NON-INVASIVE FLOW PATH CHARACTERIZATION IN A MINING-IMPACTED
WETLAND
by
James C Bethune
A thesis submitted to the Faculty and the Board of Trustees of the Colorado
School of Mines in partial fulfillment of the requirements for the degree of Master of
Science (Hydrology).
Golden, Colorado
Date ______________________
Signed: _________________________
James C Bethune
Signed: _________________________
Dr. Kamini Singha
Thesis Advisor
Golden, Colorado
Date _______________________
Signed: _________________________
Dr. David Benson
Professor and Program Director
Hydrological Science and Engineering
Signed: _________________________
Dr. Paul Santi
Professor and Department Head
Department of Geology and Geological Engineering
ii"
"
ABSTRACT
Time-lapse electrical resistivity (ER) is used in this study to capture the annual pulse
of acid mine drainage (AMD) contamination, the so-called ‘first-flush’ driven by spring
snowmelt, through the subsurface of a wetland downgradient of the abandoned Pennsylvania Mine workings in Central Colorado. Data were collected from mid-July to late October
of 2013, with an additional dataset collected in June of 2014. ER provides a distributed
measurement of changes in subsurface electrical properties at high spatial resolution. Inversion of the data shows the development through time of multiple resistive anomalies in
the subsurface, which corroborating data suggest are driven by changes in total dissolved
solids (TDS) localized in preferential flow pathways. Because of the non-uniqueness inherent
to deterministic inversion, the exact geometry and magnitude of the anomalies is unknown,
but sensitivity analyses on synthetic data taken to mimic the site suggest that the anomalies would need to be at least several meters in diameter to be adequately resolved by the
inversions. Preferential flow path existence would have a critical impact on the extent of
attenuation mechanisms at the site, and their further characterization could be used to parameterize reactive transport models in developing quantitative predictions of remediation
strategies.
iii
TABLE OF CONTENTS
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
LIST OF FIGURES AND TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
viii
CHAPTER 1 GENERAL INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . 1
CHAPTER 2 NON-INVASIVE FLOW PATH CHARACTERIZATION IN A
MINING-IMPACTED WETLAND . . . . . . . . . . . . . . . . . . . . . . 6
2.1
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3
Field Site Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4
Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.5
Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.6
Evaluating Error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.7
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.7.1
Supporting data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.8
Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.9
Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.10 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
CHAPTER 3 FUTURE WORK . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1
Long-term monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2
Reactive Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
iv
3.3
Characterization of Pennsylvania Mine Leakage . . . . . . . . . . . . . . . . . 30
APPENDIX A - EXTENDED METHODS . . . . . . . . . . . . . . . . . . . . . . . . . 32
A.1 Resistivity Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
A.2 Finite Element Mesh Design and Gmsh . . . . . . . . . . . . . . . . . . . . . . 33
APPENDIX B - MISCELLANEOUS DATA . . . . . . . . . . . . . . . . . . . . . . . . 35
REFERENCES CITED . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
v
LIST OF FIGURES AND TABLES
Figure 1.1
Geological map of the Pennsylvania Mine area, including the
hypothesized Montezuma shear zone. Modified from Bird, 2003. . . . . . . . 3
Figure 2.1
Map of study region with Peru Creek, resistivity array, and borehole
sample locations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Figure 2.2
Resistivity inversion of data collected on July 12th, 2013. Electrodes
(E1-E72), model fitting parameter results, borehole logs, and the general
character of vegetation are shown. . . . . . . . . . . . . . . . . . . . . . . 18
Figure 2.3
Resolution of inversion of data collected on July 12th, 2013. Note,
because of smoothing issues, only data for 1 m x 1 m pixels are shown. . . 18
Figure 2.4
Time-lapse percent changes in resistivity, relative to background
inversion of 12 July 2013 data. . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 2.5
Time-lapse absolute change in resistivity, relative to background
inversion of 12 July 2013 data. . . . . . . . . . . . . . . . . . . . . . . . . 19
Figure 2.6
Flow diagram of the sensitivity modeling process. ’Summarized region’
denotes the area over which the total resistivity anomaly is calculated. . . 23
Figure 2.7
Sensitivity modeling results. . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Figure B.1
All wetland inversions with fitting results. All changes are relative to
the background inversion of data from 12 July 2013. . . . . . . . . . . . . 36
Figure B.2
Resolutions of all inversions through the wetland. . . . . . . . . . . . . . . 37
Figure B.3
Temperature (A) and conductivity (B) measurements taken from
boreholes in the wetland area. . . . . . . . . . . . . . . . . . . . . . . . . . 38
Figure B.4
Average temperatures measured in the boreholes at shallow <1.5 m
bgs., and deep depths. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Figure B.5
Sensitivity modeling results. . . . . . . . . . . . . . . . . . . . . . . . . . . 39
vi
Figure B.6
Additional measurement locations, including pressure transducers and
stilling well. HOBO W1 denotes the location of the transducer installed
in Peru Creek. HOBO A1 denotes the location of the air pressure
transducer from October to November. HOBO A2 denotes the air
pressure transducer left at the site over winter. . . . . . . . . . . . . . . . 40
Figure B.7
Water temperature (A) and pressure (B) measurements of HOBO W1.
Pressure has been corrected for air pressure and converted to cm water. . 41
Figure B.8
Air temperature (A) and pressure (B) measurements of HOBO A1. . . . . 42
Table B.1
Discharge measurements from Peru Creek. . . . . . . . . . . . . . . . . . . 36
vii
ACKNOWLEDGMENTS
It took the support of many people and institutions to make this project possible. My
advisor, Dr. Kamini Singha, and committee members Dr. Rob Runkel and Dr. Alexis
Navarre-Sitchler were all instrumental in their contributions. Kamini’s constant source of
knowledge and direction throughout the project was deeply appreciated, as was Rob’s valuable assistance at the field site and with the manuscript. The inspiration to work in an AMD
impacted site arose from a chance conversation with Dr. Katie Walton-Day, following a presentation she gave at the Colorado School of Mines. Conversations with Je↵ Graves, Mark
Rudolph, and Dr. Stan Church would all later provide valuable insights for the project.
Many volunteers tirelessly supported this project in the field, often by trudging through
the mucky wetland, carrying heavy batteries, and nearly always with inclement weather
quickly approaching. In particular, fellow HSE students Ben Bader, Skuyler Herzog, Emmanual Padilla, and Mike Sanders, were all kind enough to dedicate multiple days to the
project.
My time at CSM was supported by a teaching assistantship provided by the Geology
Department. The experience was beyond rewarding, and has inspired me to continue to
incorporate teaching into my life in some capacity. It also proved to be an excellent field
work volunteer recruiting position.
