1

Resistivity and Terrain Conductivity Surveys over aRefuse Pile in Northern West Virginia

byYen TangSenior Thesis (completed April 2001)Department of Geology and GeographyWest Virginia University

2

Resistivity and Terrain Conductivity Surveys over a Refuse Pile in Northern West VirginiaA Senior Thesis (3 credit hours) by Yen Tang (Class of 2001)

Abstract

This study evaluates the geophysical characteristics of the Falls Run refuse pile

operated by the Preston County Coke and Coal Association. This area has caused minor

environmental problems, particularly from the acid water seeping out of the refuse pile.

Remedial activities undertaken by the Preston County Coke and Coal Association were

undertaken in order to protect Decker's Creek and Falls Run from water contamination. In

hopes of neutralizing the acidity of the refuse pile, lime slurry from the Greer treatment

ponds has been dumped on top of the refuse pile. However, this method of treatment failed

and acid seeps eventually formed. This study and another comparative study of the site (see

Shogren, 2001) are designated to estimate the distribution and possible location of AMD

source areas within the refuse pile. Several geophysical methods were used in this study,

including terrain conductivity, magnetics, and resistivity. Surveys were conducted over the

site and analysis of the data indicates distinct anomalous regions. The use of these methods

will provide information about the variation of conductivity and resistivity within the refuse

pile and help locate acid seep source area(s). Surveys were also conducted to determine

whether seasonal changes in ground conductivities might affect measured conductivity

variations. Finally, a number of models were developed to locate the anomalous regions.

Introduction

The production of AMD has proven to be a major problem in West Virginia. The

source of the contamination is in the coal itself. Coal refuse from the Pittsburgh and Freeport

coals, mined in nearby areas, contain a high percentage of pyrite. Coal mine refuse piles and

mine spoils act as surficial sources of pollution in the stream systems of northern West

3

Virginia and elsewhere in the coal fields of Appalachia. This particular study analyzes the

Falls Run refuse pile and attempts to locate AMD source areas, so that the refuse pile

operator can exhume and treat the source areas directly.

The Falls Run refuse pile is located in Preston County, West Virginia. Its coordinates

are 79°W 49’ 35” and 39°N 33’ 49” and can be found on the Masontown 7.5 minute

quadrangle topographic map (Figure 1). Figure 2 shows the site area itself. The Preston

County Coke and Coal Association used the area to dump coal refuse. The refuse was

deposited on top of the original topographic surface. The stratigraphy of this area consists of

four parts: the underlying bedrock, a layer of coal refuse, a layer of alkaline sludge and lime

slurry, and a layer of limestone. Both the sludge and limestone were added to the refuse pile

to help neutralize the pH of the water draining through coal refuse pile.

The history of the site begins in 1983-84 when coal refuse was first dumped at the

site. The site was recontoured and the topographical slope angle increased and the surface

became hummocky and uneven. A network of drainage ditches located at the toe of the slope

was constructed to allow for water to drain from the refuse pile. The dumping of the refuse

continued until coal production ceased. At

Figure 1: Topographic map showing the location of the study area

4

that point, a layer of lime slurry from the limestone quarry was used to cover the refuse. Its

purpose was to neutralize the acidity of the refuse minerals. This disposal of the sludge

continued until 1998, when the acid water began seeping out of the refuse pile (Figure 3).

Because of the nature of the material dumped at the site, the ground surface was originally

soft and waterlogged. To remediate this problem, the slurry and refuse would be excavated

and drained. Limestone from the nearby quarry would be mixed into this contamination to

give it additional support. Dams of limestone were also placed to contain the contamination

within its original depositional location. A layer of limestone was also placed on top of the

contamination, as well as soil and grasses. As the result of the acid seeps, the grass has since

died. Where the slope flattens out provides evidence of where the refuse was dug up and

redeposited (Figures 4 and 5).

Figure 2: The Refuse Pile, with an acid seeppond located to the right of the figure.

5

Figure 3: Acid seep pond

6

Figure 4: This slope shows where therefuse was excavated and redeposited.

