Topographic, Geophysical, and 

Mineralogical Characterization of Geologic 

Structures Using a Statistical Modeling 

Approach

By Robert R. McDougal, Anne E. McCafferty, Bruce D. Smith, 

and Douglas B. Yager

Chapter E13 of

Integrated Investigations of Environmental Effects of Historical 

Mining in the Animas River Watershed, San Juan County, Colorado

Edited by Stanley E. Church, Paul von Guerard, and Susan E. Finger

Professional Paper 1651

U.S. Department of the Interior

U.S. Geological Survey
Contents

Abstract .......................................................................................................................................................647

Introduction.................................................................................................................................................647

Purpose and Scope ..........................................................................................................................649

Geologic Structures...................................................................................................................................649

Data Collection and Compilation .............................................................................................................649

Airborne Geophysical Data .............................................................................................................651

Geologic Structural Data .................................................................................................................651

Data Accuracy...................................................................................................................................651

Remote Sensing Data .......................................................................................................................652

Methods.......................................................................................................................................................655

Calculation of Planimetric Orientation of Structures and Geophysical Gradients ................655

Predictive Modeling Methodology ................................................................................................655

Data Preparation for Predictive Modeling ...................................................................................656

Topographic Data .....................................................................................................................656

Magnetic Data Enhancement ................................................................................................656

Electrical Resistivity Data .......................................................................................................657

Remote Sensing Data ..............................................................................................................665

Results and Analysis .................................................................................................................................665

Planimetric Orientation of Geologic Structures and Geophysical Gradients .........................665

Weights Charts and Predictive Models ........................................................................................665

Profile Convexity Model ...................................................................................................................665

Resistivity Model ...............................................................................................................................667

Magnetic Models ..............................................................................................................................668

Magnetic Characteristics of Shallow Geologic Structures ..............................................668

Magnetic Characteristics of Deep Crust Geologic Structures ........................................670

Mineralogic Models .........................................................................................................................675

Model Validation ...............................................................................................................................676

Model Stability..........................................................................................................................676

Comparison of Geophysical and Profile Convexity Profiles to Geologic 

Cross Sections ............................................................................................................676

Field Verification ......................................................................................................................683

Conclusions and Discussion ....................................................................................................................686

References Cited........................................................................................................................................686

Figures

 1. Generalized map of selected geographic and geologic features in the Animas 

River watershed study area ....................................................................................................648

 2. Index map of extent of structural, airborne geophysical, and AVIRIS data sets ...........650

 3. Map of mineralized and nonmineralized faults and veins .................................................652

 4. AVIRIS mineral map showing acid-generating potential minerals ..................................653

 5. AVIRIS mineral map showing acid-neutralizing potential minerals .................................654

 6. Images of shaded relief Digital Elevation Model and derived profile convexity 

model ..........................................................................................................................................656

 7. Reduced-to-pole magnetic anomaly map ............................................................................658
 8–11. Maps showing:

 8. “High-pass” filtered magnetic anomalies calculated from RTP magnetic data .........659

 9. “Low-pass” filtered magnetic anomalies ......................................................................660

 10. “High-pass” horizontal magnetic gradient anomalies calculated from RTP 

 magnetic data ....................................................................................................................661

 11. “Low-pass” horizontal magnetic gradient anomalies calculated from RTP 

 magnetic data ....................................................................................................................662

 12. Profile illustration of various transformations applied to the Silverton magnetic 

data .............................................................................................................................................663

 13. Map showing 4,310-Hz apparent resistivity data ................................................................664

 14. Data layer images used to produce masked coverage of faults and veins from 

AVIRIS vegetation and snow cover mapping.......................................................................666

 15. Rose diagrams showing planimetric orientation of mapped geologic structure 

and geophysical gradient trends from airborne geophysical survey ..............................667

 16. Graph showing calculated weights of the relationship of structures to topography ........668

 17. Map showing profile convexity predictive model of geologic structures .......................669

 18. Graph showing weights of association of electrical resistivity with mapped 

structures ...................................................................................................................................670

 19. Map showing electrical resistivity predictive model of geologic structures .................671

 20. Images showing predictive model of resistivity—location of ground 

electromagnetic survey in Placer Gulch ..............................................................................672

 21. Graphs showing calculated weights of magnetic high-pass filtered data and 

high-pass filtered horizontal gradient magnitude associations with geologic 

structure .....................................................................................................................................673

 22. Map showing high-pass horizontal gradient magnitude magnetics predictive 

model of geologic structures ..................................................................................................674

 23. Graph showing calculated weights of geologic structure association with 

low-pass magnetic horizontal gradient magnitude ............................................................675

 24. Bar graphs showing calculated weights of geologic structure association with 

acid-generating potential and acid-neutralizing potential group minerals .......................677

 25. Graphs showing comparison of predictive models for verification .................................678

 26. High-pass magnetic, 4,310 Hz apparent resistivity, and profile convexity profiles 

over geologic cross section A–A' ...........................................................................................679

 27. High-pass magnetic, 4,310 Hz apparent resistivity, and profile convexity profiles 

over geologic cross section B–B' ...........................................................................................680

 28. Images showing comparison of profile convexity, resistivity, and magnetic 

models and maps to a silicified vein in Boulder Gulch .......................................................684

 29. Images showing comparison of profile convexity, resistivity, and magnetic 

models and maps to an altered and eroded zone near the headwaters of 

South Fork Cement Creek ........................................................................................................685

Tables

 1. Numerical comparison of geologically mapped structure locations along 

profiles A–A' (fig. 26) and B–B' (fig. 27) to locations of geologic structures 

predicted by high-pass magnetic, 4,310 Hz resistivity, and profile convexity 

models ........................................................................................................................................682

Abstract

Statistical modeling of geologic structures in the Animas 

River watershed study area enables definition of geophysical 

(electromagnetic and magnetic), mineralogical, and topographic attributes, portrayed in charts and maps as probabilities. The modeling shows that faults and veins in the 

watershed have characteristic signatures within discrete data 

value ranges for all data layers, as evident by relatively high 

probabilities. Topographically, structures are characterized 

by erosionally resistive and likely silicified ridgelines, or 

appear as eroded incised channels and valleys. Geophysically, 

the planimetric orientations of structures calculated from the 

trends of geophysical gradients identify east-west, northerly, 

and northwesterly trends, which are collinear with trends of 

mapped geologic structures. Magnetically, the faults and veins 

are mostly characterized by extremely low magnetizations 

that are associated with the magnetite-poor mineralogy of the 

silicic vein material. Although a less typical case, some faults 

and veins are characterized by moderately high magnetizations. Magnetization boundaries associated with rocks located 

at depths exceeding 1 kilometer identify surface structures that 

likely provide connectivity between the topographic surface 

and deep crust, and revealed buried crustal boundaries not 

mapped at the surface. Mapped faults and veins are characterized by moderately high electrical resistivities consistent 

with the silicic mineralogy of the vein material but may also 

point to silica-rich phases within volcanic rocks in the study 

area. The mineralogical character of structures in terms of 

their acid-producing or acid-neutralizing potential suggests 

no significant association with acid production resulting from 

the weathering of pyrite. Structures are most likely to occur in 

propylitically altered areas, and they are therefore associated 

with rocks with the highest acid-neutralizing potential. In map 

form, the predictive models show high probabilities that accurately locate many known structures but, more interestingly, 

map areas devoid of structures. Many of the permissive areas 

for unmapped structures, however, lie in places of restricted 

access, limited outcrop, and thick alluvium, soil, or vegetation 

cover. Our validation methods show that the models can successfully locate known structures and predict the location of 

previously unknown structures.

The predictive models presented in this study could be 

considered in remediation planning, or used as an exploration 

tool for ground-water or mineral resource applications. The 

methods and approaches employed here can be applied to other 

watersheds with similar structural and volcanic regimes containing acid-generating historical mines or source rocks.

Introduction

The Animas River watershed study has produced substantial geologic, geochemical, and geophysical data, and related 

scientific analysis. Mapped geologic structure, digital terrain, 

airborne magnetic and resistivity data, and remotely sensed 

mineral mapping are available for most of the watershed area 

and provide the necessary data for regional to local-scale 

analysis and investigations. In this study, we use these data 

sets and derivative products to provide an understanding of 

how physical parameters relate to and typify mapped geologic 

structures, specifically, faults, fractures, and mineralized veins. 

These linear and curvilinear structures are assumed to be positively associated with local, subregional, and regional groundwater flow regimes, particularly in the vicinity of the Eureka 

graben, caldera margins, and South Silverton mining area 

(Yager and Bove, this volume, Chapter E1) (fig. 1). In some 

cases, structures provide pathways for ground-water flow. In 

contrast, where faults and veins are annealed with quartz, they 

may act as barriers to flow.

An attempt to describe the mechanics of ground-water 

flow through bedrock needs to consider the physical characteristics of the bedrock. In this study, it was necessary to define 

bedrock structural trends, identify the mineralogy within 

Chapter E13

Topographic, Geophysical, and Mineralogical 

Characterization of Geologic Structures Using 

a Statistical Modeling Approach

By Robert R. McDougal, Anne E. McCafferty, Bruce D. Smith, and Douglas B. Yager
EXPLANATION

Late Tertiary intrusive rocks

Tertiary volcanic rocks

Mesozoic and Paleozoic sedimentary rocks

Precambrian rocks

Contact

Faults and vein structures

Silverton caldera structural margin

Dashed where concealed

San Juan caldera topographic margin

Approximately located; queried where extent 

uncertain

 MILES

0 9 KILOMETERS

0 6

Silverton

?

?

107o 45' 107o 40' 107o 35'

37o55'

37o50'

37o45'

Animas

 R

Cement Cr

M

ni eral Cr

TOLTEC FAULT

BONITA FAULT

ROSS BASIN FAULT

SUNNYSIDE FAULT

A A'

B

B'

Ohio Peak

EUREKA GRABEN

SOUTH SILVERTON

MINING AREA

Sultan Mtn

Hazelton Mtn

Kendall

Peak

Anvil Mtn

Emery

Peak

Howardsville

S

Fork Ce

ment Cr Whitehead Peak

SUNNYSIDE

SULTAN MTN

STOCK

Placer Gulch

Boulder Gulch

?

Inactive mine

Macomber

Peak

Storm

Peak

+

+

+

+

+

+

+

+

—

—

Figure 1. Selected geographic and geologic features in the Animas River watershed study area. Modified from Yager and 

Bove (2002). Cross sections A–A' and B–B'are discussed in a later section and are shown in figures 26 and 27 (p. 679, 680).

648 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 649

structures, understand the geophysical signatures of structures, 

and finally, determine how structures are geomorphologically 

expressed in order to differentiate between structures that 

likely are hydraulically conductive and those that are not. 

Determining the mineralogical characteristics of structures 

may provide useful environmental information by differentiating structures that may have increased acid-generating 

potential and consequently may be associated with increased 

metal loading to ground and surface water, or alternatively, by 

identifying structures that are possibly associated with greater 

acid-neutralizing potential.