Finally, Jackie Randell provided key assistance throughout the project, in the field, with
the text, and in the lab. Without Jackie, this project would be in a very di↵erent place, and
I don’t think I can thank her enough.
viii
CHAPTER 1
GENERAL INTRODUCTION
Weathering of sulfide deposits throughout the Montezuma Mining District in Central
Colorado presents a major environmental water quality issue for the Snake River and its
tributaries. Sulfide oxidation produces acid and releases high concentrations of metals, resulting in ecologically toxic discharge known as acid rock drainage (ARD). Because of its
abundance, pyrite (FeS2 ) is the primary mineral responsible for ARD production. There are
multiple pyrite oxidation reaction pathways, but in the acidic conditions observed at mine
sites, pyrite is oxidized by ferric iron (Fe3+ ) in the following microbially mediated reaction
(Hallberg, 2010):
F eS2 + 14F e3+ + 8H2 O ! 15F e2+ + 2HSO4 + 14H +
(1.1)
Mining operations greatly accelerate the sulfide weathering process through augmentation
of available reactive mineral surface area (Alpers et al., 2007). To di↵erentiate it from
naturally occurring ARD, discharge from mined lands is called acid mine drainage (AMD).
Current mining practices seek to minimize impact on water resources, but the Montezuma
District contains many historic and abandoned mines that pre-date recent impact concerns
and regulations. Equation 1.1 typically proceeds until all available pyrite is consumed, as as
a result the e↵ects of AMD can persist for decades or even centuries after mining operations
have ceased (Younger, 1997). Because of its persistent and pervasive nature, AMD has been
described as the greatest water quality issue facing the western US today (Da Rosa et al.,
1997).
Analyses of water and sediment samples taken from throughout the Snake River and its
tributaries found that concentrations of zinc consistently exceed acute and chronic toxicity
thresholds for trout (Fey et al., 2001). Indeed, the Snake River currently needs to be restocked with trout each spring because they cannot survive the winter in the mining-impaired
1
habitat (Fey et al., 2001). There is some debate as to the existence of a shear zone, locally
known as the Montezuma shear zone, cutting through the site (Figure 1.1) that may be regionally enhancing the rate of pyrite weathering (Wood et al., 2005). Some have argued that
a linear zone of ductile and brittle features across the front range represent a major strain
feature of the crust. Others have documented features in the area that would be inconsistent
with a large crustal strain feature, and instead suggest that deformation associated with the
area is related to Laramide deformation (Caine et al., 2010). In any event, the bedrock of
the region contains a large density of fractures that serve as fundamental hydrogeological
conduits (Caine & Tomusiak, 2003).
The Snake River becomes significantly more impacted with metals after its confluence
with Peru Creek, its largest tributary. Although some of the dissolved metals loads are the
result of naturally occurring ARD (Verplanck et al., 2009), the U.S. Geological Survey came
to the following conclusion after extensive sediment and water chemistry sampling (Fey et al.,
2001):
Primary targets for remediation should target identified mining sources draining
into those reaches of Peru Creek. Other sources of metals in the watershed are
minor by comparison.
In particular, the Pennsylvania Mine, which is near Peru Creek about 4 miles upstream of
its confluence with the Snake River, was identified as a major contributor of metals to the
watershed (Fey et al., 2001). The Pennsylvania Mine was historically the largest in the area,
yielding a total of over 105 kg of gold, 26,000 kg of silver, 2,800 kg of lead, 27,000 kg of
copper, 336,000 kg of zinc (Bird, 2003; Lovering, 1935). After the mine was closed in 1953
(Bird, 2003), it changed hands several times, eventually falling into management by the US
Forest Service as a part of a broader e↵ort to restore the watershed (County, 2005).
Initial mass balance calculations showed that the major surface inflows of the upper
reaches of Peru Creek could not account for the extent of local metal loading (Fey et al.,
2001), a fact which was attributed to additional loading from the Pennsylvania Mine, but
2
pC
Yg
Qal
Per
eek
u Cr
Pennsylvania
Mine
Penn. Mill
na
Cin
Qal
n
mo
lch
Gu
Yg
Yg
EXPLANATION
Yg
Porphyritic quartz
monzonite of the
Montezuma Stock
Precambrian
DIVIDE X
pC
ENT
AL
pCdeposits
Surficial
Montezuma
Shear zone
TIN
Qal
CO
N
Delaware
Mine
Figure 1.1: Geological map of the Pennsylvania Mine area, including the hypothesized
Montezuma shear zone. Modified from Bird, 2003.
which was not investigated further at that time. Additional synoptic sampling performed
along the Pennsylvania Mine reach of Peru Creek in September 2009 identified a di↵use source
of contamination emanating from a wetland between the mine and Peru Creek (Runkel et al.,
2013). There are three potential contaminant transport pathways through the wetland that
could be contributing to the metals loading in Peru Creek. First, the wetland could be
generating contamination in several large deposits of mining waste rock. Waste rock often
contains disseminated pyrite, and has been documented to discharge severely contaminated
water at other sites (Smith, 1995). Second, the wetland could have a direct hydrogeological
connection with the mine, as indicated by the recovery of tracers injected directly in multiple
wells in the wetland (Mark Rudolph, Colorado Geological Survey, personal communication
3
of unpublished data). The mine workings are extensive and remain poorly mapped due to
hazardous structural collapses (Lovering, 1935; Rudolph, 2010). As a result, flow through
the mine workings remains poorly understood. Third, chemical analyses of groundwater
downgradient of the mine outflow suggest that the mine outflow is infiltrating into groundwater, opening the possibility that water from the mine outflow is also reaching the wetland
area (Rudolph, 2010).
It is unclear how flow through the wetland is a↵ecting downstream transport of AMD
contaminants. Wetlands have the capacity to precipitate metal sulfides (Sheoran & Sheoran,
2006) and adsorb positive metal ions to negatively charged clay particles or organic material
(Johnson & Hallberg, 2005). The longer flow paths and slower velocities of subsurface flow
allow for greater contact time with biogeochemically active attenuating features, therefore
flow through the subsurface can be particularly important to promoting removal processes
(Gandy et al., 2007; Mulholland & DeAngelis, 2000). However, the wetland was previously
found to be ine↵ective in remediating redirected mine discharge (Emerick et al., 1988).
The presence of preferential flow paths through the wetland could limit the extent of
attenuation mechanisms, while complicating interpretations of downstream breakthrough
curves. Preferential flow paths result in earlier breakthrough time, lower residence time,
and more pronounced tailing (Brusseau, 1994). The existence of mining waste piles in the
wetland increases the likelihood that preferential flow exists in the wetland because deposition
of mining waste often results in vertical grading, with larger grains tumbling down and
finer grains settling over the surface (Smith, 1995). Slug tests on boreholes in the wetland
reveal substantial variability in hydraulic conductivity, which also suggests preferential flow
(Emerick et al., 1988).
Ongoing remediation e↵orts begun in the summer of 2012 include entering the mine to
identify potential sources of contamination, evaluating the potential for a bulkhead installation to stymie the outflow of water from the mine, and moving the mine tailings farther
from Peru Creek. A recent characterization of water flowing through the mine found that
4
a substantial volume of relatively clean water drains the crosscut of the lowest mine level
upstream of the main mine workings, while a smaller volume of highly impacted water drains
the inner mine works (Personal Communication Mark Rudolph, 2013). As of the time of
this writing, the plan is to install two separate bulkheads in the lower cross-cut. However,
success of the bulkhead installation is contingent on its ability to plug the mine, saturate the
mine workings, and limit further sulfide oxidation. If the wetland is in direct hydrogeological
connection with the mine workings, it would indicate that the mine workings may be leaking
internally, and may not hold the water required to maintain fully saturated conditions.