Figure 5: Another view of the slope

7

Methods

The equipment used for this study includes two terrain conductivity meters, the EM-

31 and EM-34, the Geometrics (model 816) proton precession magnetometer, and the

American Geosciences Sting and Swift resistivity meter. The magnetometer (Figure 6)

consisted of a portable controller, two-meter aluminum pole, and a cylindrical sensor located

on top of the pole. The sensor itself consists a hydrogen-rich liquid core surrounded by a

wire. When a DC current is applied to the coil, a magnetic field is created, which is aligned

parallel to the coil axis. The magnetic field forces the spin of each hydrogen proton into

aligning with the induced field of the coil. When power is removed from the system, the

hydrogen protons begin to wobble, or precess about the earth’s magnetic field. This

precession induces a small alternating current to flow in the coil at the precession frequency

(Burger 1992). The frequency of the current alternations are measured and converted to a

total magnetic field intensity.

The magnetometer served two purposes in this survey. The first was used to locate

buried metallic waste, usually in the form of drums and barrels. For example, Gilkeson et al.

(1986) used the magnetometer to locate buried drums containing hazardous and chemical

waste. These drums were buried in trenches and magnetics was used to detect the boundaries

of the trenches. Some of the magnetic anomalies observed at the site were obviously

associated with buried metallic debris, because, in a few instances partially buried pipes were

actually founding the anomalous magnetics areas of the pile. The second purpose was to

detect any magnetic minerals in the subsurface. Local perturbations of the

8

Figure 6: The Geometrics (model 816)proton precession magnetometer

Figure 9: Sting and Swift System

Figure 7: EM-31 Figure 8: EM-34

9

earth’s magnetic field are related to local geologic conditions such as basaltic dykes, zones of

hydrothermal alterations, depositional changes in sediments, or other structural features

affecting the distribution of magnetic materials (Fahringer and Wilson 1999). In some cases,

broader, longer wavelength anomalies were observed to having uncertain origins. Fahringer

and Wilson (1999) have observed these anomalies over the Greer mine spoil. They speculate

that some of the magnetic anomalies may be associated with concentrations of weathered

pyrite or magnetic hematite.

The EM-31 (Figure 7), with its relatively short spacing of 12 feet and high operating

frequency of 6500 Hz provides a relatively shallow investigation that allows for the rapid

survey of areas for near surface terrain conductivity features. The instrument provides a

continuous reading of terrain conductivity variations associated with the refuse pile and

serves as a basis for planning the location of subsequent terrain conductivity and resistivity

soundings. The location of the high conductivity source area is inferred from high

conductivity areas observed over the refuse pile. Terrain conductivity and resistivity

soundings were then acquired across these high conductivity regions. Initial attempts to

model the terrain conductivity soundings failed to convincingly discriminate between the

layers of sludge and refuse. The modeling is discussed in detail in the following section.

The EM-31 consists of transmitter and receiver coils separated by a twelve-foot long

frame. The transmitter and receiver coils are covered and do not make contact with the

ground surface. The receiver coil records the quadrature component of the magnetic field,

which is due to currents induced in the subsurface by the transmitter field.

Numerous studies have demonstrated the successful use of the EM-31 to investigate

waste disposal sites. Stenson (1989) used the EM-31 to delineate landfill boundaries and

map changes in waste mass composition. Terrain conductivity collected by the EM-31 was

used to define landfill boundaries. Data collected over the site revealed areas of high terrain

10

conductivity gradient and indicate the boundary of the landfill. This interpretation was

consistent with the high conductivity of waste materials and the low conductivity of natural

soils.

The EM-34 (Figure 8) also measures apparent conductivity. It can be operated at

three different intercoil spacings in either a horizontal or vertical dipole mode measurements

at intercoil spacings of 10, 20, and 40 meters. The combination of measurements obtained

from the EM-31 and EM-34 can be modeled to estimate the depth of each anomaly.

Horizontal measurements are made with the coils situated vertically, while vertical

measurements are taken with the coils situated horizontally. The coils must be at the same

height when the readings are taken. The vertical readings provide greater penetration depth,

but tend to be less sensitive to the upper layers than the horizontal readings.

The Sting and Swift resistivity imaging system (Figure 9) is composed of two

components. The Swift consists of a line of switches connected by a cable, which

automatically measures potential difference between electrodes that will be used for two-

dimensional and three-dimensional surveys, as well as depth soundings. The electrodes in

the array are connected to a central control unit, the Sting. The Sting resistivity meter takes

measurements and stores the readings taken by the Swift. When used together, the system

uses 30 electrodes, which can create a 600 reading 2-D profile in two hours.

