistivity depth slice between 46 and 57 m depth from the 19-layer SCI (spatially 

constrained inversion) for the San Luis Valley. Depth slices from additional layers are provided in multiple formats 

in the GRIDS subdirectory.

Section Data

The SECTIONDATA folder contains the inverted resistivity data as a function of depth along 

the flight lines, as determined from the previously described inversion processes. Two databases are 

present for each survey area representing the LCI inversion of the 30-Hz noise compensated data and 

the 19-layer SCI inversion of the noise-culled data. The depth intervals for each inversion model layer 

(DepTop and DepBot channels) are relative to land surface. Land-surface elevation along flight lines are 

defined by the GPS-derived DEM and is specified in meters relative to the North American Vertical 

Datum of 1988 (NAVD88). 

Section Plots

The SECTIONPLOTS folder contains USGS-generated figures of inverted resistivity section 

data resulting from the 19-layer SCI inversion of the 30-Hz noise-culled AEM dataset. Each survey area 

has been displayed at independent color scales intended to be useful for the interpretive goals of the 
15

ongoing research in both areas. For the Paradox Valley, a logarithmic scale from 1 to 1,000 ohm-m is

shown to illustrate the contrasts between different geologic units and the Paradox Valley brine plume 

(fig. 7). For the San Luis Valley, logarithmic color scales from 5 to 100 and 5 to 500 ohm-m are shown 

to illustrate the sand-clay and sediment-bedrock geologic contacts, respectively (fig. 8). Inverted 

resistivity section data are also displayed in the contractor’s reports (appendix 1). Note that resistivity 

values falling above or below the limits of the color scales are colored the same as the maximum or 

minimum limits, respectively.

The DOI is a useful tool when displaying the final inverted resistivity sections because it 

provides a means for evaluating areas of confidence in the inverted model. In the SECTIONPLOTS

folder, inverted resistivity sections for the Aarhus SCI inversion are shown with a semitransparent mask 

applied below the DOI. As such, the portions of the inverted model that are well-constrained by the 

measured data are shown in full-strength color, while the poorly constrained portions are obscured. The 

uneven “ragged” DOI seen in some sections is in part the result of the 30-Hz culling routine applied to 

the data prior to the SCI inversion.
16

Figure 7. Example of inverted resistivity section plot for the Paradox Valley. Plots for all sections can be found in the SECTIONPLOTS 

subdirectory.
17

Figure 8. Example of inverted resistivity section plot for the San Luis Valley. Plots for all sections can be found in the SECTIONPLOTS 

subdirectory. AEM, airborne electromagnetic.
18 

Acknowledgments

The authors acknowledge the efforts of Jared Abraham (formerly USGS), Ben Drenth (USGS), 

and Ken Watts (USGS) in the design and contracting of these surveys. Appreciation is extended to 

Andrew Nicholas (USBR) and the Colorado River Basin Salinity Control Forum for their support of the 

Paradox Valley survey and to Andrew Valdez and Fred Bunch (NPS) and the staff at Great Sand Dunes 

National Park and Preserve for their support of the San Luis Valley survey. This report was improved 

through reviews by Ben Drenth and Jeff Lucius (both USGS).

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ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20151024