The goal of this research is to contribute to the understanding of subsurface flow within
the wetland, and to explore those results in light of recent remediation activities and with
regard to AMD transport processes more generally. The results from this research have
been compiled into the following manuscript for submission to the Journal of Contaminant
Hydrology. After the paper, the reader will find a closing statement in which future directions
for this research are explored, followed by a number of appendices containing extended
documentation of methods and data, and discussion of several topics in the main body of
the paper.
5
CHAPTER 2
NON-INVASIVE FLOW PATH CHARACTERIZATION IN A MINING-IMPACTED
WETLAND
A paper to be submitted to the Journal of Contaminant Hydrology
James Bethune1 , Jackie Randell2 , Robert L. Runkel3 , Kamini Singha4
2.1
Abstract
Time-lapse electrical resistivity (ER) is used in this study to capture the annual pulse
of acid mine drainage (AMD) contamination, the so-called ‘first-flush’ driven by spring
snowmelt, through the subsurface of a wetland downgradient of the abandoned Pennsylvania Mine workings in Central Colorado. Data were collected from mid-July to late October
of 2013, with an additional dataset collected in June of 2014. ER provides a distributed
measurement of changes in subsurface electrical properties at high spatial resolution. Inversion of the data shows the development through time of multiple resistive anomalies in
the subsurface, which corroborating data suggest are driven by changes in total dissolved
solids (TDS) localized in preferential flow pathways. Because of the non-uniqueness inherent
to deterministic inversion, the exact geometry and magnitude of the anomalies is unknown,
but sensitivity analyses on synthetic data taken to mimic the site suggest that the anomalies would need to be at least several meters in diameter to be adequately resolved by the
inversions. Preferential flow path existence would have a critical impact on the extent of
attenuation mechanisms at the site, and their further characterization could be used to parameterize reactive transport models in developing quantitative predictions of remediation
strategies.
1
Primary Author and Researcher, Graduate Student, Colorado School of Mines
Field Technician, Colorado School of Mines
3
Scientist, U.S. Geological Survey
4
Associate Professor, Colorado School of Mines
2
6
2.2
Introduction
Weathering of sulfide deposits presents a major environmental water quality issue by
creating acidic conditions and mobilizing heavy metals (reviews include Da Rosa et al.,
1997; Nordstrom, 2011a). Although acid rock drainage naturally forms as a byproduct of
sulfide oxidation, mining operations can increase the weathering rate by up to three orders of
magnitude through the augmentation of reactive mineral surface area (Alpers et al., 2007).
In the western U.S., acid mine drainage (AMD) impacts between 8,000 and 16,000 km of
streams on Forest Service land alone (US Forest Service, 1993). The e↵ects of AMD can
persist for decades or even centuries after mining operations have ceased through continued
oxidation and dissolution of acid-releasing minerals (Younger, 1997).
E↵ective remediation of AMD requires detailed knowledge of contaminant transport
through the subsurface, where longer retention times may allow for extended contact with
attenuating agents (Zhu et al., 2002). Heterogeneity and preferential flow path development in AMD settings has been shown to decrease the efficiency of contaminant attenuation
(Malmström et al., 2008), likely because preferential flow paths reduce the residence time of
solutes in the subsurface and contact with attenuating agents (Brusseau, 1994). Deposition
of mining waste piles typically results in graded bedding, through which most discharge is
concentrated into a small of the total rock volume (Morin & Hutt, 1994; Smith, 1995). Unfortunately, the subsurface is rarely mapped to a sufficient extent to identify and characterize
flow paths, especially at historical mine sites, where e↵orts generally contend with a lack of
site data and highly disturbed aquifer material (e.g., Nordstrom, 2011b; Oram et al., 2010).
Many AMD remediation projects expend considerable e↵ort constraining flow and transport
parameters through tracer injections (Benner et al., 2002), hydrograph separation (Smith,
1995), flow balance calculations (Gélinas et al., 1994), and aquifer permeability tests, or are
otherwise forced to make simplifying assumptions regarding subsurface homogeneity.
The high conductivity of AMD has been demonstrated to be a useful tracer for mapping
mining contamination (Gray, 1996), and makes it an excellent target for electrical geophysical
7
methods (Merkel, 1972). Electrical resistivity (ER) is a geophysical technique that measures
the electrical conductance of the subsurface by both establishing and measuring a potential
gradient between one or more pairs of electrode (Binley & Kemna, 2005). The procedure
is repeated for many di↵erent electrode locations and current configurations to develop a
spatially distributed dataset of subsurface conductance (See Loke et al., 2013, for a recent
review). ER has been previously used to image both the extent and concentrations of
subsurface mining contamination (e.g., Oldenburg & Li, 1994; Rucker et al., 2009). However,
these studies were limited in that they assumed a consistent relationship between resistivity
and TDS (Day-Lewis et al., 2005), which, due to heterogeneities in the aquifer and in the
resolution of the inversion, can be difficult to define (Singha & Gorelick, 2006).
Time-lapse ER circumvents the reliance on petrophysical relationships by attributing
changes in measured resistivity to changes in pore fluid conductivity. Many time-lapse ER
studies inject a conductive tracer to facilitate flow path imaging (e.g., Ward et al., 2010);
however the ‘first-flush’ behavior demonstrated by many mine sites creates an ideal natural
electrical signal to capture and define contaminant transport using time-lapse ER. This seasonal pulse of AMD can be used in place of a tracer, eliminating assumptions about contamination source location that are implicit in injected tracer studies. The largest contaminant
loads are typically, though not always, coincident with large storms following prolonged dry
conditions (Miller & Miller, 2007; Nordstrom, 2009).
The goals of this paper are twofold: first, to demonstrate the use of time-lapse ER to
map AMD flow paths with application to characterizing contaminant transport. Second, to
demonstrate the sensitivity of ER to di↵erent flow path geometries. Inverting ER measurements using a standard L2-norm necessarily involves smoothing (Day-Lewis et al., 2005),
which may lead to some smaller features being lost. An understanding of the capabilities of
ER to resolve small features is crucial for actionable analysis in AMD settings. ER has been
previously used to characterize the extent of AMD contamination, but the novel approach
outlined in this paper is the first time that time-lapse ER has been used to image natural
8
conductivity changes in an AMD setting.
2.3
Field Site Description
This research was conducted in a wetland between the historic Pennsylvania Mine and
Peru Creek, a headwater stream to the Colorado River in Central Colorado (Figure 2.1).
The Peru Creek basin is bracketed on the north and east by the Continental Divide, and
drains west into the Snake River. Because 80% of precipitation falls as snow, the hydrograph
is dominated by spring snowmelt pulse (Crouch et al., 2013).
The local geology includes part of a heavily mined Oligocene quartz monzonite porphyry
of the Montezuma district (Figure 1.1). The Montezuma stock intruded the precambrian
schist and gneiss, causing extensive fracturing and faulting and widely disseminating pyrite
(Fey et al., 2001). The mineral assemblage includes abundant sulfides, in particular pyrite
(FeS2 ), sphalerite ([Zn,Fe]S), and galena (PbS) (Lovering, 1935). The Snake River contains
ecologically toxic concentrations of zinc, cadmium, and copper as a result of natural and
anthropogenically-induced pyrite weathering (Wood et al., 2005). Secondary porosity associated with the Colorado Mineral Belt has been suggested to enhance the rate of pyrite
weathering in both mining impacted and unimpacted areas, though the precise nature and
cause of that porosity has been debated (Caine & Tomusiak, 2003; Wood et al., 2005).