In this study, we derived models using statistical methodology to quantify the topographic, geophysical, and mineralogical signatures of mapped mineralized and nonmineralized 

faults and veins in the watershed. The calculated probabilities 

describe the association between a structural feature and a 

data layer, and can be expressed as either positive or negative 

associations. The analyses should reveal which parameters 

(topography, electrical resistivity, magnetic signature, or 

mineralogy) are least likely to characterize structural features 

(negative association), and alternately, which are most likely to 

characterize structural features (positive associations). Results 

of the calculations quantify characteristics of the geologic 

structures. Additionally, a series of predictive maps identify 

areas within the watershed that exhibit characteristics similar 

to those of the known faults and veins.

The predictive models presented here can identify 

areas where previously unmapped structures may exist. This 

information could be considered in remediation planning, or it 

could be used as an exploration tool for ground-water or mineral resource applications, as it contributes to a more complete 

understanding of ground-water and surface-water interactions, metal loading to streams, and identification of possible 

ground-water pathways.

Purpose and Scope

The purpose of this investigation is to define the topographic, geophysical, and mineralogical characteristics of 

mineralized faults and veins in the study area and to use this 

information to infer the locations of exposed and buried geologic structures in the watershed. The objectives of the study 

are to:

• Examine the relationship of geologic structure 

to topography. Determine the geomorphological 

characteristics of mapped structure and identify the 

types of terrain where faults and mineralized veins 

occur

• Determine the structural trends (planimetric orientation) of mineralized faults and veins, and compare 

their orientations to those of geophysical gradient 

trends

• Determine the geophysical (magnetic and electrical 

resistivity) characteristics of mapped structures

• Using remotely sensed data, determine the environmental effects of mapped structures in terms of their acidneutralizing or acid-generating potential as indicated 

by mineral assemblages

• Identify areas where previously unmapped structures 

may exist based on topographic, geophysical, and 

mineralogical attributes.

Geologic Structures

For the purposes of this study, the general terms “geologic structure” or “structures” are intended to refer specifically to mineralized and nonmineralized faults and veins. 

The prominent fault systems and extensive mineralization of 

faults and veins of the San Juan–Uncompahgre and Silverton 

calderas are of particular interest in terms of their potential 

as flow paths for structurally controlled ground water. Faults 

occur as ring faults associated with the caldera structural 

margin, as adjacent radial faults, and as faults associated 

with the Eureka graben. In some areas of the watershed, vein 

orientation appears to be strongly associated with the degree 

of mineralization. In the South Silverton mining area (fig. 1), 

southeast of Silverton, and near the northwestern boundary 

of the caldera, northwest-trending veins, perpendicular to the 

structural margin ring faults, are relatively more mineralized. 

Near the Eureka graben, mineralized structures generally 

trend to the northeast (approximately 45°) or to the northwest 

(approximately 270°) (Casadevall and Ohmoto, 1977).

Veins in the study area commonly occur in broad zones 

that have undergone multiple episodes of mineralization. Such 

zones in the South Silverton mining area have been described 

as country rock traversed irregularly by ore-bearing stringers, which can attain a width of about 30 m (Burbank, 1933). 

The wider veins and bodies of veined and altered rock in the 

Sunnyside vein system achieve a width of 30 m. Ore stopes 

in the Sunnyside mine are reported to have been 15 m wide in 

some places (Burbank and Luedke, 1969). Outcrop vein widths 

of several meters were observed in several places across the 

Sunnyside and Ross Basin fault vein systems in Eureka Gulch 

and across the Toltec fault in Eureka Gulch. Field observations 

of veins in the study area have aided us in determining the 

appropriate proximity zone used in the analysis presented here.

Topographically, faults and veins can create zones of 

erosional weakness, resulting in linear to curvilinear valleys, 

gullies, and stream channels. Alternatively, where faults and 

veins are silicified, erosionally resistive linear ridges can form.

Data Collection and Compilation

This section describes the basic data layers and related 

processing and transformations that we applied as preparation 

for calculating orientations of structural trends and performing 

the predictive modeling. The areal extent of each data set used 

in the analysis is shown in figure 2.
0

0 5 KILOMETERS

5 MILES

EXPLANATION

Watershed study area

Extent of structural mapping

Extent of airborne geophysical survey

Extent of AVIRIS data

Silverton

Figure 2. Extent of structural, airborne geophysical, and AVIRIS data sets.

650 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 651

Airborne Geophysical Data

An airborne geophysical survey was flown over the 

Animas River watershed study area and surrounding region 

during the fall of 1998. Magnetic and electromagnetic (EM) 

data were collected along north-south flightlines at a 200 m 

spacing for the west half of the watershed and at 400 m spacing for the eastern part of the watershed. The electromagnetic 

field was measured with three frequencies (984, 4,310, and 

33,000 Hz) using horizontal coplanar transmitting and receiving coils towed in a capsule container (“bird”) that varied in 

elevation but was generally located at approximately 30 m 

above the ground surface. The helicopter towed the sensor 

at higher altitudes where appropriate for safe flying, for 

example, over power lines, tramlines, and on ascent up steep 

mountainsides. Accurate measurement of EM fields from a 

helicopter platform requires that the sensor be positioned at 

low altitudes. However, due to the extremely rugged topography within the Animas River watershed study area, safe flying 

dictated that the EM sensor was, at times, out of range for 

collection of accurate EM data. Data were not collected over 

populated areas. Areas within the survey where the EM data 

are inaccurate or absent are mapped as white on the resistivity maps and associated derivative products. The EM data 

were post-processed to compute values of apparent resistivity 

(Fraser, 1978).

The total field magnetic sensor was also mounted in the 

EM bird. Magnetic-field measurements are less affected by 

altitude variations; therefore, the maps of magnetic data and 

derivative products have complete coverage within the area 

covered by the airborne survey. The total field data were postprocessed to remove diurnal variations produced by the Earth’s 

magnetic field.

Flightline data for the magnetic and resistivity data were 

interpolated onto a 40 m interval grid for the western part of 

the survey and onto an 80 m interval grid for the eastern part 

of the survey to maintain data resolution for each survey. The 

grid interval along the north-south flightlines was 3 m. Data 

for the eastern part of the survey were resampled to a 40 m 

interval and merged with the west survey grid to create a composite grid. A description of the digital flightline data, grids, 

and information on other derivative geophysical products from 

this survey are available in Smith and others (this volume, 

Chapter E4).

Geologic Structural Data

Structural data were compiled from numerous paper map 

sources that were originally produced at multiple scales, and 

subsequently combined into separate Geographic Information 

Systems (GIS) coverages of mineralized veins and faults (Yager 

and Bove, this volume, pl. 1). The scale of data utilized ranged 

from 1:20,000-scale generalized geology for the western part 

of the Howardsville quadrangle and Silverton quadrangle 

(Luedke and Burbank, 1975a, b) to 1:250,000-scale geology 

compiled for intrusive rocks exposed in lower Cunningham 

Creek (Steven and others, 1974). Most data were compiled 

from 1:24,000-scale and 1:48,000-scale source maps (Lipman, 

1976; Burbank and Luedke, 1964; Luedke and Burbank, 1987; 

Luedke, 1996). Due to (1) the numerous digital processing steps 

involved, (2) use of multiple software packages in digitizing 

source maps, (3) use of multiple map-scale source data, and 

(4) subsequent comparison and verification of digitized geologic features with paper maps, a final map scale of 1:48,000 

was determined to be appropriate for the final geologic compilation (Yager and Bove, this volume, pl. 1).

Data Accuracy

Accuracy issues are important to consider when data 

of multiple scales are utilized in spatial analysis of geologic 

features. These issues are especially important when a GIS 

proximity analysis is done to determine how many structures 

or veins are within a specified distance from a linear feature 

or fault. A logical search distance was determined in this study 

based on the digital data sources utilized and on geologic field 

observations for the geologic features.

National Map Accuracy Standards (initially published 

by the National Bureau of the Budget in 1941 and revised 

in 1947) established that for map scales that are larger than 

1:20,000, ninety percent of all features mapped must be 

within 1/50 of an inch of their actual locations on the map. A 

map that is published at the 1:48,000 scale has a map accuracy 

on the ground of 80 feet (about 24 m), whereas a map scale 

of 1:24,000 has an accuracy on the ground of 40 feet (about 

12 m).

Other source data utilized include high-resolution 

U.S. Geological Survey digital orthophoto quadrangles (DOQ). 

The DOQs became available near the end of this study, after 

the geologic compilation was completed (Yager and Bove, 

this volume, pl. 1). The accuracy of these data is equivalent 

to 1:24,000 scale, that is, about 12 m. The area of the ground 

represented by each DOQ pixel is 1 m. Although these highresolution images are very useful for interpreting structural 

information, a finer resolution data set is not necessarily 

directly applicable for comparison with geologic data that 

were compiled at a coarser scale.

The geologic structure data used in this study were 

obtained from the digital coverages used to create plate 1 

(Yager and Bove, this volume, pl. 1). Separate digital GIS coverages of mapped faults and mineralized veins were combined 

to produce a single vector data file because, in many cases, 

mineralization in the study area occurred not only in veins, but 

also in faults where displacement and offset are evident. The 

combined vector coverage was then used to generate a raster 

data set with a 2-pixel width proximity zone around each line 

depicting mapped structure (fig. 3). A 20 m (2 pixels on each 

side of the mapped line) search distance is conservatively 

consistent with the 24 m accuracy of a 1:48,000-scale map 

product and reflects the approximate widths of structural zones 

observed in the field.
Remote Sensing Data

As part of the Animas River watershed study, two 

flightlines of AVIRIS (Airborne Visible Infrared Imaging 

Spectrometer) data were acquired in June 1996 by the Jet 

Propulsion Laboratory (JPL). The extents of coverage, 

processing of data, and mapping procedures are described 

by Dalton and others (this volume, Chapter E2). For the 

purposes of this investigation, the mapping results showing 

acid-generating and acid-neutralizing mineral assemblages 

were chosen to characterize geologic structures in terms of 

their AGP (acid-generating potential) (fig. 4) and ANP (acidneutralizing potential) (fig. 5). Knowledge of the distribution 

of these mineral assemblages and their spatial association with 

structure is important to our understanding of the mineralogic 

character of possible ground-water pathways.

Alteration types in the study area, in order of weakest to 

most intense, include regional propylitic, weak sericite-pyrite, 

vein-related quartz-sericite-pyrite, quartz-sericite-pyrite, 

and acid-sulfate (Bove and others, this volume, Chapter E3). 

Most of the volcanic rocks in the watershed are propylitically 

altered, with pervasive secondary replacement minerals of 

chlorite, epidote, calcite, and locally sericite. The widespread 

propylitic alteration is significant because of the high acidneutralizing potential (ANP) of calcite, and to a lesser extent, 

chlorite (Desborough and Yager, 2000).