The Sting and Swift resistivity system has been used to locate groundwater sources,

locate fractures and cavities in bedrock, and detect pollution plumes (www.agiusa.com).

Resistivity surveys work best when there is a conductive overburden. In this study,

resistivity soundings were used in conjunction with the terrain conductivity soundings to

determine the depth of the refuse pile. EM-31 data collected over the refuse pile indicate that

considerable conductivity/resistivity variation is present at the site, so that a successful

survey can be conducted. Also, a uniform geological environment and a relatively shallow

11

depth are needed. While these parameters are not completely met, this experimental

application and comparison is made to determine the degree of constraint on subsurface

conductivity/resistivity variation that can be obtained from these two kinds of geophysical

surveys.

A number of studies have proven the reliability of the Sting and Swift Resistivity

System. Loke (1999) used the resistivity meter to locate former sludge deposits in southern

Sweden. The model derived by Loke reveals that the highly contaminated sludge deposits as

having low resistivity in the near surface. Both Russel and Higer (1987) and Merkel (1972)

have used resistivity methods to locate contamination plumes. Based on the concentration of

the pollutants, the source area could be established. Areas of pollution would have a low

resistivity, while natural waters would have a high resistivity.

Data Collected

Magnetic field intensities were measured early in the study over a large area of the

pile. Because the refuse pile contained a number of pipes and metallic strips poking out from

the subsurface, it was assumed that a magnetics survey would show the location of

anomalous regions. Also, the existence of metallic minerals within the refuse could be

detected by the magnetometer. The magnetics survey was conducted simultaneously with the

EM-31.

Terrain conductivity data was collected over the site early in the fall of 2000 and then

again in mid-winter of 2001. These surveys were taken of the top of the spoil to see if the

conductivity would be affected by weather changes. A spacing of 20 feet was used to collect

the data. Contour maps created from the EM-31 data revealed a number of distinct terrain

conductivity anomalies. Terrain conductivity anomalies identified by the EM-31 data was

used to place the location of EM-34 soundings. Soundings were run from the 60-foot line,

260-foot line, and 520-line. Eight soundings were taken, 20 feet apart along each profile.

12

When modeling, the readings were split between this project and Shogren’s research. For

this study, four soundings were modeled, 40 feet apart. Finally, after modeling the EM-34

data, the points for the Sting and Swift Resistivity Meter were established.

Two surveys using the EM-31 were conducted on the top of the spoil to determine

whether or not climate changes would affect conductivity. The initial investigation was

conducted in late fall, when the weather was still warm and the ground was dry, while the

second survey was taken in January, when there were several inches of snow laying on the

surface. Based on the data, it was concluded that climate did not affect the conductivity

readings.

After the preliminary EM-31 survey, a resistivity survey was conducted to determine

if there was an inverse relationship between resistivity and terrain conductivity values. Three

lines of data were taken; Line 60 was taken in December 2000, while Lines 260 and 520 were

taken in March 2001. The lines began at the top of the spoil and followed the slope towards

the acid ponds. The data recorded transmitter current, resulting voltage, and geometry.

Potentials are usually measured at several positions for every current source location. Results

at wider separations between the potential pair and the transmitter pair provide some

information about deeper structures. However, this is not a true cross section, because

separation distance is plotted, rather than depth. With this data, three pseudosections were

created, using apparent resistivity and geometry as the axes.

Software

A number of software was used to interpret the data collected during the survey of the

site. After the preliminary survey of the site, data acquired using the EM-31 were plotted

using Surfer. This software presents the data in a contour map form. Areas of high

conductivity are shown in yellow, while areas of low conductivity are in blue.

13

EMIX was used to interpret data collected by the EM-31 and EM-34. This modeling

software takes the data and creates a forward model using the full solution described by H.P.

Patra and K. Mallick in Geosounding Principles, 2 (1980) published by Elsevier

(www.interplex.com). To invert the model, an Inman-style ridge regression approach of

nonlinear least squares curve fitting was used. This method was used to obtain a model,

which would best fit the data in a least-squares sense. Starting model parameters can be

fixed, so they would not be adjusted in an inversion model. This was particularly useful

when integrating known geological or geophysical data of the given site.