A study of the nearby Handcart Gulch, an unmined drainage near the edge of the Montezuma district, found deposits of ferricrete (iron oxide) coating the streambed (Verplanck
et al., 2009), indicating that background metals concentrations are high even in unmined
drainages in the area, likely due to natural weathering of sulfide minerals.
Regionally, sulfate concentrations, which are a common proxy of mining contamination,
are highest in areas with inactive mines and with extensive hydrothermal alteration (Fey
et al., 2001). Many abandoned mines are scattered throughout the region, but water and
sediment chemistry analyses of the Snake River reveal that one of the largest contributor of
metals is the Pennsylvania Mine reach of Peru Creek (Todd et al., 2005).
9
The Pennsylvania Mine was one of the largest mines in the region during its operation
from 1885 to 1953 (Bird, 2003). The extensive underground mine workings were historically
accessed via 6 adits, two of which remain at least partially open today (Lovering, 1935;
Wood et al., 2005). A surface flow exits the lower adit and discharges into Peru Creek
approximately 100 m upgradient of the wetland (Figure 2.1).
^
>
>
k
Electrode #72
GW5
Pe
ru
ee
Cr
GW3
E
>
Electrode #1
>
>
>
MW02-04
>
GWC1
Electrode
Sample well
o
nam
Cin
Resistivity survey
Pennsylvania Mine
nG
Peru Creek
ulch
Surface inflow
Elevation contour (10 m)
Wetland area
0
0.05
0.1
0.2 Kilometers
±
Figure 2.1: Map of study region with Peru Creek, resistivity array, and borehole sample
locations.
Because of its high impact on local water sources, the surface water chemistry of Peru
Creek has been studied extensively (Fey et al., 2001; McKnight & Bencala, 1990; Runkel
et al., 2013; Sullivan & Drever, 2001). There is a large seasonality to both surface flow and
contaminant concentration and loading in Peru Creek (Sullivan & Drever, 2001). The peak
flow occurs in late May to early June and is typically 5-10 times as great as low flow during
early spring (Sullivan & Drever, 2001; Todd et al., 2005). Metals concentrations in the mine
10
outflow are highest during the high spring flows, consistent with the first-flush behavior
identified at other mine sites (Sullivan & Drever, 2001). In September of 2009, synoptic
sampling along the Pennsylvania Mine reach of Peru Creek identified a di↵use contaminant
source emanating from the wetland area (Runkel et al., 2013). Peru Creek discharge was
found to increase from 55 L/s to 100 L/s over the wetland reach (Runkel et al., 2013).
Sampled inflows show higher metal concentrations than Peru Creek, indicating that water
discharging from the wetland is mining-impacted (Runkel et al., 2013). The constant pH and
increasing sulfate concentrations over the wetland reach also indicate that the unsampled
contributing water is mining impacted (Runkel et al., 2013), since unimpacted water would
be expected to dilute the acidity and sulfate concentration. Concentrations of specific metals
were variable over the wetland reach: in-stream concentrations of Cu, Zn, and Cd increased,
while concentrations of Al, Fe, Pb decreased (Runkel et al., 2013).
The mine outflow carries higher metals loads to Peru Creek (Runkel et al., 2013), but
groundwater discharging to Peru Creek from the wetland is still fundamental in addressing
the Pennsylvania Mine impact as a whole. The wetland has large deposits of potentially
AMD generating waste rock. Using an average precipitation total of 91 cm/year, and estimating the total waste rock area as 4,600 m2 , the Colorado Geological Society estimates
that an annual average of 0.5 m3 /hr of flow could be passing through waste rock and into
groundwater each year (Wood et al., 2005). Water budget calculations from Cinnamon Gulch
(Figure 2.1) show that the vast majority (>95%) of discharge to Peru Creek is from groundwater inflow (Wood et al., 2005). Furthermore, a tracer injected directly into the mine was
recovered in boreholes in the wetland about 100 days after injection, indicating there is a
hydrological connection with the mine (Mark Rudolph, Colorado Geological Survey, personal
communication of unpublished data). Chemical analyses of groundwater sampled downgradient of the mine outflow suggest that the mine outflow is infiltrating into groundwater,
implying that the mine outflow is infiltrating into the wetland area (Rudolph, 2010). TDS
levels are highest in the deeper wells, indicating that the wetland connection with the mine
11
workings or other tailings piles is through the deeper fractured granite bedrock (Rudolph,
2010). Mixing and end-member analyses of metal concentrations indicate that the wetland
could be receiving drainage from the Pennsylvania Mine as well as neighboring mines in
Cinnamon Gulch (Runkel et al., 2013).
Flow through the wetland was previously studied in evaluation of the site’s capacity to
naturally attenuate redirected mine e✏uent (Emerick et al., 1988). Boreholes drilled in the
wetland show three stratigraphic units: from 0-6 meters depth there are interbedded layers
of clay, silt, and peat; from 6-12 meters depth there is a sand and gravel layer; below 12
meters depth there is a layer of fractured granite bedrock (Rudolph, 2010). Interpolation of
the borehole logs suggests that the upper layer of clay and silt in the wetland is bowl-shaped,
roughly 5 m thick in the middle and tapering out toward the edges (Emerick et al., 1988).
The hydraulic conductivity of this uppermost layer was found to be highly variable, with
recovery rates from bailing tests of the boreholes spanning orders of magnitude (Emerick
et al., 1988). The highly variably recovery rates were attributed to anomalous 2-3 inch thick
layers of fine grained clayey sand encountered in multiple boreholes (Emerick et al., 1988).
The upper 10 cm of the wetland soil is characterized as 41% organic, with the texture of
loam or clayey loam (Emerick et al., 1988). The upper 3-4 cm of the soil is oxidized, with
meter-scale surface patches of metal oxide deposits. Vegetation is dominated by water sedge
and patches of bog birch (Emerick et al., 1988). The ground surface has localized 10-15 m
patches of standing water up to 5 cm deep. Tailings were dumped haphazardly throughout
the eastern half of the wetland, but the extent of these deposits is poorly mapped (Rudolph
& Mackenzie, 2009).
2.4
Material and Methods
An array of 72 electrodes with 5 meter spacing was installed east to west, through the
wetland area and across the mine outlet, running roughly parallel to the creek (Figure 2.1).
Data were collected on a 645 dipole-dipole quadripole sequence, which was collected in 3
replicates each field session to better estimate measurement error. Each stored quadripole
12
measurement represents the average of a stack of 3-6 separate measurements, resulting in
a total of 9-18 measurements collected per quadripole per field session. The dipole-dipole
geometry allows for up to 10 measurements to be collected simultaneously with a 10-channel
Syscal Pro resistivity meter (IRIS Instruments, Orleans, France), resulting in a total collection time of approximately 15 minutes per sequence. Initial resistivity data were collected
on July 12th, 2013. Subsequent time-steps were collected at approximately 2 week intervals,
until the road was inaccessible in late October, 2013. An additional dataset was collected in
June of 2014.