Figure 3. Mineralized and nonmineralized faults and veins (Yager and Bove, this volume, pl. 1). Unmapped areas shown in gray. 

Watershed study area boundary shown in red. Example inset of mapped structures with proximity zone is outlined in blue.

0 5 MILES

0 5 KILOMETERS

652 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 653

Figure 4. AVIRIS mineral map (white outline, extent of data) showing acid-generating potential (AGP) minerals. Base image is a 

shaded relief generated from the DEM (from Dalton and others, this volume, Chapter E2).

0 5 MILES

0 5 KILOMETERS

Silverton

Goethite

Jarosite

Hematite

Nanocrystalline hematite

Amorphous iron oxides

and iron hydroxides

Acid-generating potential minerals group

EXPLANATION
0 5 MILES

0 5 KILOMETERS

Silverton

Calcite

Calcite + epidote

Epidote + calcite

Epidote

Epidote + calcite + chlorite

Epidote + chlorite

Chlorite

Chlorite + muscovite

Muscovite/sericite

Muscovite with calcite

and (or) chlorite

Acid-neutralizing potential minerals group

EXPLANATION

Figure 5. AVIRIS mineral map (white outline, extent of data), showing acid-neutralizing potential (ANP) minerals. Base image is a 

shaded relief generated from the DEM (from Dalton and others, this volume, Chapter E2).

654 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 655

The most significant acid-generating potential in the 

watershed is attributable to the weathering of pyrite. The AGP 

group includes jarosite, goethite, hematite, and amorphous iron 

oxides and hydroxides. Although these minerals themselves 

have generally low acid-generating potential (Desborough 

and Yager, 2000), they can indicate areas of greater potential 

for acid generation because of their association with pyrite 

in the weathering process. Pyrite itself is not included in the 

AGP group because, spectrally, it is difficult to map at AVIRIS 

resolution.

The ANP group of minerals spectrally detectable using 

AVIRIS data includes calcite, chlorite, epidote, and muscovite/

sericite. The distribution of calcite, particularly in relation 

to faults and veins, is important because of its high ANP of 

acidic waters resulting from pyrite weathering and acidic 

runoff (Church and others, 2000). Chlorite also has some ANP 

though it is much less effective than calcite (Desborough and 

others, 1998). The mineral assemblage of calcite-chloriteepidote occurs in areas of propylitic alteration and is indicative 

of areas of greater ANP. The combination muscovite/sericite 

is included in the ANP group because of its spectral relationship with and effect on the calcite-chlorite-epidote assemblage 

(Dalton and others, 2002).

Methods

Calculation of Planimetric Orientation of 

Structures and Geophysical Gradients

To evaluate similarities between planimetric orientations of mapped structures and structures at depth, generalized trends of mineralized faults and veins were compared 

to orientations of geophysical gradients. Gradients were 

calculated from the residual reduced-to-pole (RTP) magnetic 

data and the 4,310 Hz frequency electrical resistivity data following the method of Grauch (1988). Magnetic and resistivity gradients result from contrasts in magnetic and electrical 

properties of rocks, respectively, and map boundaries between 

rocks with dissimilar geologic characteristics. Geophysical 

gradient trends are defined as distinct linear and curvilinear 

elements that can be mapped from contours in the geophysical data. Faults and mineralized veins can be characterized by 

low electrical resistivities (high conductivity) if the structures 

contain clay, contain metallic minerals, or are filled with water 

containing high dissolved solids. Alternately, fractures that 

have been silicified or contain fresh water are commonly resistive. In general, dry, unfractured rock will have high resistivity 

values in contrast to its wet and fractured counterpart. If clays 

are present in fractured rock, and water is present, resistivity 

values can drop significantly (Olhoeft, 1985) in comparison 

to completely dry rocks.

The vector coverages of faults and mineralized veins 

(Yager and Bove, this volume, pl. 1) were simplified so that 

long curvilinear lines could be represented as straight-line 

segments and the geographic coordinates of the endpoints 

could be recorded. The geographic coordinates (recorded as 

Universal Transverse Mercator (UTM) coordinates) of the 

endpoints of each mapped vector were used to calculate the 

azimuth of each mapped structure. These data were used in 

a 2-D orientation analysis (RockworksTM, Rockware® Inc., 

1999) to determine primary orientations of faults and mineralized veins from rose diagrams.

Predictive Modeling Methodology

A statistical approach using methodology developed 

by Lee and others (2001) for a statewide geoenvironmental 

assessment of Montana was applied to the geologic structure, 

topographic, geophysical, and mineralogical data layers in our 

study. The approach uses a ratio of probabilities to describe 

the statistical likelihood that a class within a particular evidential layer has a spatial association with a given prototype 

area. In this study, the prototype area is the area around and 

including the mapped faults and veins together with the 20 m 

proximity zone surrounding them. The probability ratios are 

expressed as weights of association, W. The expression for this 

weight is given by:

 Conditional Probability

 W = ——————————————

 Prior Probability

In practice, the formula used to determine (W) is as follows:

 (areas of overlap between an evidential 

 layer class and a prototype layer /area that 

 a test class occupies in the overall study area)

 W = ——————————————————————

 (area that the prototype layer occupies 

 in the overall study area /

 total area of the study)

The probability ratio is expressed as the ratio of the probability of being in the prototype area given the condition of being 

in the test area (numerator) to the probability of being in the 

prototype area with no condition except being in the study area 

(denominator). In other words, the relative likelihood of finding 

the prototype (geologic structures) within each evidential (profile convexity, resistivity, magnetics, and mineralogy) class area 

is measured compared to the probability of finding the prototype within the study area (Lee and others, 2001). Weights fall 

within three categories that describe the spatial associations 

between evidential data layers and geologic structures.

A positive spatial association exists if W>1;

A negative spatial association exists if W<1; and

A random association is present if W=1.
For example, a probability ratio of 30 for a data layer 

class suggests that an observer is 30 times as likely to be 

within 20 m of a geologic structure if located within that class 

than if randomly (unconditionally) located within the study 

area. For this study, the topographic expression, magnetic 

properties, electromagnetic resistivity, and mineralogical 

characteristics (evidential layers) are evaluated for their spatial 

association with mapped geologic structures. The assumption 

is made when applying the probability ratio method that the 

training area (reference or prototype) accurately and adequately represents what the models are meant to predict.

The probability ratio method requires that the maps 

being compared be equal in their areal extent. If a data set 

lacks information over a particular region, the area analyzed 

is restricted to the region of overlap. All processing steps for 

the probability ratio method (Lee and others, 2001) were completed using GIS analysis tools in ERDAS® IMAGINE v. 8.5.

Data Preparation for Predictive Modeling

Topographic Data

The profile convexity topographic model was produced 

from a USGS digital elevation model (DEM) (10 m spatial 

resolution) using the commercially available image processing 

and analysis software package ENVI® v. 3.5 (Environment 

for Visualizing Images, Research Systems Inc.). The model 

measures the rate of change of the topographic aspect orthogonal to the XY plane. This parametric information is extracted 

by fitting a quadratic surface to the DEM data for a chosen cell 

size and taking the second derivative (Wood, 1996). For this 

study, it was determined that a five-by-five cell size sufficiently 

smoothed the DEM data without significant loss of topographic 

detail. The profile convexity image shows ridgelines as positive 

values (light values in image) and valleys and stream channels 

as negative values (dark values in image; fig. 6). The geologic 

structure data layer was used to mask the profile convexity data 

so that the two layers would have the same spatial extent, and 

therefore, could be compared on a pixel-by-pixel basis.

Magnetic Data Enhancement

Comprehensive discussion of the magnetic data acquisition can be found in Smith and others (this volume). Aeromagnetic surveying is based on mapping variations in the local 

magnetic field caused by changes in the surface and subsurface 

concentration of various magnetic minerals in rocks. Depth of 

investigation for magnetic data can vary from a few meters to 

hundreds of meters and is a function of anomaly wavelength. 

Shorter wavelength anomalies generally are produced by shallower magnetic sources, and longer wavelength anomalies represent the magnetic anomaly response of deeper buried features.

A number of data enhancement techniques are applied 

to the magnetic data to transform anomalies into a form that 

can be directly compared with variations in rock mineralogical properties. Enhancements were used that (1) reduce the 

magnetic field to the magnetic pole to position anomalies 

approximately over their causative sources; (2) increase the 

resolution of small-scale anomalies related to near-surface 

magnetic sources; and (3) enhance anomalies related to deepseated crustal boundaries.

Figure 6. Example of shaded relief Digital Elevation Model (DEM) (left), and derived profile convexity model of corresponding area (right).

Cunningham Creek

Cunningham Creek

656 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 657

The study area is located at a geomagnetic latitude where 

magnetic anomalies are shifted slightly to the southwest from 

their sources and are represented by an anomaly pair determined, in part, by the inclination of the present-day Earth’s 

field. To correct anomalies for this offset in symmetry and 

position, a filter called reduction-to-pole (RTP) was applied 

to the magnetic data for the watershed using U.S. Geological 

Survey potential fields programs (Phillips, 1997). Application 

of the RTP filter correctly positions magnetic anomalies over 

their sources. This assumes that the rock’s total magnetization 

(sum of induced magnetization and remanent magnetization) 

is effectively collinear (coaxial) with the Earth’s present field 

(Telford and others, 1976).

Although some rock units within the Silverton caldera 

(fig. 1) exhibit remanent magnetization (Gettings and others, 

1994; Grauch and Hudson, 1987), the magnetization intensities and directions are low and within a range considered 

collinear with the present-day magnetic field (Bath, 1968). 

Consequently, we can consider the remanent magnetization 

negligible for purposes of this study and assume that for the 

majority of RTP magnetic anomalies, their shape and positions 

are accurately estimated (fig. 7).

To aid interpretation of anomalies related to nearsurface geologic units, “match” filtering was applied to the 

RTP data. Match filters facilitate approximations of magnetic fields produced by sources at different depths based 

on ranges of wavelengths. The filtering was applied using a 

method developed by Syberg (1972), and was implemented 

with U.S. Geological Survey computer programs by Phillips 

(1997). Application of this method yielded a short-wavelength 

anomaly field that approximates the magnetic response from 

a shallow magnetic source layer located approximately in 

the upper 200 m of the topographic surface. The magnetic 

response from the shallowest depth layer, defined as the 

“high-pass” magnetic anomaly, relates to near-surface geologic units and processes important to this study (fig. 8). The 

depth range of the shallowest magnetic-source layer corresponds most closely to the apparent-resistivity-data depths 

of investigation and to acid-generating and acid-neutralizing 

interactions.

A second product of match filtering is a long-wavelength 

component of the RTP magnetic field (fig. 9). Anomalies in 

this map represent the magnetic response of rocks at depths 

exceeding approximately 1,300 m. This estimate is given as 

part of the matched filtering process and is based on theory 

discussed in Syberg (1972). The magnetic response from the 

deepest sources, defined as the “low-pass” magnetic anomalies, is likely associated with features such as the structural 

boundary of a caldera.