In this study, both horizontal and vertical magnetic dipole measurements were used to

create a number of profiles. These profiles differed with respect to frequency, coil height,

and spacing. The data from the profiles was presented as apparent conductivity at low

induction number for each configuration. However, the forward calculation accounted for

the deviation from the low induction number assumption. Using McNeil’s equation to

convert the quadrature response of the earth to apparent conductivity, a model was created

(McNeil 1980).

The software used to model data taken from the Sting and Swift resistivity meter is

known as RES2DINV Version 3.3. The purpose of this program was to determine the

resistivity of rectangular blocks that will produce a pseudosection that agrees with actual

measurements. This program used the smoothness-constrained least squares method

inversion technique to produce a two-dimensional model of the subsurface from the apparent

resistivity data, which was calculated using a forward model (www.agiusa.com). It was

designed to invert data collected from, in this particular study, dipole-dipole arrays. The two-

dimensional model used a number of rectangular blocks that are loosely tied to the

distribution of datum points in the pseudosection. Also, the depth of the bottom row of

14

blocks was roughly equivalent to the investigation depth. The program chose the optimum

inversion parameters for any given data set; however, the user could modify the parameters.

The user could also enter changes in elevation, adjust the smoothing filter, and adjust the

number of iterations run for each data set. A pseudosection was then produced, showing the

measured resistivities, apparent resistivities, and the inverted model. However, the

pseudosection, which depicts the subsurface, is often distorted.

In this study, the resistivity survey was used to compare the accuracy of the terrain

conductivity survey. There was an inverse numerical relationship between terrain

conductivity and resistivity. Anomalous regions on the terrain conductivity map showed

high values, while the resistivity model showed low values. Three lines, located at the 60’

line, 260’ line, and 520’ line, were tested. A pseudosection of each line was then generated.

Results

EM-31 terrain conductivity variation mapped across the site revealed an extended

area of high conductivity (Figure 10). High conductivity areas associated with the site extend

through the central part of the refuse pile, forming a broad anomaly nearly 200 feet in width

that connects to the acid seeps eminating from the edge of the refuse pile.

After modeling the data collected from the EM-31, soundings using both the EM-31,

the EM-34 and Sting and Swift were then be implemented to determine the depth of the

anomalous region. However, one problem associated with the analysis

15

is to distinguish between sludge and lime slurry and the acid source areas. The programs

used to model the data also allows for a computer model of a multi-layered earth to be

constructed. These models may present distortion of the vertical distribution of the limestone

cap, slurry, refuse, and bedrock at the site. The sludge and lime slurry are basic, but are also

highly conductive. A small amount of contrast does appear in the preliminary modeling.

The inverted models reveal a four-layer structure that, based on the description of the site,

suggests that the slurry sludge may have a higher conductivity. The slurry is located in the

upper levels of the pile. The existence of equivalent solutions however, may suggest varying

scenarios. It may, in fact, be possible to model the combined effect of the slurry and refuse

by a single layer.

Based on the preliminary data collected from the EM-31, test data was collected at

line 200. Data was also collected from lines 60, 260, and 520 and at stations spaced 20 feet

apart. For this particular study, only data taken 40 feet apart was modeled. Three different

models were derived from the terrain conductivity data: a four-layer model with the refuse

Figure 10: EM-31 Contour Map. Both thex and y coordinates are in terms of feet.

Line (ft)

0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00 550.00 600.000.00

50.00

100.00

150.00

200.00

250.00

16

layer having a higher conductivity than the slurry layer, a four-layer model with the refuse

layer having a lower conductivity than the slurry layer, and a three layer model in which the

slurry and refuse layers are combined into one layer. After modeling the data using EMIX, a

common trend was noticed. In the four-layer models, the conductivity of the slurry pile

remained higher than the refuse pile, regardless of the initial parameters. The thickness of

each layer would decrease as the as the survey progressed down the slope. The three layer

models demonstrated the same decrease in thickness.

The models of the test line are depicted in Figure 11. Data collected began at Station

20 and ended at Station 220. The total thickness of the model is seven meters and the

conductivity ranges from 0-150 mmhos/m. The top of the refuse and slurry layers begins at

about 1 meter below the surface. Based on the model type, the maximum thickness of the

contamination ranges from 3-5 meters.