Electrodes were constructed from 75 cm X 1.27 cm schedule 40 PVC, wrapped with 8
cm of conductive foil tape approximately 10 cm from one end. Each electrode was installed
to 20 cm below ground surface (bgs) and connected to the resistivity meter using 18 gauge
tinned copper wire and prebuilt cables. Electrodes were left in place throughout the field
campaign, including over the winter season. Contact resistance was checked at each electrode
before each survey, and was typically less than 1 kohm-m in the wetland, indicating excellent
electrical connection with the ground. Elevations of each electrode were recorded using a
Trimble XT6000 GPS unit, and post-corrected with GPS Pathfinder Office 2, resulting in
sub-decimeter accuracy in the horizontal direction, and 10-20 cm accuracy in the vertical
direction.
Ancillary data that were collected to facilitate interpretation of the ER measurements
include: pore fluid conductivity, temperature, and water levels in 6 pre-existing wells (identified as MW02, MW03, MW04, CGW1, GW3, and GW5 on Figure 2.1) using a Solinst water
level probe. In each borehole, temperature and conductivity data were collected at water
level, as well as the top, middle, and bottom of the screened interval. Borehole measurements were made synchronous to, or immediately following, ER measurements. The water
level probe was rinsed with water from bailers installed in each borehole prior to collecting
measurements. MW02 was screened into the deeper granite bedrock (from 14-16.8 m bgs),
MW03 was screened into the sandy gravel layer (5.5-8.5 m bgs), GW5 (1.5-3 m bgs), MW04
13
(1.5-3 m bgs), and CGW1 (0.3-1.4 m bgs) were screened into the wetland clay and peat, and
GW3 is screened into Peru Creek alluvium (1.5-4.6 m bgs).
Peru Creek flow was gaged near the center of the resistivity array using velocimeters on
Oct 11th, 2013; thereafter, flow was monitored continuously with two pressure transducers
until Nov 4th, 2013. Pressure transducers were left in four monitoring wells (MW02, MW03,
MW03, CGW1) over winter and spring to monitor water level, temperature, and pore fluid
conductivity.
2.5
Inversion
ER measurements were inverted using the R2 research code (v2.7, Generalized 2-D In-
version of Resistivity Data, (described in Binley & Kemna, 2005)). Inversions are inherently
non-unique and ill-posed because model unknowns typically greatly outnumber measurements (LaBrecque et al., 1996), and hence require additional model constraints. To satisfy
that requirement, R2 uses regularized optimization, which seeks to minimize both data misfit
and model roughness (Tikhonov & Arsenin, 1977). The objective function,
(), takes the
form:
(m) = (Wd (m)[d
f (m)])2 + ↵(Wm [m
where
m
d
Wd
f (m)
Wm
↵
mref
model vector
measured resistance data
data weighting matrix
forward solution operating on the model
model weighting matrix that typically evaluates model roughness
weight that controls the relative importance
of the two terms on the right side of the equation
starting model guess
14
mref ])2
(2.1)
Conceptually, the term on the far right of Equation 2.1 measures model roughness, while
the next term to the left measures model misfit. Inversions require an initial guess or model
starting value, mref . In time-lapse ER, mref refers to the inversion of the initial dataset.
A finite element mesh was designed with 1 m x 1 m elements to 20 m depth, below
which element size gradually increased, resulting in a total of 10,152 elements. Element
nodes needed to be placed at each electrode location in the model, resulting in slightly
non-uniform element sizes.
Because of the regularization term in Equation 2.1, the resulting tomograms represent
smoothed depictions of the subsurface bulk electrical resistivity. However, the degree of
smoothing varies over the model space, resulting in an inversion having less resolving power in
some regions than others. Resolution matrices inform on the degree of smoothing associated
with a given pixel (Day-Lewis et al., 2005). The resolution matrix, R, is quantified as:
m̂ ⇡ Rmtrue
(2.2)
in which m̂ is the model estimate, and mtrue is the true resistivity value of the measured
domain. The diagonal of R quantifies the degree to which the value of a given pixel in the
inversion is informed by the data corresponding to that pixel, as opposed to the smoothing
influence of the regularization term. To limit interpretation of results dominated by smoothing, resistivity results corresponding to values less than Log[-2.5] in the diagonal of their
resolution matrix were clipped from the analysis.
2.6
Evaluating Error
Appropriately defining error is vital to achieving proper inversion fit. Error estimates
that are too low result in a noisy model with inversion artifacts, while error estimates that
are too high result in an overly smooth model with low resolving power (LaBrecque et al.,
1996). The data weighting term in Equation 2.1, Wd , is typically of the form diag(1/✏1 , ...,
1/✏n ), in which ✏i is the percent standard deviation associated with a stack of quadripole
resistance measurements.
15
Measurement error was reported as the percent standard deviation of each stack; however,
inspection of the data revealed that the measured resistances between replicate stacks were
much more variable than the individual stack errors. Accordingly, the total measurement
error for each quadripole was calculated as the global percent standard deviation from the
three replicate stacks. Final measurement error was then either the total measurement error,
or the reported precision of the Syscal Pro unit (0.2%), whichever was greater. R2 also allows
for measurement error to be calculated based on a generalized error model, but this method
was deemed less appropriate after inspection of the data measurement errors revealed that
error values were highly variable between quadripoles, particularly when comparing the errors
of the flatter wetland area, where we typically had excellent contact, and errors of the steeper
upland area, where contact resistance was generally higher and where electrodes replaced
before each field session. Measurement errors generally decreased over the field campaign,
likely because soil settling around the electrodes promoted better electrical contact with the
ground.
Some quadripoles covering the far eastern part of the survey measured unreasonably large
changes in resistance between time-steps, possibly due to electrode placement issues or local
construction activities. To make sure that these suspect quadripoles did not negatively a↵ect
the analysis, quadripoles measuring resistance values with a coefficient of variation greater
than 1 over the survey duration were filtered out of the analysis.
Model error was assessed by comparing the apparent resistivities resulting from a forward
solution on an homogeneous model with a flat surface boundary (Binley & Kemna, 2005).
The average model error was 0.5%. Total error for each quadripole was taken to be the sum
of measurement error and model error.
16
2.7
Results
The resistivity data (Figure 2.2) conform to lithology interpretation made by boreholes
(Emerick et al., 1988; Rudolph, 2010). There is a bowl-shaped, low resistivity (<50 ohm-m)
unit in the wetland with a maximum thickness of about 5 m that tapers out toward the
edges of the wetland area. The resistivity of this unit is typical of clay (<100 ohm-m), which
corresponds well with the interbedded clays, silts, and peat logged in boreholes MW02-04
and CGW1 (Telford & Sheri↵, 1990). There appears to be a more resistive unit extending
from electrode 48 at the surface down and to the west that corresponds to the layer of sand
and gravel (80-120 ohm-m) seen in MW02 and GW05 (Telford & Sheri↵, 1990). Below the
sand layer, resistivity increases to about 700 ohm-m, typical of the granite bedrock observed
in the bottom of MW02, though this depth is near the resolution limit of this study. The
bedrock appears to outcrop at electrode 48. There is a small surface flow here at electrode
48, likely because of the contact between sand and granite. Work planned for the summer
of 2014 will confirm the existence of the granite at this location.