In broadest terms, magnetic anomaly maps depict 

anomalies and the gradients that separate them. The characteristics of a magnetic anomaly are its amplitude and wavelength, 

which are functions of the amount of magnetic minerals 

(commonly titanomagnetites) present in the rock and the depth 

to the magnetic source. The gradients that surround magnetic 

anomalies map lateral discontinuities among rocks with 

different magnetic properties. These gradients can be related 

to faults and structures that juxtapose rocks with different 

magnetizations.

To enhance the locations of magnetic boundaries and 

to evaluate directional trends within the magnetic anomaly 

maps as they relate to geologic structural trends, a three-step 

procedure was applied to both the high-pass RTP (fig. 8) and 

low-pass RTP (fig. 9) maps. First, the magnetic potential 

was calculated, originally called pseudogravity by Baronov 

(1957). The magnetic potential transformation reduces the 

side lobe gradients in RTP maps caused by magnetic sources 

with limited thickness (Blakely, 1995). The horizontal gradient 

magnitude (HGM) of the magnetic potential was then calculated following the method of Cordell and Grauch (1985). 

The HGM of the high-pass and low-pass magnetic potential 

field is shown in figures 10 and 11, respectively.

The HGM method objectively locates the steepest 

parts of gradients present in the magnetic potential data. In 

figures 10 and 11 magnetization boundaries are portrayed as 

elongate ridges. The ridge crests locate boundaries where rocks 

with significantly different magnetizations are juxtaposed. In 

general, this technique works on the premise that an anomaly 

slope (either positive or negative) will be steepest at a point 

directly over a vertical or near-vertical geologic contact. The 

positions of the ridge crests can be offset somewhat if the 

contact is gradational or not steeply dipping, if the magnetic 

source was under-sampled, or if there are a number of magnetic 

sources superimposed (Grauch and Cordell, 1987). Examples 

of the magnetic data transforms just described are shown in 

figure 12.

Due to the dissimilar areal coverage (fig. 2) of the geologic structure and airborne magnetic data sets, a two-step 

masking process was used to produce raster layers of equal 

spatial extent. First, the geologic structure layer of faults and 

veins was used to mask the magnetic data layer. The resulting layer of magnetic data was then used to mask the original 

layer of faults and veins.

Electrical Resistivity Data

The airborne electromagnetic system maps the lateral 

and vertical variations in electrical resistivity (or the reciprocal, conductivity) of rocks, structures, and soils. The depth of 

investigation for the EM measurements varies inversely as a 

square root of frequency and ground resistivity (Telford and 

others, 1976). The electromagnetic (EM) field was measured 

with three frequencies (984, 4,310, and 36,000 Hz) using horizontal coplanar transmitting and receiving coils. Discussion 

of the electromagnetic data acquisition and processing can be 

found in Smith and others (this volume).

For this study, the mid-frequency 4,310 Hz data were 

used to investigate the resistivity characteristics of structures 

to an approximate depth of 50 m (fig. 13). The structures in 

this depth range are likely those that would be associated with 

shallow to mid-depth structurally controlled ground-water 

flow. The same two-step masking procedure used for the magnetic data set was also applied to the resistivity data.
Figure 7. Reduced-to-pole magnetic anomaly map. Data from this map were filtered to enhance magnetic features located in the shallow and deep crust (figs. 8 and 9, 

respectively). Map of mineralized and nonmineralized faults and veins (Yager and Bove, this volume, pl. 1) is overlaid on the magnetic data map.

nTesla

0

0 5 KILOMETERS

5 MILES

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neral 

 

Cre ek

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ne t

Creek

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mi as

Ri

ev r

658 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 659 nTesla

0

0 5 KILOMETERS

5 MILES

Mineral Cre ek

em C

ne t 

C er ek

 Animas

R vi er

Figure 8. “High-pass” filtered magnetic anomalies calculated from RTP magnetic data (fig. 7). Map shows magnetic anomalies in the shallow crust as related to mineralized veins (black lines).
Figure 9. “Low-pass” filtered magnetic anomalies. The filter enhances anomalies associated with magnetic properties of deep crustal features. The Sultan 

Mountain stock granitic intrusives (Tig) are shown in black and gray, and major structural features are also shown.

0

0 5 KILOMETERS

5 MILES

Min

e

ral 

 C ree k

en me C t

 

C er ek

 

 

An

mi as 

 

R

 

ve i r

Silverton calderastructural margin

Tig

Tig

Tig

Tig

Tig Tig

Tig

Tig

Tig

ROSS BASIN FAULT EUREKA GRABEN

nTesla

SUNNYSIDE

AF

LU T

BONITA

FAU

LT

TOLTEC FAULT

660 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 661 Figure 10. “High-pass” horizontal magnetic gradient (HMG) anomalies calculated from RTP magnetic data (fig. 7). Map shows magnetic gradients in the shallow crust as related to mineralized veins (black lines). MPU, magnetic potential units.

0

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as

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MPU/m
0

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n

e

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Cre ek

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Creek

 

An

mi as 

 

iv R er

Silverton calderastructural margin

BONITA FAULT

ROSS BASIN FAULT SUNNYSIDE FAULT

TOLTEC FAULT

EUREKA GRABEN

MPU/m

S Fork

Cem

e

n

t

C

r

Figure 11. “Low-pass” horizontal magnetic gradient (HGM) anomalies calculated from RTP magnetic data (fig. 7). Map shows magnetic gradients in the deep crust. 

Major faults are shown as black lines. MPU, magnetic potential units.

662 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 663 Figure 12. Profile illustration of various transformations applied to the Silverton magnetic data. A theoretical geologic cross section is shown below geophysical profiles to illustrate the geophysical signature of the transformations. The geologic cross section depicts a rock unit with higher magnetization (M1) juxtaposed next to a unit with lower magnetization (M2).

nT nT

Magnetic PotentialUnits

MPU/m (MPU)

Observed magnetic

anomaly

Reduced-to-pole

anomaly

Magnetic potential

anomaly Horizontal gradient magnitude 

M1 M2 M2

M1 > M2

M1 M2 M2

M1 > M2

M1 M2 M2

M1 > M2

M1 M2 M2

M1 > M2
0

0 5 KILOMETERS

5 MILES

 Animas

R vi

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Resistivity

ohm-m

Mi

n

e

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Cre ek

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C er ek

Figure 13. Map of 4,310-Hz apparent resistivity data showing lateral variations in electrical resistivity of rocks from surface elevations to approximate depths of 50 m. 

Data from this map are used to define resistivity characteristics of geologic structures.

664 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 665

Remote Sensing Data

The AVIRIS flightlines extend well to the southwest 

beyond the watershed boundary (fig. 2). Therefore, the AGP 

and ANP mineral maps were masked to correspond with the 

extents of the mapped faults and veins.

In addition to the data layers of acid-generating and acidneutralizing minerals previously described, a map of vegetation and snow cover was also produced from the AVIRIS data 

set (Dalton and others, this volume). For the purposes of this 

study, this map represents areas where the spectral signals of 

minerals are obscured, or in this case, represent areas of “no 

data.” As with the electrical resistivity data where the sensor 

was out of range with the ground, it was necessary to omit 

these areas from the probability ratio calculations. In order to 

mask the coverage of mapped faults and veins, we converted 

the vegetation and snow cover data from a red-green-blue 

(RGB) image to a binary data file (fig. 14). The mapped faults 

and veins data layer was then masked using the extents of the 

AVIRIS data. Finally, the resulting structural data layer was 

masked using the binary layer of vegetation and snow cover.

Results and Analysis

Planimetric Orientation of Geologic Structures 

and Geophysical Gradients

The planimetric orientations (orientation in the XY plane) 

of geophysical gradients were compared to trend orientations 

of mineralized faults and veins to determine if mapped surface 

geologic structures are parallel to magnetic and resistivity 

trends caused by subsurface features. The orientations are 

presented as rose diagrams that provide a straightforward 

means of comparison among the different data sets (fig. 15). 

Gradients of the residual reduced-to-pole field reflect changes 

in magnetic properties of rocks at the surface to depths of tens 

of kilometers, whereas the 4,310 Hz apparent resistivity data 

reflect gradients caused by lateral resistivity changes in rocks 

at the surface to depths of approximately 30 m.

Major northeast (approximately 45°), north (approximately 0°), and northwest (approximately 270°) structural 

trends are known to occur in the region of the Silverton 

caldera. Structural activity, throughout much of the geologic history of the region, has occurred along these trend 

lines (Burbank and Luedke, 1969). A prominent north-south 

(0°–10°) trend is observed in the mapped structure, which is 

parallel to the north-south trend observed in the magnetic and 

apparent resistivity gradients. This trend may be somewhat 

enhanced in the airborne geophysical data due to the northsouth flightline direction. The resistivity data are more sensitive to this effect as evidenced by the north-south trends that 

occur along a few flightlines.

A northeast trend is prominent in the orientation of 

faults and is evident to a lesser degree in the orientation of 

mineralized veins (fig. 15B, A, respectively). This trend of 

faults is likely the expression of the structures of the Eureka 

graben such as the Toltec and Sunnyside faults (see Yager and 

Bove, this volume, pl. 1). A northeast trend is also indicated 

in the rose diagram of apparent resistivity (fig. 15D), with 

a minor northeast trend shown in the magnetic gradients 

(fig. 15C). The rose diagram of the apparent resistivity gradient (fig. 15D) shows a strong easterly trend, correlating with 

east-west-trending silicic structures (Burbank and Luedke, 

1969) that are probably electrically resistive.

Weights Charts and Predictive Models

Modeling results are presented in two ways. The first is 

a chart showing probability weight values on the vertical axis 

versus evidential layer classes on the horizontal axis. Weights 

greater than 1 show where a range of data best characterizes 

the structures. Weights with values less than 1 indicate classes 

least likely to be associated with structures. Weights of zero 

value indicate classes in the evidential layers that had no 

overlap with geologic structures. Evidential layer classes with 

weights near 1 indicate that random spatial associations with 

geologic structures exist, and those layer classes are therefore 

not predictive of the presence or absence of veins or faults.

The second type of result of the modeling approach 

presented here is a predictive map. In the cases where a physical property characterized a prototype area, a predictive map 

was generated by redefining classes within the evidential layer 

in terms of their corresponding weights. Only classes with 

weights greater than 1 are shown. A weight greater than 1 

indicates that a particular topographic, geophysical, or mineralogical class exhibits a positive association to the prototype 

area. Colors are used in the predictive maps to show areas with 

ranked potential for the target characteristics from least (blue) 

to best (red).