The following figures depict the modeling results from data acquired by the author. In Figure

12, Line 60 was modeled. Note that the contamination layers were confined to the top four

meters of the pile. The conductivity ranged from 0-200 mmhos/m and steadily increased as

the readings progressed down the slope. The top of the refuse and slurry layers begins at 0.5

meters below the surface. The maximum thickness of the layer varies from 2.5-3 meters

below the surface. Figure 13 demonstrate the model created using data acquired from Line

260. The maximum conductivity for each model was 100 mmhos/m. In the high refuse

model, the conductivity remained consistent when progressing down the slope. However, the

other two models show an increase in conductivity when progressing down the slope. The

maximum thickness of the contamination layer fluctuates from 4.5 to 6 meters. The results

collected at Line 520 were modeled in Figure 14. For this particular model, the depth to the

top of the contamination layer is significantly higher than the subsequent readings. However,

the layer thickness does decrease like the other models. The conductivity ranges from 0-100

17

mmhos/m. The conductivity decreases as the readings progress down the slope. The

maximum thickness of the profile ranges from 4-5 meters.

The resistivity models using RES2DINV demonstrate the resistivity results in

pseudo-section form. A pseudo-section measured the apparent resistivity of non-

homogeneous sections. These non-homogeneous sections were broken down into blocks of

datum points. The apparent resistivity was dependent on the resistivity contrast between

model layers, the depth of the interface between two layers, and electrode spacing.

RES2DINV used the least-squares equation to calculate apparent resistivity. The equation

used is as follows:

(JT J + λ CT C) p = JT g

where J is the Jacobian Matrix of Partial Derivatives, λ is a damping factor, g is the

discrepancy vector containing the log differences between the measured and calculated

apparent resistivity values, p is equal to the correction vector to model parameters, and C is

equal to the two-dimensional flatness filter. This flatness filter was used to constrain the

smoothness of model parameters to some constant value

18

Figure 11: Terrain Conductivity Profile for Line 200. The dotted linesshow the changing thickness of the coal refuse layer. (a) Three layermodel. (b) Four layer model with the refuse layer having higherresistivity. (c) Four layer model with the slurry layer having thehigher resistivity.

(a)

(b)

(c)

19

(a)

(b)

(c)

Figure 12: Terrain Conductivity Profile for Line 60. The dotted lines show thechanging thickness of the coal refuse layer. (a) Three layer model. (b) Fourlayer model with the refuse layer having higher resistivity. (c) Four layermodel with the slurry layer having the higher resistivity.

20

Figure 13: Terrain Conductivity Profile for Line 260. The dotted linesshow the changing thickness of the coal refuse layer. (a) Three layermodel. (b) Four layer model with the refuse layer having higherresistivity. (c) Four layer model with the slurry layer having thehigher resistivity.

21

Figure 14: Terrain Conductivity Profile for Line 520. The dottedlines show the changing thickness of the coal refuse layer. (a)Three layer model. (b) Four layer model with the refuse layerhaving higher resistivity. (c) Four layer model with the slurrylayer having the higher resistivity.

22

(Sasaki 1992). The correction vector, p, is achieved by calculating the model resistivity

values used in the calculation.

When processing a pseudosection, one must make a number of assumptions. First,

the log of resistivity qo of the starting homogeneous earth model is calculated by taking the

log of the average of measured resistivity values. This is done using the following equation:

40 = 1/m Σ fi

For this study, three resistivity lines were surveyed at the site, at line 60, 260, and

520, the same lines as the terrain conductivity model. However, the data taken using the

Sting and Swift Resistivity meter ran from the top of the spoil to the toe of the slope, the

discharge location of the acid water (Figure 16). Only the top of the spoil was surveyed

using terrain conductivity methods. Figure 15 shows the locations of data acquisition.

0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00 550.00 600.000.00

50.00

100.00

150.00

200.00

250.00

Figure 15: Terrain conductivity map of the site with thelocation of resistivity lines drawn in green.

Line (ft)

23

Line 60 data was acquired during the winter of 2000 (Figure 17). Thirty electrodes

were set up using the dipole-dipole method. Measurements were taken with a spacing of 2.5

m. After 3 iterations, using a damping factor of 0.05, an RMS error of 13.6 % was returned.

After calculating the true resistivity from the apparent resistivity, it was determined that the

total depth of the refuse pile at line 60 was 10 meters. The resistivity ranged from less than 6

ohm-m to approximately 6000 ohm-m. The line had fairly low anomalies until the 40-meter

station. Areas that had abnormally high resistivities were located towards the bottom of the

slope, where the layer of bedrock was closest to the surface. Another area of high resistivity

would be located 40 m from the beginning of the line.