Resolution on the east side of the profile was impacted by local construction activities
and the added complication of reinstalling electrodes with each survey (Figure 2.3). Consequently, the east side of the profile has lower resolution, especially near the mine outflow.
There are scattered high resistivity anomalies at various depths, possibly due to natural
landslide or rockfall deposits or construction activities related to the emplacement of the
road. The high resistivities under electrodes 68-72 likely correspond to dry granite bedrock.
Time-lapse resistivity data collected over four months show the development of two resistive anomalies at approximately 5 m bgs (Figure 2.4), and the development of a more
extensive resistive feature in the near-surface (<3 m) of the wetland. Note that because
resistivity increases with depth at this site, any changes at depth have to be larger in magnitude to produce the same percent change; as a result, the surficial resistivity anomalies,
though they represent a larger percent change, are actually of a lower magnitude than the
changes at depth (Figure 2.5).
17
West
East
Mine outflow
Large surface flow
E72
Road
Wetland Area
E48
E24
GW5
MW02
E1
CGW1
Borehole Log Key
Clay and peat
Sand and gravel
Granite bedrock
Figure 2.2: Resistivity inversion of data collected on July 12th, 2013. Electrodes (E1-E72),
model fitting parameter results, borehole logs, and the general character of vegetation are
shown.
Figure 2.3: Resolution of inversion of data collected on July 12th, 2013. Note, because of
smoothing issues, only data for 1 m x 1 m pixels are shown.
18
Figure 2.4: Time-lapse percent changes in resistivity, relative to background inversion of 12
July 2013 data.
Figure 2.5: Time-lapse absolute change in resistivity, relative to background inversion of 12
July 2013 data.
19
2.7.1
Supporting data
Assuming that mineralogy and surface conduction remained constant over the study
period, three parameters could explain the development of the resistive anomalies at depth:
saturation, temperature, and pore fluid conductivity.
Field observations suggested and water levels in the boreholes confirmed that the wetland
stayed saturated throughout the field campaign, as the static water level was typically within
0.5 m of the ground surface. As a result, no saturation correction was necessary for the
resistivity data and saturation di↵erences cannot explain the development of the resistive
anomalies.
Localized enhanced communication with surface water could produce imagable temperature anomalies in the subsurface, in e↵ect acting as a temperature tracer (Musgrave & Binley,
2011). To explore the possibility of the anomalies being temperature based, the resistivity
response to small changes in temperature was modeled linearly (Schon, 2004):
⇢(T ) =
⇢(T0 )
1 + (T T0 )
(2.3)
where
⇢ resistivity (ohm-m)
T temperature ( C)
T0 initial temperature ( C)
constant, equal to 0.025 ( C 1 )
Over the course of the field campaign, temperatures in the top 2 m bgs generally increased
by about 2.5 C by the end of September, before decreasing by about 2.5 C by the end
of October According to Equation 2.3, a temperature decrease of 2.5
C would produce
a roughly 6.8 % increase in resistivity, which would not completely explain the resistivity
anomalies. However, the development of a localized layer of ice, which is much more resistive
20
than water, at the site in October indicates that some areas had more pronounced cooling
than others. A temperature decrease of 8 C would produce a resistivity increase of 25%,
which is entirely within the range of the observed data. It therefore likely that the resistivity
changes observed at the surface are primarily temperature driven.
However, water temperatures in the boreholes remained relatively constant (+/- 1 C)
at depths greater than 1.5 m bgs, and any changes were typically increases from July to
October. Even if some localized temperature decrease occurred away from the boreholes,
the average starting temperatures at depth was approximately 3.5
C, indicating that a
highly improbably phase change would need to occur to produce the resistive anomalies at
depth. Therefore, it is unlikely that the resistivity anomalies could be completely explained
by temperature changes.
This leaves conductivity change as the only possible explanation for the development of
resistivity anomalies. Conductivity decreases could have occurred if preferential flow paths
exist in the wetland that allow for the flushing of contamination. Since the contamination
is typically produced during the dry season, any additional flow through the system after
the spring snowmelt pulse is likely to have lower TDS concentrations and produce a more
resistive signal.
There are multiple lines of evidence in the borehole data to suggest that such preferential
flow paths exist in the wetlands. There was a small (1 C) but consistent positive temperature anomaly observed in two of the boreholes (MW02, MW03) at approximately 4 m depth,
which indicates localized hydrological connection with either sulfide oxidation, or upgradient
surface waters. Some AMD piles have been known to reach internal temperatures of 65 ,
driven by the exothermic nature of Equation 1.1 (Lefebvre et al., 2001). Note that sulfide
oxidation would not be favorable at such depths below the water table, so the anomalously
warm water would need to have transported from upgradient. TDS initial values were more
variable and changes were more localized than they were for temperature. There was little
consistency between boreholes screened into the same aquifer; for example, CGW1 had TDS
21
concentrations nearly an order of magnitude higher than MW02, even though they are 100
m apart and both screened into the surface aquifer. Furthermore, the trends in TDS over
the study period were not consistent between boreholes: MW04 had the largest relative
variability in TDS, as it nearly doubled in TDS from 180 to just over 300 µS from the initial
July 12th to the final Oct 28th measurement. TDS in GW05 gradually decreased from about
900 µS to just over 700 µS until October 1st, before rebound back to over 800 µS. There
was little TDS change in GCW1 or MW02.
Petrophysical relationships allow for examination of the feasibility that TDS is controlling
the trends in resistivity. Archie’s law relates pore fluid conductivity to bulk conductivity
(Archie, 1942; Yuval & Oldenburg, 1996):
w
=
a✓m
(2.4)
where
w
a
✓
m
pore fluid conductivity µS/cm
bulk conductivity µS/cm
constant
porosity (-)
cementation factor
To calculate the pore fluid conductivity change necessary to produce a resistivity change
of 50 ohm-m, Equation 2.4 was used with a = 1.2 and m = 2, both values consistent with
clay-free unconsolidated rock (Yuval & Oldenburg, 1996). The results of the calculation
depend on initial resistivity, but near the center of the anomalies, where starting resistivity
is approximately 130 ohm-m, an decrease in pore fluid conductivity of about 200 µS/cm
would be required to produce the observed signal. This value is within the range of observed
conductivities at the site (up to 1600 µS/cm), therefore TDS flushing could explain the
development of resistive anomalies at depth.
22
2.8
Sensitivity Analysis
As a result of the non-uniqueness of the inverse problem, a large number of potential
solutions exist that fit the data sufficiently well. The sensitivity of the inversion to changes
in the model resistivity was investigated with synthetic data to develop an understanding
of how regularization impacts the inversion’s ability to resolve features of di↵erent sizes and
contrasts, and to constrain the range of possible features that would be expected to produce
the tomograms in Figure 2.4. Synthetic data parameters were chosen to mimic the resistive
conditions of this study, such that the results inform on real-world, non-uniform settings and
geometries. The below process (schematically depicted in Figure 2.6) was followed:
Sensitivity Model Flow Diagram
July 12th Resistivity
Calculate anomaly
magnitude
Oct 28th Resistivity
Add anomalies
to background
Summarized region
Modeled
anomaly
R2 inverse
Data errors
from July 12th
Captured anomaly
R2
forward
V
I
Equipotential
lines
+ 1 % noise
Figure 2.6: Flow diagram of the sensitivity modeling process. ’Summarized region’ denotes
the area over which the total resistivity anomaly is calculated.