Profile Convexity Model

The first predictive model compares the map of mineralized and nonmineralized faults and veins (fig. 3) with the raster image of topographic profile convexity (fig. 6). The chart 

of weights of profile convexity (fig. 16) shows that structures 

are most likely to occur along sharp ridges or in proximity to 

stream channels and incised valleys. Structures are least likely 

to occur where the convexity is near zero, or, where the terrain 

is flat. In fact, where the convexity is near zero the weight is 

less than 1, indicating a negative association of structures to 

low topographic gradients. This suggests that mapped mineralized faults and veins in the study area are most likely to occur 

as either erosionally resistive, and likely silicified structures, 

or as eroded, and possibly altered structures.

The predictive model map (fig. 17) shows where 

unmapped structures are most likely to occur based upon their 

topographic signature. Highest probabilities are shown in red 

and lowest probabilities are shown in blue. Several examples 

show where the model predicts that stream channels or ridges 
Figure 14. Data layers used to produce masked coverage of faults and veins from AVIRIS vegetation and snow cover 

mapping. A, The original map (from Dalton and others, this volume, Chapter E2). B, Binary mask produced from the vegetation 

map (black indicates areas of no mineralogical data). C, Mapped faults and veins masked using the AVIRIS data extents. 

D, The final masked coverage of mapped structure (red, structures; black, covered areas).

Vegetation

Wet vegetation

Dry vegetation

Melting snow

with vegetation

Snow, slush,

and vegetation

Snow and slush

Melting snow

Partially melted snow

Frozen snow

A B

C D

EXPLANATION

666 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 667

are most likely related to structures. Locality A (fig. 17), 

upstream of Chattanooga on upper Mineral Creek, has a high 

probability of association with structure and is mapped as a 

significant fault on the geologic map (Yager and Bove, 2002; 

Yager and Bove, this volume, pl. 1). High probabilities are 

also shown at B, near the confluence of Mineral Creek and 

South Fork Mineral Creek, and C, below Arrastra Creek; these 

are interpreted as structures associated with the structural margin of the Silverton caldera. Examples D, near the lower part 

of Cement Creek, and E, upstream of Animas Forks on the 

upper part of the Animas River, illustrate areas where faults 

have not been mapped, but these areas are predicted to be 

structurally controlled linear stream channels. Example F, near 

Storm Peak, is an area where numerous mineralized veins have 

been mapped (Yager and Bove, 2002; Yager and Bove, this 

volume, pl. 1). The profile convexity model suggests that these 

structures form erosionally resistive ridges and are therefore 

likely to be silicic in composition. The weights chart (fig. 16) 

shows that structures with similar positive convexity are more 

likely to occur in the study area based on their topographic 

characteristics.

Resistivity Model

Data from the 4,310 Hz resistivity grid were compared 

to mapped geologic structures to evaluate whether the structures have a characteristic electrical amplitude. Results show a 

strong association of moderately high resistivity with structure 

(fig. 18). The predictive map calculated from the statistical 

model (fig. 19) shows areas of higher weights that coincide 

with groups of structures. Whole rock chemical analysis of 

various members of Tertiary volcanic rock units show high 

weight percentages of silica (Luedke and Burbank, 2000), 

a correlation that is consistent with the high electrical resistivities that occur over parts of these units. Silica-rich rocks 

typically exhibit high electrical resistivities (Telford and 

others, 1976). The areas with high weights are interpreted 

to predict areas where silica-rich vein and volcanic mineralogy is predominant. In contrast, rocks that have undergone 

extensive hydrothermal alteration likely develop more clays 

and will map as low resistivity regions (structures within the 

gray areas in fig. 19). Areas where mapped structure does 

not coincide with high weights in the predictive model may 

Figure 15. Planimetric orientation of mapped geologic structure (A, B), and geophysical gradient trends from airborne geophysical 

survey (C, D). Rose diagrams of mapped structures were calculated as class percentages; rose diagrams of geophysical gradients 

were calculated as number per class.

8 7 6 5 4 3 3 2 2 1 1 4 5 6 7 8

0

270 90

Orientation of mapped mineralized veins

10 8 6 4 4 2 2 6 8 10

0

270 90

Orientation of mapped faults

2,800 1,400 0 1,400 2,800

270 90

0

Trends from gradient of residual reduced-to-pole field

1,500 0 1,500

270 90

0

Trends from gradient of 4,310 Hz apparent resistivity

A B

C D
point to faults and veins with higher clay contents and will 

be seen as areas of low electrical resistivity. Notably, the ring 

fault of the structural margin of the Silverton caldera is predicted to be electrically resistive in the Mineral Creek drainage, and generally more conductive along the drainage of the 

upper Animas River. This suggests that the ring structures 

associated with the upper Animas River are altered and likely 

clay filled, and may act as barriers to structurally controlled 

ground-water flow. The resistive ring structures found near 

the Mineral Creek drainage may also act as aquitards (a layer 

of rock or a geologic structure having low permeability that 

impedes ground-water flow) if these faults and mineralized 

veins are filled with quartz, as predicted by their electrical 

signature.

The result of the predictive modeling suggests that silicic 

mineralogy more commonly characterizes the faults and veins 

present in the watershed study area. Ground electromagnetic 

(EM) surveys conducted for this study in Placer Gulch (fig. 1) 

indicated high resistivity values over the mineralized veins. 

The predictive model also indicates that structures in this area 

are likely resistive, and therefore composed of silicic material. 

Figure 20 shows the location of the ground EM survey and 

known structures. Field reconnaissance has shown that silicic 

veins are common in the Placer Gulch area.

Magnetic Models

Magnetic Characteristics of Shallow 

Geologic Structures

The statistical methodology was used to evaluate 

whether the faults and veins have a characteristic magnetic 

signature as expressed by anomaly amplitude in the highpass magnetic map (fig. 8). Weights were computed using 

the high-pass magnetic anomaly data layer and geologic structures data layer. Results revealed a bimodal magnetic signature 

indicating that faults and veins are most likely to occur over 

PROFILE CONVEXITY (L -1)

WEIGHTS

concave (channels and valleys) flat terrain convex (ridges)

0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

Figure 16. Calculated weights of the relationship of structures to topography. Negative values indicate increasing concavity or 

channels; positive values indicate increasing convexity or ridgelines. Values of convexity are in units of 1 divided by length (Wood, 

1996).

668 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 669

very low amplitude magnetic anomalies and, to a lesser 

degree, over moderately high amplitude magnetic anomalies 

(fig. 21A). Initially, it may appear that there are more magnetic anomaly classes showing positive weights in the high 

magnetic anomaly range. However, most of the high magnetic 

value classes showing association with geologic structures 

occupy small areas. Most of these areas coincide with structures within the Sultan Mountain stock (Yager and Bove, this 

volume). In contrast, classes with high weights located within 

low magnetic anomaly classes occupy significantly more 

area in the overall magnetic anomaly grid. Consequently, low 

magnetic anomaly values better typify the magnetic signature 

of most of the faults and veins.

The horizontal gradient of the magnetic potential, calculated from the high-pass magnetic data, indicates a positive 

spatial association with steep magnetic gradients (fig. 21B). As 

discussed previously, magnetic gradients occur at boundaries 

between rocks with different magnetic properties. The steeper 

the gradient, the shallower the contact, or the greater the magnetic contrast between juxtaposed rock units.

0 5 MILES

0 5 KILOMETERS

Animas

R

Ce

ment

Cr

Min

eral Cr

S Fork Mineral Cr

Chattanooga

Arrastra Cr

Animas

Forks

Storm

Peak

A

B C D

E

F

A

B C D

E

F

Figure 17. Profile convexity predictive model of geologic structures. Blue, lower probabilities (weights); red, higher probabilities; 

yellow, moderate. A–F, areas where stream channels and ridges have high probabilities of structural control.
The predictive model calculated from the weights of 

the high pass HGM (fig. 22) shows areas where structures are 

likely to occur near the surface based on the magnetic gradient magnitude. In many areas, mapped structures coincide 

with the highest probabilities (areas in red). The model also 

identifies permissive areas for structures. Examples of these 

areas are shown in figure 22 at (A) Anvil Mountain, (B) east 

of Ohio Peak, (C) southeast of Emery Peak, and (D) northeast 

of Howardsville.

Magnetic Characteristics of Deep Crust 

Geologic Structures

Magnetic anomalies related to deep crustal features, 

such as covered parts of the Silverton caldera, are difficult to 

discern in the RTP map (fig. 7) because of the influence of 

shallower magnetic sources, which tend to mask anomalies 

associated with deeper sources. However, the low-pass magnetic anomaly and the horizontal gradient magnitude (HGM) 

of the low-pass magnetic field (figs. 9 and 11, respectively) 

enhance magnetic anomalies associated with buried features 

and allow for an evaluation of which geologic structures 

exposed at the surface may continue from the shallow crust 

to deeper levels.

A visual comparison of the low-pass HGM with mapped 

geologic structures (fig. 11) shows many of the fault structures 

mapped at the surface correlate with deeper magnetic boundaries or sources. The statistical comparison (fig. 23) performed 

between faults and the HGM low-pass magnetic data supports 

the visual observations. Low to moderate magnetic gradients 

are associated with the locations of fault structures. The structural margin of the Silverton caldera coincides with moderately steep magnetic gradient maxima that, for the most part, 

lie outside the Animas River and Mineral Creek drainages. 

The pronounced magnetic boundary that parallels the southeast part of the Silverton caldera ring fault (fig. 11) is likely 

the magnetic expression of the structural margin of the older 

San Juan caldera. The southwestern part of the Eureka graben 

is characterized at depth by the pronounced magnetic anomaly 

low in figure 11 that reflects a large volume of nonmagnetic 

volcanic rock. The Ross Basin and Sunnyside faults are 

located over moderate amplitude HGMs. The magnetic boundary that coincides with the Ross Basin fault, however, continues in a northwest and southeast direction beyond its surface 

expression. The southeast HGM projection of the Ross Basin 

fault crosses the Eureka graben where it intersects the Toltec 

fault. The northwest HGM projection of the Ross Basin fault 

continues another 2 km beyond its mapped extent (fig. 11).

Some of the major fault structures not expressed magnetically at deeper levels include the Toltec and Bonita faults. 

Though the Toltec fault does not coincide with a deep crustal 

magnetic boundary, a parallel HGM trend is obvious to the 

southeast. The Bonita fault is not expressed magnetically at 

deeper crustal levels, but the points at which it intersects with 

the Ross Basin and Toltec faults are present magnetically as 

moderate amplitude HGM (fig. 11).

RESISTIVITY, IN OHM-METERS

WEIGHTS

0

1

2

3

4

5

6

7

8

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600

Figure 18. Weights of association of electrical resistivity with mapped structures.

670 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 671

0

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Mineral Cre ek

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Figure 19. Electrical resistivity predictive model of geologic structures. Cooler colors, lower probabilities (weights); warmer colors, higher probabilities. 

White, areas of no data; gray, areas of no overlap.
Figure 20. Predictive model of resistivity (left) showing location of ground electromagnetic (EM) survey (white dashed line) in Placer Gulch. Only probabilities greater than 1 

are shown. Higher probabilities of structures are shown in red. Photo (right) shows example of silicic vein in Placer Gulch. Height of outcrop is approximately 25 m.