The data for both Lines 260 and 520 were acquired during the spring of 2001.

During this time period, the results recorded by the Sting and Swift resistivity

Figure 16: Sting and Swift survey linetraveling towards the acid seep pond.

24

meter were much better than in December 2000. The first line analyzed would be Line 520

(Figure 18). Using an electrode spacing of 2.0 meters, a range of resistivities from less than

16 ohm-m to over 319 were recorded. After iterating the data set, an RMS error of 4.6 was

achieved. The total depth was 5.6 m and the line extended 56 m to the bottom of the refuse

pile. High resistivity values were restricted to the toe of the slope. Low resistivity areas

were located within the first 3 meters of the subsurface. Areas of moderate resistivity are

concentrated in pockets located a between the 8 m to 22 m stations. The second line

interpreted

Figure 17: The resistivity pseudosection and inverse model resistivitysection of Line 60

25

would be Line 260 (Figure 18). Using an electrode spacing of 3.0 meters, the resistivities

ranged from less than 11 ohm-m to over 80 ohm-m. The total depth of the profile was 9.8

meters. The total RMS error was 4.9%. Again, high resistivity values were restricted to the

toe of the refuse pile slope. A distinct pocket of low resistivity is located between 36 and 72

meters.

The magnetics survey (Figure 20) failed to detect any buried metallic materials. The

few minor anomalies turned out to be exposed pipes, which did not apply to this particular

study. The survey also failed to detect ferromagnesium minerals that could have been buried

within the refuse layer. Thus, it proved to be unsuccessful in this particular survey.

Figure 18: The resistivity pseudosection andinverse model resistivity section of Line 520.

26

Figure 19: The resistivity pseudosection and inverse model resistivitysection of Line 260

0.00 50.00 100.00 150.00 200.00 250.00 300.00 350.00 400.00 450.00 500.00 550.00 600.000.00

50.00

100.00

Figure 20: Magnetic survey of the site. Notice the lack ofreal anomalous regions.

27

Data Comparison

The models presented demonstrate that there was a distinct inverse relationship

between terrain conductivity and resistivity. High values in the terrain conductivity model

demonstrated low values in resistivity. This was especially prevalent at Line 260. When

studying the resistivity profile, it was noted that there was an area of low resistivty located

between 36 and 72 meters. This anomaly coincided with the area of high terrain conductivity

found on the surfer map of the initial EM-31 survey. The depth to the top of the refuse

determined by the resistivity survey also agreed with results demonstrated by the EM-34

profile of the top of the spoil. Based on the three-layer model of Line 260, the depth to

refuse was approximately 2.5 meters.

The model produced by data taken from Line 520 also agreed with the terrain

conductivity profile. In the resistivity profile, there was an anomalous region, colored in

green, beginning 26 meters from the start of the line and extending 20 meters down the slope.

This corresponds with the location of the anomaly located on the same line on the Surfer

contour map. The depth to the top of the refuse layer found on the resistivity profile was 2.5

meters, which was equal to the depth found in the terrain conductivity profile.

Line 60 had a greater percentage of error than the other two lines. The models

created using the resistivity and terrain conductivity data did not agree. In the terrain

conductivity map, a distinctive anomaly was located at the 100 ft. station and extended 75 ft.

towards the toe of the slope. When comparing this information with the resistivity model,

there was no inverse correlation between the two data types. At the 30-meter station, the

resistivity model did show an area of low resistivity; however, this area did not extend to the

60-meter station. Rather, the resistivity quickly increased to 4671 ohm-m. Also, the depth to

the top of the refuse layer was much greater in the resistivity profile, as opposed to the terrain

conductivity profile. A possible source of this error was the weather. This data was taken in

28

December and the frigid temperatures and snow could have affected the resistivity

measurements.

Conclusion

This study was successful in evaluating the geological characteristics of a coal refuse

pile in northern West Virginia. Based on this comparative study of various geophysical

methods, it was determined that the use of assorted geophysical equipment did provide an

accurate picture of the subsurface. The use of terrain conductivity and resistivity methods

provided a method to locating the source of the acid water seeping out of the pile. Data

acquired from these investigations were modeled and interpreted. Anomalous regions were

profiled and cross sections were made. After an initial EM-31 survey of the site, it was

determined that the anomalous regions were located at Line 60, 260, and 520. Three types of

profiles were constructed of the top of the spoil, using the EM-34 data in conjunction with

the EM-31 data. Of the three model types, the three layer model combining the slurry and

refuse layers provided the best results. When cross sections of the top of the spoil were

modeled, there was a noticeable decrease in thickness of the refuse layer as it traveled

towards the toe of the slope.