• Total resistivity change was calculated in a region encompassing the two anomalies
that developed between the background and final datasets, respectively collected on
July 12th and Oct 28th.
23
• The recovered resistivity change was then split into two anomalies and added onto the
background resistivity model in locations that replicated the observed anomalies. The
anomalies were emplaced at y = -5 m with circular geometries that had variable radii.
Total mass remained constant and equal to the original anomaly mass, such that an
areally larger anomaly would have lower contrast against the background.
• A forward solution was computed for the synthetic anomalies using the same quadripole
sequence that was used to collect field data.
• The resulting forward model data were given 1% random noise to replicate the noise of
field data. The same error parameters that were used to evaluate the field data were
assigned to the forward model data.
• The inverse solution was then computed in R2 (Figure 2.7).
In short, forward and inverse solution was computed for the observed anomalies from
this study using di↵erent geometries to test the inversion’s ability to resolve anomalies of
di↵erent sizes and contrasts. As was expected, results show that the model is less sensitive
to smaller plume structures (Figure 2.7). The inversion was unable to detect anomalies with
radii of 0.5 to 1 m at relatively shallow depths of 5 m, even though the contrasts of these
anomalies are much larger. The inversions of the actual data had much more weight given
to the data, and would be expected to outperform these synthetic demonstrations.
2.9
Discussion and Conclusion
Time-lapse ER techniques allowed for non-invasive location and characterization of flow
paths in an AMD impacted wetland. Borehole measurements support the interpretation
that preferential flow is occurring. Because of smoothing inherent in inversions, the exact
geometry of the flow paths is unknown, but the sensitivity tests reveal that they must be
at least several meters in diameter. Petrophysical relationships suggest that the localized
24
Figure 2.7: Sensitivity modeling results.
changes in TDS within the flow paths are on the order of hundreds of µg/L, which is within
the temporal variability of the borehole data.
The results of this study indicate that contamination may be discharging from the wetland to Peru Creek that would have been missed by spatially localized traditional borehole
sampling. Synoptic samples along Peru Creek would also fail to account for the pulse-like
character of the contaminant transport. If the pore fluid changes are driven by leakage from
the mine workings, there may be substantially more flow leaking from the mine workings
than previously thought, which would have substantial implications for the hydrogeology of
the mine workings and proposed remediation e↵orts. Furthermore, this study shows that
the wetland metals contributions would be expected to change over time as the pulse of
contaminant travels through the wetland.
The results of this study could be a significant contribution to the development of a
reactive transport model of the site. Reactive transport models are an important tool for
developing quantitative predictions of remediation activities (e.g., see Benner et al., 2002;
Walton-Day et al., 2012); however, collecting sufficient data in the field to parameterize these
25
models in AMD impacted regions can be difficult, especially considering that heterogeneous
distributions of either potentially contaminant-retarding agents or hydraulic conductivity
can dramatically complicate interpretation of contaminant breakthrough curves. In particular, the presence of preferential flow paths has the same e↵ect on resulting breakthrough
curves as geochemical attenuating processes, which if not properly distinguished could lead
to overestimates of the total amount of attenuation taking place (Malmström et al., 2008).
The results of this study demonstrate the need to develop much more control on subsurface
processes at legacy AMD sites.
2.10
Acknowledgments
Dr. Rob Runkel’s time was supported through the USGS Toxic Substances Hydrology
Program. Site access was permitted by the U.S. Forest Service, in particular through Paul
Semmer and Brian LLoyd. Dr. Alexis Navarre-Sitchler provided helpful guidance throughout
the project. Many people put in long hours in the field to make this work possible. In
particular, CSM graduate students Ben Bader, Skuyler Herzog, Emmanual Padilla, and
Michael Sanders were of tremendous assistance. The authors would also like to thank Dr
David Benson, Dr. Katie Walton-Day, Dr. Stan Church, Mark Rudolph, and Je↵ Graves for
their contributions.
26
CHAPTER 3
FUTURE WORK
This study was successful in its goal of using the natural first-flush of mining contamination to characterize subsurface flow paths. As with any research project, important questions
remain. The AMD setting has many attributes that make it ideal for studying hydrological
processes, such as strong annual signals and a rich history of data. There is also a significant
human impact and the opportunity to intersect pure science research with water resource
and engineering needs. Three possible extensions of this work are outlined below, including
long-term monitoring, reactive transport imaging, and mine workings characterization.
3.1
Long-term monitoring
The most direct extension of this project would be to continue to observe the identified
anomalies, seeking both to develop a better constrained quantitative model of subsurface
changes, and to observe the e↵ects of interannual flow variability on transport through
the site. It would have been logistically challenging to sample the subsurface over an entire
survey of this extent, especially with the complexity of multiple possible contaminant sources.
Better control data should be easier to obtain with a more targeted study, possible now given
these new measurements of subsurface hydrology.
The stream-quality consequences of flow variability in AMD generating systems can be
tremendous. Comparison of two stream tracer studies conducted during the extreme drought
2002 water year (Todd et al., 2005), and the relatively wet years of 2004-2005 indicate that
lower flows are more likely to have higher contributions from di↵use groundwater sources
(Rudolph, 2010). Furthermore, there is evidence that climate change is increasing the sulfide
weathering rate and in turn increasing in-stream zinc concentrations in the Snake River
watershed (Crouch et al., 2013; Nordstrom, 2009). During this study, there was large storm
in September, in which more than 12 cm of rain more than doubled the average monthly
27
precipitation. If the anomaly behavior is substantially di↵erent between the results seen in
this study versus a more normal or drier water year, such observations could provide insight
into the e↵ects climatic change will have on long-term stream quality.
3.2
Reactive Transport
The application of ER to imaging reactive processes is a promising new field that had
previously been largely confined to laboratory settings (e.g., Regberg et al., 2011), but is now
starting to emerge as a viable field technique (Flores Orozco et al., 2011). With its strong
annual signals and high total solute concentrations, AMD o↵ers an ideal setting to pursue
the development of ER to characterize reactive transport. Contaminants were assumed to
be conservative in this study because the focus was on the deeper subsurface, where low
concentrations of nutrients typically depress reaction kinetics. However, AMD transport in
the near-surface is complicated by attenuation mechanisms, including reactions of sorption,
complexation, precipitation-dissolution, oxidation-reduction, and biological uptake (Gandy
et al., 2007). In particular, wetlands have been identified as settings that host many AMD
attenuating reactions due to their steep redox and dissolved organic matter (DOM) concentration gradients (Johnson & Hallberg, 2005). Wetlands with clay deposits can also host
sorption reactions as positively charged metals adsorb to negatively charged clay particles
or organic material (Sheoran & Sheoran, 2006). The resistive anomalies in the shallow subsurface in this study were interpreted to be primarily driven by temperature changes from
localized surface-groundwater communication, but where there is enhanced communication
with surface water there is also likely to be mixing of organic-rich surface water to provide
nutrients for enhanced sulfate and metal oxide reduction. Therefore, there is a likely TDS
component to the observed very near-surface signal (<2 m) beyond the resolution capacity
of this study.