0 5000 250 500 METERS

1000 FEET

N

672 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 673

Figure 21. Calculated weights of A, magnetic high-pass filtered data, and B, high-pass filtered horizontal 

gradient magnitude (HGM) associations with geologic structure. WEIGHTS WEIGHTS

A

B

0

1

2

3

4

5

6

7

8

9

-200 -150 -100 -50 0 50 100 150 200 250 300 350 400 450 500

0

1

2

3

4

5

6

7

8

9

50 100 150 200 250 300 350 400 450

NANOTESLA

MAGNETIC POTENTIAL UNITS PER METER (VALUES x 105)
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A

B

C

D

Figure 22. High-pass horizontal gradient magnitude (HGM) magnetics predictive model of geologic structures. Cooler colors, lower probabilities; warmer 

colors, higher probabilities. White, areas of no data. Examples of permissive areas for structures are shown at A, B, C, and D.

674 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 675

In addition to the linear magnetic boundary that parallels 

the Toltec fault, other magnetic gradient trends are present that 

are parallel to either mapped structure or drainage trends. Two 

of the more interesting of these are the magnetic boundaries 

that parallel the semi-elliptical trend formed by Cement Creek 

and South Fork Cement Creek. Arcuate magnetic boundaries 

mimic this pattern to the south of the drainages. The concentric nature of these magnetic boundaries could be an expression of the resurgent doming that occurred in the formation 

of the nested Silverton and San Juan calderas (Luedke and 

Burbank, 2000).

Steepest magnetic gradients present in figure 11 coincide with the contact between the magnetic Tertiary Sultan 

Mountain stock and the surrounding, less magnetic country 

rock. The steep magnetic gradients that coincide with the edge 

of the Sultan Mountain stock are not reflected in the weights 

chart (fig. 23) because only fault structures were compared to 

the HGM of the low-pass magnetic data. The elongate HGM 

feature associated with the Sultan Mountain stock is a prominent element in figure 11, however, and marks a significant 

through-going geologic boundary. It therefore warrants some 

discussion. Comparison of the mapped outcrop of the Sultan 

Mountain stock (Yager and Bove, this volume, pl. 1), west of 

Silverton, with the HGM shows a close coincidence between 

the two features. This coincidence supports a nearly vertical 

sided geometry to a depth of at least 3 km. Other unnamed 

Tertiary stocks that occur within and near the periphery of the 

Silverton caldera are associated with the large semicircular to 

scalloped-shaped long-wavelength magnetic high that rings 

the Silverton caldera (fig. 9). Although their mapped extents 

do not have as close a coincidence with magnetic boundaries, 

most of the Tertiary stocks occur near parallel magnetization 

boundaries. Magnetic rock properties determined from samples of the Sultan Mountain stock and other unnamed Tertiary 

intrusions show them to possess some of the highest magnetic 

susceptibilities in the study area (Gettings and others, 1994). 

Consequently, the Tertiary plutons are a likely source for 

the arcuate magnetic high, and the continuous nature of this 

feature suggests that the plutons may be connected, forming a 

continuous mass at depth.

Mineralogic Models

The weights charts produced from comparison of mapped 

structure to AVIRIS mapped mineralogy are intended to provide a general characterization of mineralized faults and veins 

in terms of their acid-generating or acid-neutralizing potential (AGP, ANP). A limitation to the AVIRIS mineral maps 

and their relationship to structure is that in many parts of the 

study area the surface mineralogy is not exposed because of 

Figure 23. Calculated weights of geologic structure association with low-pass magnetic horizontal gradient 

magnitude (HGM).

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

0 50 100 150 200 250 300 350 400 450 500 550

WEIGHTS

MAGNETIC POTENTIAL UNITS PER METER
vegetation cover or snow cover. Also, in some cases, structures 

may not have been mapped because they are covered with soil, 

alluvium, or talus.

The chart of weights of the AGP group minerals 

(fig. 24A) shows a random association of jarosite, goethite, 

and hematite to structures within the watershed. Structures 

are most likely to be associated with amorphous iron oxide 

and iron hydroxide minerals. This model would suggest that 

structures do not have a positive association with acid production based upon their mineralogic distribution. However, this 

does not preclude that there are structures that have provided 

discrete pathways for acidic and metal-rich waters.

The weights chart for the ANP group minerals (fig. 24B) 

indicates positive associations of epidote and chlorite, chlorite, 

and mixtures of chlorite, muscovite and calcite, to mapped 

structure. This is consistent with the pervasive occurrence 

of propylitically altered rocks in the study area. The random 

association of the “pure” end members of calcite and epidote 

suggests that these minerals occur primarily in mixtures with 

the other minerals of the propylitic alteration assemblage. 

Calcite is reported to have only 1 percent abundance in veins 

near the Sunnyside mine workings (Casadevall and Ohmoto, 

1977), so that calcite would not be expected to have a positive 

association with structure.

Model Validation

Validation, or verification of results, is a fundamental 

component of any predictive modeling investigation (Chung 

and Shaw, 1999). The validity of the models produced in this 

study was analyzed in several ways. First, we examined the 

stability of the models by random alteration of the reference 

area data. Second, the topographic and geophysical models 

were compared to two geologic cross sections. Finally, we 

used the predictive maps to guide field investigations of structures expressed at the surface.

Model Stability

The stability of the models (also referred to as sensitivity 

by Lee and others, 2001) is an indication of how dependent 

the success rate of spatial association is between an evidential layer and a prototype area with respect to changes in the 

definition of the reference area (in this case the mapped faults 

and veins). To test the stability of the models, we compared 

the various evidential layers (profile convexity, resistivity, 

and magnetics) to the prototype layer which was randomly 

decimated to 50 percent of the original population of structures. Probability ratios were calculated using the decimated 

reference area, and weights charts were prepared.

Comparison of the weights charts (fig. 25) shows that the 

models produced from the decimated reference data layer have 

similar distribution characteristics to the models calculated 

from the original reference data layer. This indicates that the 

models are not dependent on the definition of the reference 

layer, and are therefore considered stable. An exception is seen 

in the high-pass filtered magnetic models in the high amplitude anomaly range (≈300–500 nT). Weights tend to be higher 

in the model produced from the decimated reference layer and 

there is no overlap in this model in the ≈470–500 nT range. 

However, as previously discussed, the overlap area of structures with high-pass magnetic data in this range tends to be 

smaller than in the low amplitude anomaly range. Therefore, 

the low amplitude range better typifies the magnetic signature 

of most of the faults and veins.

The data layers of ANP and AGP minerals were not modeled using the decimated reference data layer because of the 

relatively sparse density of AVIRIS mineralogic data coverage. 

The extent to which the mineralogy is obscured can be seen 

in figure 14. In fact, 71 percent of the study area is covered by 

vegetation and snow, and 66 percent of the mapped structures 

are covered.

Comparison of Geophysical and Profile Convexity 

Profiles to Geologic Cross Sections

A second aspect to the model validation includes a 

comparison of predicted locations of geologic structures to 

mapped structures in profile. Geologic cross sections (locations shown on map, fig. 1) in the western and southeastern 

parts of the study area are available from geologic mapping 

of the Silverton and Howardsville 7.5-minute quadrangles 

(Luedke and Burbank, 2000). Data that co-registered with 

the cross section locations were extracted from the high-pass 

magnetic, 4,310 Hz resistivity, and profile convexity grids. 

The north profile, A–A' (fig. 26), runs from west to east and 

begins north of Middle Fork Mineral Creek, crosses south of 

Ohio Peak, and ends southwest of Storm Peak. The profile is 

entirely within the Silverton caldera. The south profile, B–B' 

(fig. 27), begins at the southern ridge of Storm Peak, continues 

in a southeasterly direction across the upper Animas River to 

Hazelton Mountain, and ends on the north ridge of Whitehead 

Peak. In terms of geological extent, this profile begins in the 

Silverton caldera, crosses its structural margin, and continues 

beyond the outer rim of the older San Juan caldera.

Weights of predicted locations of geologic structures 

from the various data layer models are shown as red bars 

below the corresponding geophysical or profile convexity 

profiles. The variable bar heights indicate the relative strength 

of positive spatial associations, with only weights greater than 

1 shown. Therefore, the values on the vertical axis on all the 

cross sections correspond only to the values of magnetics (nT), 

resistivity (ohm-m), profile convexity (L–1), or elevation (m). 

Small squares on the geophysical or profile convexity profiles 

indicate the projection of a mapped mineralized fault or vein 

(red), an andesitic dike (green), or a fault (black) onto the 

profile for reference.

676 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 677

Figure 24. Calculated weights of geologic structure association with A, acid-generating 

potential (AGP), and B, acid-neutralizing potential (ANP) group minerals based on mineralogic 

analysis of AVIRIS data. goethite

jarosite

hematite

nanocrystalline

hematite

amorphous FeO and

FeOH

WEIGHTS WEIGHTS

0

0.2

0.4

0.6

0.8

1.0

1.2

0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

calcite

calcite+epidoteepidote+calcite

epidote

epidote+calcite+chlorite

epidote+chlorite

chlorite

chlorite+muscovite

muscovite/sericite

muscovite w/calcite and (or) chlorite

A

B
0

1

2

3

4

5

6

7

8

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600

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7

8

200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200 2,400 2,600

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9

50 100 150 200 250 300 350 400 450

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9

50 100 150 200 250 300 350 400 450

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1.6

50 100 150 200 250 300 350 400 450 500 550

0

0.2

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0.6

0.8

1.0

1.2

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1.6

50 100 150 200 250 300 350 400 450 500 550

50% structure population

WEIGHTS

WEIGHTSWEIGHTS

WEIGHTSWEIGHTS

WEIGHTS

WEIGHTS

WEIGHTSWEIGHTS

CONVEXITY (L-1)

OHM-METER OHM-METER

NANOTESLA NANOTESLA

MAGNETIC POTENTIAL UNITS PER METER (VALUE x 105)

Profile convexity

models

Resistivity

models

High-pass

filtered magnetic

models

High-pass

filtered HGM

magnetic models

Low-pass

filtered HGM

magnetic models

0

1

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3

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7

8

9

-200 -150 -100 -50 0 50 100 150 200 250 300 350 400 450 500 0

1

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-200 -150 -100 -50 0 50 100 150 200 250 300 350 400 450 500

0

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-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8

CONVEXITY (L-1)

0

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3.5

-1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2

100% structure population

WEIGHTS

MAGNETIC POTENTIAL UNITS PER METER (VALUE x 105)

MAGNETIC POTENTIAL UNITS PER METER (VALUE x 105) MAGNETIC POTENTIAL UNITS PER METER (VALUE x 105)

Figure 25. Comparison of predictive models for verification. Models in left column were produced using 100 percent 

of mapped structures; models in right column were produced using a randomly decimated population of 50 percent of 

mapped structures.