The resistivity and terrain conductivity models were analyzed and compared to each

other. For the most part, the models were consistent to each other. The inverse relationship

between terrain conductivity and resistivity was upheld, except in the case of Line 60. A

possible reason would be the weather. But, overall, this study proved to be successful.

29

Acknowledgements

I’d like to extend special thanks to Joe Dean of Greer Industries for allowing access to the

refuse area and for providing background on the development of the refuse area and

associated problems encountered with AMD seepage. This provided invaluable

perspective on the make-up of the refuse pile and helped guide the interpretations to

reasonable conclusion.

I’d also like to extend my gratitude to the NASA Space Grant Consortium for their Space

Grant Scholarship award. This, in addition to helping cover the costs of books and school

supplies also provided valuable support of travel and other expenses associated with the

Senior thesis effort.

Bibliography

Ackman, T., C. Lyons, R . Current. 1998. Post Injection Geophysical Investigation ofthe Winding Ridge Site. Proceedings; Nineteenth Annual Surface Mine DrainageTask Force Symposium, Morgantown, WV.

Advanced Geosciences. February 16, 2001. <www.agiusa.com>

Burger, H.R. 1992. Electrical Resistivity. Exploration Geophysics of the ShallowSubsurface. Prentice Hall: New Jersey, pp 241-316.

Ebraheem, A.M., M.W. Hamburger, E.R. Bayless, and N.C. Krothe. 1990. A Study ofAcid Mine Drainage Using Earth Resistivity Measurements. Groundwater 28(3): pp 361-368.

Fahringer, P.E. 1999. Geophysical Investigations of Near-Surface Mine Sites inNorthern West Virginia, Unpublished Master’s Thesis, West Virginia University,Department of Geology and Geography, 130 pp.

Gilkeson, R.H., P.C. Heigold, and D.E. Layman. 1986. Practical Application ofTheoretical Models to Magnetometer Surveys on Hazardous Waste DisposalSites-A Case History

Interplex Inc. April 20, 2001. <www.interplex.com>

30

Loke, M.H. and R.D. Barker. 1995. Least Squares Convolution of Apparent ResistivityPseudosections. Geophysics 60 (6): pp 1682-1690.

Loke, M.H. 1999. Rapid 2-D Resistivity & IP Inversion Using the Least SquaresMethod. Geometrics Inc: San Jose, 89 pp.

McNeill, J.D. 1980. Electromagnetic Terrain Conductivity Measurement at LowInduction Numbers. Technical Note TN-6, GEONICS Ltd. Ontario, Canada, 15pp.

Merkel, R.H. 1972. The Use of Resistivity Techniques to Delineate Acid Mine Drainagein Groundwater. Groundwater 10 (5): pp 38-42.

Russell, G.M. and A.L. Higer. 1988. Assessment of Groundwater Contamination NearLantana Landfill, Southeast Florida. Groundwater 26 (2): pp 156-164.

Schlinger, C.M. 1990. Magnetometer and Gradiometer Surveys for Detection ofUnderground Storage Tanks: Bulletin of Association of Engineering Geologists28 (1): pp 37-50.

Slain, D.D. and J.P. Greenhouse. 1982. Case Study of Geophysical ContaminantMapping at Several Waste Disposal Sites: In Proceedings of the Second NationalSymposium on Aquifer Restoration and Groundwater Monitoring, NationalWater Well Association, pp 299-315.

Stenson, R.W. 1989. Electromagnetic Data Acquisition Techniques for LandfillInvestigations: in Symposium on the Application of Geophysics to Engineeringand Environmental Problems, pp735-746.

Stollar, R.L. and P. Roux. 1975. Earth Resistivity Surveys—A Method for DefiningGround-Water Contamination. Groundwater 13 (2), pp 145-150.

Sweetman, M.B. and W.R. Bergstrom. 1993. Use of a Magnetometer/Gradiometer toLocate Abandoned Wells. Proceedings of the Seventh Annual Outdoor ActionConference and Exposition: Las Vegas, pp 315-324.