678 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 679 Figure 26. High-pass magnetic, 4,310 Hz apparent resistivity, and profile convexity profiles over geologic cross section (Luedke and Burbank, 2000). Boxes on profiles map locations of mineralized veins or faults (red). Locations of predicted structures from modeling are shown as red bar charts at bottom of related profile. Predicted structures with coincident locations in all three models are shown as white dashed lines. Location of section is shown on figure 1.

Surficial deposits

Tertiary extrusive rocks

Quaternary

Mineralized vein or

fault Dashed whereinferred or covered;barbs showdirection of relativemovement

E

X

P

LA

N

ATIO

N

Mineral Creek

US ROUTE 550

Browns GulchOhio Peak

Ohio Gulch

Cement Creek

Storm Peak

SILVERTON AND SAN JUAN CALDERAS

4,000

3,500

3,000

2,500

NO VERTICAL EXAGGERATION

METERS

WEST EAST

Predicted weights greater than 1

-100

-50

0

50

150

100

MAGNETICS (nTesla)

0

500

3,0002,500

2,000

1,5001,000

APPARENT RESISTIVITY (OHM-M)

-1

2

10

-2 PROFILE CONVEXITY (L-1) 

Profile A-A'

0 0.5 1 1.5 2 KILOMETERS

1 MILE 0

Eureka Member of Sapinero Mesa Tuff (Oligocene)

San Juan Formation (lower Oligocene)

Predicted structures with weights greater than 1 in all three models 

Pyroxene andesite memberBurns Member

Silverton Volcanics(Oligocene)

—
4,000

3,5003,000

2,500

2,000

SILVERTON CALDERA

SAN JUANCALDERA

STATE ROUTE 110

Hazelton

Mountain

Woodchuck

Basin

Kendall Peak

Arrastra

Basin

Spencer

Basin

NO VERTICAL EXAGGERATION

0

800

400

2,000

1,600

1,200

APPARENT RESISTIVITY (OHM-M)

END OF GEOPHYSICAL SURVEY

-100

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-50

0

50

100

MAGNETICS (nTesla)

END OF GEOPHYSICAL SURVEY

Surficial deposits

Quaternary

Tertiary extrusive rocks

Precambrian

Metamorphic rocks

EXPLANATION

Predicted weights greater than 1

NORTHWEST SOUTHEAST

3

0

-1

-2

2

1

PROFILE CONVEXITY (L-1)

Profile B-B'

?

? ? ?

Pyroxene andesite member

Burns Member

Eureka Member of Sapinero Mesa Tuff (Oligocene)San Juan Formation (lower Oligocene)

Mineralized vein or faultAndesite dikeFault Dashed where covered or inferred

METERS

Silverton Volcanics(Oligocene)

Predicted structures with weights greater than 1 in all three models Queried where extent uncertain

—

—

Boulder Gulch

0 0.5 1 1.5 2 KILOMETERS

1 MILE 0

Animas River

Figure 27. High-pass magnetic, 4,310 Hz apparent resistivity, and profile convexity profiles over geologic cross section (Luedke and Burbank, 2000). Boxes on profiles map locations of mineralized veins or faults (red), andesite dikes (green), and faults (black). Locations of predicted structures from modeling are shown as red bar charts at bottom of related profile. Predicted structures with coincident locations in all three models are shown as white dashed lines. Location of section is shown on figure 1.

680 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 681

In general, for both profiles, the majority of predicted 

locations of faults and veins lie along geophysical anomaly 

gradients and not on resistivity or magnetic anomaly peaks. 

Additionally, predicted locations tend to intersect the gradients of resistivity anomalies with moderately high values. 

Strongest spatial associations in the resistivity model (fig. 18) 

are defined by higher weights within a moderately high to 

high resistivity value range. Where high resistivity values are 

associated with structures, the high values tend to be due to 

infilling of structures with silicic material. Less common but 

still present are lower model weights, barely above a probability ratio of 1, which point to geologic structures located in 

electrically conductive (low resistivity) rock and soil material 

such as clays.

The predicted locations of geologic structures along the 

magnetic anomaly profiles share a similarity with those of the 

resistivity data in that predicted locations of faults and veins 

occur along magnetic gradients. However, in contrast to the 

resistivity data, geologic structures occur over a wide range 

of magnetic anomaly values indicating that geologic structures 

occur in rocks with a wide range of rock magnetizations.

The profile convexity model predicts the occurrence of 

structures primarily in areas of moderate to extreme convexity. 

A general observation of the convexity models in both cross 

sections indicates that an overabundance of structures seems to 

have been predicted. Note, however, that many of the weights 

shown have values just slightly greater than 1, and therefore 

should be considered significant only where groupings of 

higher weights occur.

On geologic cross section A–A' (fig. 26), the majority 

of mapped mineralized veins and faults are located west of 

Mineral Creek and are part of a fault zone that defines the 

structural margins of the San Juan and Silverton calderas, 

which are interpreted to be more or less coincident here. 

The caldera margins are characterized by faults and fractures 

that have provided ample pathways for the emplacement of 

intrusive igneous material and mineralized fluids (Luedke 

and Burbank, 2000).

All three models show a number of predicted structures 

coincident with mapped structures (table 1). Between the west 

end of profile A–A' and Mineral Creek, there are 14 mapped 

mineralized veins. Out of the 14, the magnetic model located 

8 (57 percent), the resistivity model located 12 (86 percent), 

and the profile convexity located 7 (50 percent). High weights 

in association with high resistivities and positive convexities 

suggest that this area is extensively silicified from the intense 

veining. The magnetic anomaly highs over this area, however, suggest that the nonmagnetic character of the silicification is relatively thin (50 m or less). The magnetic signature 

likely relates to the greater volume of San Juan Formation 

that underlies the Oligocene Eureka Member of the Sapinero 

Mesa Tuff and Burns Member of the Silverton Volcanics. 

Rock magnetic property measurements on rocks within the 

Silverton caldera show the San Juan Formation volcanic 

rocks to be significantly more magnetic than those of the 

Eureka or Burns Members (Gettings and others, 1994). Along 

the caldera margin, segments of the San Juan Formation have 

been uplifted to shallower levels in the fault blocks along the 

caldera margin, which best explains the presence of magnetic 

highs in this area of profile A–A'.

A significant drop in resistivity occurs at Mineral Creek 

and continues east along profile A–A' across Browns Gulch 

to Ohio Peak. The convexity profile also drops significantly 

at Mineral Creek, which would suggest that, at least along 

this reach, the stream is structurally controlled. The extreme 

negative convexity and low resistivity values are characteristic 

of areas where alteration is associated with zones of crustal 

weakness. This section of the profile is entirely within the 

Silverton and San Juan calderas and mapped as the Burns 

Member of the Silverton Volcanics blanketed with a thin 

veneer of Quaternary surficial deposits. In map view, the 

resistivity low (fig. 13) coincides with a triangular magnetic 

anomaly low (fig. 8). A slight increase in resistivity and magnetic anomaly amplitudes occurs within the center of the lows 

along the west flank of Ohio Peak. In map view, the subtle rise 

in resistivity and magnetic values within the larger resistivity 

and magnetic lows is suggestive of an intrusion at depth that 

has extensively altered the surrounding country rock. Eight 

mapped structures are present in this area of intense alteration, 

and the magnetic and resistivity models did a poor job of predicting the exact locations of this set of faults (13 and 25 percent of the structures were predicted, respectively). Much of 

this part of A–A', especially between Browns Gulch and Ohio 

Peak, is covered with surficial deposits. The resistivity model 

predicts structures within this more electrically conductive 

area, but not in association with mapped structures. Because 

the modeling is based on an area calculation, we suggest that 

the silica-rich structures are more common (cover a larger 

area) in the study than structures filled with clay-rich material, 

such as would be produced in areas with intense alteration.

The magnetic and resistivity anomaly highs that occur 

to the east of Ohio Peak are offset from the topographic peak. 

The offset from the mountain top may be due to the presence 

of more pyroxene andesite in the subsurface between Ohio 

Peak and Ohio Gulch. The pyroxene andesite is one of the 

most magnetic and resistive rock types within the study area.

The magnetic, resistivity, and profile convexity models 

would all suggest that Ohio Gulch is structurally controlled 

based on the presence of predicted structures in all three models. Highest weights in the magnetic model for profile A–A' 

occur at Ohio Gulch. Geophysically, Ohio Gulch is characterized by low magnetizations and resistivities, suggesting a 

structure or structures likely dominated by clay rather than 

silica mineralogy. Topographically, structures are indicated 

in this area by the negative convexity.
Storm Peak is covered with the same pyroxene andesite 

rock as at Ohio Peak, and magnetic and resistivity anomaly 

highs with large amplitudes occur here as well. Thirteen 

structures are mapped between Ohio Gulch and Storm 

Peak, with 10 of the 13 mineralized veins located within the 

pyroxene andesite of Storm Peak. As is the case for most 

of the study area, the resistivity model predicted geologic 

structures within the moderately high value resistivity range 

that occurs over Storm Peak. Of the 10 mineralized veins 

that occur within the pyroxene andesite, the resistivity model 

predicted 9, or 90 percent. The magnetic data predicted only 

50 percent. Apparently, the pyroxene andesite at Storm Peak 

is fairly homogeneous in its magnetic properties and, although 

fractured, does not possess enough magnetization contrast 

within the fractures to produce variations in the magnetic 

anomaly.

Profile B–B' (fig. 27) extends for a distance of approximately 9 km, with coincident airborne geophysical data 

available for the northwesternmost 6 km. Four different types 

of geologic structures are mapped along the profile, including 

mineralized veins, faults, andesitic dikes, and nonmineralized 

faults.

From the northwest end of the profile to the Animas 

River, the profile is entirely within the Silverton caldera. One 

of the most pronounced features within this profile segment 

is the resistivity high occurring over Boulder Gulch. The 

resistivity model predicted 87 percent (table 1) of the mapped 

mineralized veins in this segment, with the distinct resistivity high likely due to intense silicic veining. The magnetic 

signature here is also consistent with a broad area containing a 

silicic mineralogy, as evidenced by magnetic anomaly values 

near zero. However, in terms of predicting the individual 

structures, the magnetic model did a poor job, only predicting 

37 percent of the mineralized vein locations. The low likelihood of structures in the magnetic model coincides with the 

lack of pronounced magnetic gradients, as would be expected 

over an area with a homogeneous magnetic mineralogy. The 

profile convexity model predicted 75 percent of the mapped 

Table 1. Numerical comparison of geologically mapped structure locations along profiles A–A' (fig. 26) and B–B' (fig. 27) to locations 

of geologic structures predicted by high-pass magnetic, 4,310 Hz resistivity, and profile convexity models.

Number of 

mapped structures 

(Luedke and 

Burbank, 1990)

Predicted number of structures 

(percent predicted out of total structures)

High-pass

magnetic

4,310 Hz

resistivity

Profile

convexity

Profile A–A'

West end of profile 

to Mineral Creek 14 8 (57%) 12 (86%) 7 (50%)

Mineral Creek 

to Ohio Gulch 8 1 (13%) 2 (25%) 2 (25%)

Ohio Gulch 

to Storm Peak 13 4 (31%) 8 (62%) 10 (77%)

TOTAL

Profile A–A' 35 13 (37%) 22 (63%) 19 (54%)

Profile B–B'

NW end of profile 

to Animas River 8 3 (37%) 7 (87%) 6 (75%)

Animas River 

to Hazelton Mtn 12 6 (50%) 8 (67%) 10 (83%)

Hazelton Mtn 

to Kendall Peak 7 5 (71%) 5 (71%) 5 (71%)

TOTAL

Profile B–B' 27 14 (52%) 20 (74%) 21 (78%)

682 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 683

structures in Boulder Gulch, and the model indicates that 

structures have both positive and negative convexity values. 

Notably, the Animas River is marked by an extreme negative convexity and low resistivity analogous to that seen in 

profile A–A' near Mineral Creek, suggesting a similar altered 

environment.

The contact between the Silverton and San Juan calderas is located between the Animas River and Hazelton 

Mountain and marked by a diversity of geologic structure 

types. The magnetic, resistivity, and profile convexity models 

did well predicting the zone of geologic structures marking 

this contact (table 1). This section of the profile is characterized by steep geophysical gradients, high resistivity, and low 

magnetizations.

Once the caldera contact is crossed, the magnetic anomaly amplitude increases and likely is the result of the more 

magnetic mineralogy of the San Juan Formation. Plutonic 

rocks similar to those exposed within the Sultan Mountain 

stock may be present in the subsurface. The magnetic high is 

part of a ring-shaped high that is a pronounced feature in the 

magnetic maps (figs. 7, 8, and 9) and is connected to the west 

with the magnetic Sultan Mountain stock. The northwestdipping mapped andesite dike approximately 500 m northwest 

of Hazelton Mountain is shown in the profiles to be erosionally and electrically resistive, and magnetic. These characteristics are consistent with the properties of other andesite dikes in 

the study area.

Hazelton Mountain is characterized topographically by 

extreme positive convexity. Near the summit the convexity 

decreases substantially in apparent correlation with a mapped 

structure. The geophysical profiles indicate that this structure 

is relatively nonresistive and nonmagnetic, and therefore, is 

likely altered.

In Woodchuck Basin (fig. 27), the mapped structures are 

characterized by somewhat positive to significantly negative 

convexity, and they are nonresistive (conductive) and nonmagnetic. As in other cases, these characteristics are typical of 

altered and likely clay filled structures.

Near the end of the geophysical profiles the resistivity begins to increase and the magnetic profile goes sharply 

negative. This is interpreted as the geophysical response to 

Precambrian metamorphic rocks mapped at depth. However, 

because the profiles here are at the limit of the geophysical survey, edge effects resulting from filtering of the data 

may introduce inaccuracies that should be considered in the 

interpretation.

A final utility of the profiles comparing the topographic 

and geophysical data and predictive models to mapped structures was to determine where predicted structures with weights 

greater than 1 had coincident occurrence in all three models. 

We assumed that where the three models all predict structures, 

locations are identified where previously unmapped structures 

may exist. In figure 26 and figure 27 the predicted structures 

are shown as white dashed lines on the geologic cross sections.

For profile A–A', 16 “structures,” or locations of coincidence, were identified. Notably, Browns Gulch, Ohio Peak, 

and Ohio Gulch are favorably associated with predicted 

structures. A total of 35 structures (table 1) are mapped on this 

profile, which means that 46 percent more mineralized and 

nonmineralized faults and veins are predicted. Profile B–B' 

shows seven predicted structures, or 26 percent more than are 

mapped in the cross section.

Field Verification

In practice, verification by direct observation in the field 

or by comparison to known data, such as geologic maps, can 

be the most critical test of a predictive model. As previously 

discussed, the profile convexity predictive model map (fig. 17) 

shows examples of coincidence of predicted structures with 

stream channels or ridges. These examples are typical of the 

agreement between the topographic model and known mapped 

structures throughout the study area.

The ground EM survey conducted in Placer Gulch, as 

previously discussed, is an example of agreement between the 

resistivity model and direct field investigation (fig. 20). The 

model predicts that structures in this area should be electrically resistive, and the results of the ground surveys support 

this prediction.

During the fall of 2003, field investigations were guided 

in part by the predictive models of topography, resistivity, 

and magnetics. Areas of interest included Boulder Gulch and 

the South Fork Cement Creek (fig. 1). Figure 28 shows an 

example in Boulder Gulch of a mapped silicified vein that, as 

indicated by the airborne geophysical data, is in an area that 

is moderately resistive and nonmagnetic. The profile convexity model predicts that a structure should exist in this location 

based on the topographic characteristics. Although less precise 

in location, the resistivity model indicates that the mapped 

vein is in an area favorable for electrically resistive structures. 

The high-pass HGM magnetic model predicts that the mapped 

structure is near a steep magnetic gradient, suggesting a 

significant difference in the magnetic properties of juxtaposed 

rock types.

Figure 29 shows an area near the headwaters of South 

Fork Cement Creek where alteration (vein-related quartzsericite-pyrite; Bove and others, this volume) and subsequent 

erosion have occurred. Notably, the profile convexity model 

predicts a structure in this location. However, the geologic 

map of faults and veins (Yager and Bove, 2002; Yager and 

Bove, this volume, pl. 1) does not show a mapped structure associated with the gully. The resistivity map indicates 

that this altered and eroded zone is electrically nonresistive 

(conductive), which suggests the presence of wet clays or 

high dissolved solids in surface or ground water. The resistivity and high-pass HGM magnetic models show the area to be 

moderately permissive for structures based on the electromagnetic and magnetic gradient characteristics.
Silicified vein (shown at arrow) and adjacent fault in west-trending tributary of Boulder

Gulch (view looking west). Height of outcrop at arrow approximately 2 m. 

Profile convexity model and location of example silicified vein

(shown at arrow). Probabilities are shown as blue (lowest) to red (highest).

Resistivity map. Resistivity values are shown as blue (lowest) to red (highest). Resistivity model. Probabilities are shown as blue (lowest) to red (highest).

High-pass horizontal gradient magnitude (HGM) magnetics map.

Gradients are shown as blue (lowest) to red (steepest).

High-pass horizontal gradient magnitude (HGM) magnetics model.

Higher probabilities are shown as blue (lowest) to red (highest).

Boulder Gulch

Boulder Gulch

Boulder Gulch

Bou

lder Gulch

0 250 500 METERS

0 500 1000 FEET

N

Figure 28. Comparison of profile convexity, resistivity, and magnetic models and maps to a silicified vein in Boulder Gulch. Location 

of the vein, approximately 900 m west of Macomber Peak (fig. 1) and shown in upper left photograph, is indicated on the ground by 

white arrows on the maps.

684 Environmental Effects of Historical Mining, Animas River Watershed, Colorado
Characterization of Geologic Structures Using Statistical Modeling 685

View looking west-southwest of altered and eroded zone near

South Fork Cement Creek. Man for scale in foreground.

Profile convexity model and location of altered zone (shown at arrow).

Probabilities are shown as blue (lowest) to red (highest). South Fork Cement Creek

Resistivity map. Resistivity values are shown as blue (lowest) to red (highest). Resistivity model. Probabilities are shown as blue (lowest) to red (highest).

High-pass horizontal gradient magnitude (HGM) magnetics map.

Gradients are shown as blue (lowest) to red (steepest).

High-pass horizontal gradient magnitude (HGM) magnetics model.

Higher probabilities are shown as blue (lowest) to red (highest).

0 250 500 METERS

0 500 1000 FEET

N

Figure 29. Comparison of profile convexity, resistivity, and magnetic models and maps to an altered and eroded zone 

near the headwaters of South Fork Cement Creek. Location of the zone, shown in upper left photograph, is indicated on 

the ground by white arrows in the maps.
Conclusions and Discussion

This investigation was conducted to define and analyze 

the topographic, geophysical, and mineralogical characteristics 

of faults and veins within the study area. This information was 

used to predict permissive areas for unmapped geologic structures in the watershed. Topographic, geophysical, and mineralogical attributes were quantified for geologic structures, and 

the statistical method employed identified target areas for further investigation. In map form, the predictive models showing 

high probabilities accurately locate many known structures. 

More interestingly, the models suggest structures in areas 

where structures had not been previously mapped. Many of 

the permissive areas lie in places of restricted access, limited 

outcrop, and thick alluvial, soil, or vegetation cover.

Topographically, structures are characterized by erosionally resistant and likely silicified ridgelines, or as eroded and 

incised channels and valleys. The predictive model of structure 

derived from the topographic criteria identifies multiple areas 

where faults and veins extend beyond their previously mapped 

extent, or where unmapped structures may occur.

The planimetric orientation of electrical resistivity gradients identifies a prominent east-west orientation that correlates 

with one trend of unmapped structures. In terms of structurally 

controlled ground-water flow, faults and veins of this orientation would be least likely to provide flow paths. The resistive 

geophysical signature suggests that these structures are quartz 

filled and extend to a depth of at least 60 m. In areas where 

quartz-filled structures are fractured, secondary permeability 

may be considerable.

Magnetically, the faults and veins are characterized by 

either extreme low magnetizations or moderately high magnetizations, and moderately steep magnetic gradients. Magnetization boundaries associated with rocks at depths exceeding 

1 km identify surface structures that may provide connectivity between the topographic surface and deep crust. Buried 

crustal boundaries not mapped at the surface are suggested 

by the magnetic data. Faults and veins are characterized by 

moderately high electrical resistivities and may point to more 

silica-rich phases within the Silverton Volcanics. The predictive model calculated from the electrical resistivity characteristics of structures identifies areas where faults and veins are 

resistive at depth, and as previously discussed, least likely to 

be structurally controlled ground-water conduits, except where 

significantly fractured.

Based on the results of the AVIRIS analysis, the mineralogical character of structures in terms of their acid-generating 

or acid-neutralizing potential suggests a random association 

with acid production resulting from the weathering of pyrite. 

Structures are most likely to occur in propylitically altered 

areas, and therefore are associated with rocks with the highest 

acid-neutralizing potential.

The predictive models presented here could be used to 

guide further geologic mapping and investigation, incorporated 

in hydrologic applications, or considered in remediation planning. The implication that significantly more mineralized and 

nonmineralized faults and veins (approximately 36 percent on 

average) may exist in the watershed than previously mapped 

might be important in terms of estimating fracture permeability that would be included in ground-water modeling efforts.

The methods and approaches employed in this study 

can be applied to other watersheds with similar structural and 

volcanic regimes containing acid-generating historical mines 

or source rocks. Further investigations may benefit from 

integration of the topographic, geophysical, and mineralogical 

characterization of structures presented here, with geochemical and geologic analysis to identify discrete ground-water 

flow paths and structurally controlled inflows to streams.

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