ER is uniquely capable of informing on reactive transport of AMD. Subsurface residence
time is a crucial parameter in predicting the extent of AMD attenuation because the kinetics
of typical attenuating reactions are slow (Gandy et al., 2007). Furthermore, solubilities and
28
bioavailabilities of many mine-related solutes are sensitive to redox conditions, and many
studies have demonstrated that redox conditions become more reducing along subsurface
flow paths due to microbial respiration and mixing behavior (e.g., Findlay et al., 1993).
Traditional stream-groundwater field studies have been unable to distinguish between mobile
and less-mobile domains of subsurface solutes (Ward et al., 2010), but ER is sensitive to all
subsurface domains and could be used to more accurately quantify residence time.
Several modifications to the existing survey of this study would need to take place to
e↵ectively capture reactive transport. The electrode spacing would need to be tightened because these reactive processes are smaller scale and are confined to the shallow near-surface.
More temperature and chemistry control data should also be developed. In particular, pH
control data would be crucial, because pH has been described as a master variable with
regards to metals transport in streams, as it a↵ects metals speciation and precipitation/dissolution through solubility limits (McKnight & Bencala, 1990). Much of the near-surface of
the wetland would lack the first-flush pulse of contaminants that acted as a natural tracer
for this study, but there were several surface flows in the wetland that could be targeted.
If first-flush dynamics prove difficult to capture in a stream setting near the Pennsylvania
Mine because of site access issues or snow cover, natural contaminant pulses from storm
events could be used instead. Small storms have been documented to increase the flow of
streams near the continental divide by a factor of 2 to 3 (Campbell & Clow, 1995), and
there has been well documented hysteresis storm event cycles, with TDS concentrations in
the rising limb being larger than in the corresponding falling limb of the stream hydrograph.
This e↵ect was attributed to flushing solutes that accumulated in the hyporheic zone, where
the longer flow paths allowed for more interactions with the bu↵ering capabilities of soils
(Campbell & Clow, 1995; Nagorski et al., 2003). However, the opposite e↵ect has also been
observed in which the rising hydrograph has lower solute concentrations (Harvey et al., 2012).
The hypothesized mechanism was storm events pushing solutes deeper into the hyporheic
zone, thereby decreasing the solute concentration through dilute and by slowing the reentry
29
of solutes into the stream (Harvey et al., 2012). Again, because it provides a distributed
measurement of mobile and less-mobile domains, ER provides an ideal measurement for
distinguishing between these theories.
Reactive transport in an AMD wetland would have a significant biogeochemical component because of the importance of microbes in catalyzing many of the AMD attenuating reactions. In particular, feedback mechanisms can be important components of transport. For
example, water velocity and turbulence can a↵ect microbial community structure, which can
alter hydrodynamic conditions through the precipitation of metal oxides (Bottacin-Busolin
et al., 2009). Developing quantitative descriptions of these processes in AMD settings is
crucial to predicting AMD transport and designing remediation schemes, and would be a
significant contribution to the emerging field of biogeophysics.
3.3
Characterization of Pennsylvania Mine Leakage
At the time of this writing, a plan is underway to emplace at least one and possibly
two bulkheads inside the mine. For the bulkhead to be e↵ective it needs to keep the mine
workings saturated, such that no further sulfide oxidation takes place. Success of the plan is
contingent on the inner mine workings being sufficiently watertight so as to prevent leakage
around the bulkheads. All of the evidence to date, including tracer tests, groundwater
chemistry analyses, and this research, suggest that contamination is leaking from the mine
workings into the wetland. However, these results do not inform on the nature or location
of these leaks.
It would be a substantial contribution to characterize the flows exiting the mine. Unfortunately, identifying and characterizing fracture flow remains extremely difficult for most
geophysical techniques because of resolution issues inherent to settings in which the vast
majority of the flow is contributed by a small fraction of the medium. In this case, the
seeps and fractures through which flow discharges from the mine workings would likely be
on the order of centimeters, while the adjacent mine workings are on a scale from meter to
tens of meters. However, this could perhaps be overcome if, instead of imaging the frac30
tures directly themselves, the material immediately down gradient of the mine workings was
imaged with resistivity. Any significant flows exiting the mine are likely to a↵ect localized
water content levels and would therefore be represented as conductive anomalies that develop through time. Installation of the bulkhead should produce large head gradients within
the mine, which could drive more flow through the fractures and make the anomalies easier
to detect, but, of course, any information obtained after the bulkhead installation will be of
little predictive value for the current construction e↵orts.
31
APPENDIX A - EXTENDED METHODS
A.1
Resistivity Data Analysis
Due to the construction activities, several events took place that influenced the quality
of results on the eastern side of the profile. The road adjacent to the mine outflow was
regraded. Electrodes 1-9 had to be manually replaced, and while an e↵ort was made to
maintain electrode spacing, the regraded terrain was significantly altered such that the data
from those electrodes became suspect. Further complicating the issue, the road debris was
dumped over the embankment under electrode 9, substantially altering the material with
which it was in contact. In the first week of October, the mine outflow was redirected
through a culvert buried about 0.25 m under the road, running parallel with the survey at
this location. As a result, the mine outflow began to pool around electrodes 9-11. Significant
ferricrete deposits were visible at the bottom of the embankment by the end of the 2013 field
season.
Because of the construction, some of the measurements from the east side of the array
had higher error than was reflected in the data stacks. To filter out the quadripoles that
had been a↵ected by the construction activities, the coefficient of variation was calculated
for each quadripole over the extent of the survey. Quadripoles that had coefficients of
variation greater than 0.25 in the construction zone were removed from the analysis. The
high resistivities of that area make such large resistive variations unlikely. As a result, most
of the quadripoles involving electrodes 6-9 were removed from the analysis, resulting in a
large loss of resolution near the surface in that region.
Individual errors for each quadripole were used to weight the data in the inversions of
this analysis, however, several studies report success fitting a general error model to the
data instead (Binley & Kemna, 2005; Musgrave & Binley, 2011). The general error model
approach has the advantage of smoothing out chance occurrences of high or low error on
32
relatively sparse datasets, but it is only appropriate when measurement errors are expected
to be similar across the resistivity survey. The east and west sides of the survey of this study
di↵ered enough in their error that individual data error was maintained. Chance deviations
from the measurement error were minimized through the collection of many measures per
time-step.
Measurement error is often quantified from the variance between reciprocal quadripole
measurements, but that not was not done in this study due to the large amount of time
necessary to collect reciprocal measurements. The benefit of using the Syscal Pro unit is
that 10 measurements can be collected simultaneously with the dipole-dipole configuration,
but the geometry of reciprocal measurements allows for only one measurement at a time.
Again, the collection of a large number of stacks is thought to make up for the lack of
reciprocal measurements.
R2 o↵ers the ability to do regular inversion, inversion relative to a background dataset,
and di↵erence inversion, in which the di↵erence between the dataset and the background
dataset are inverted. Di↵erence inversion was chosen for this analysis because inversion
errors from the electrode configuration and numerical errors in the model tend to be constant
through time, and thereby cancel in time-lapse analyis, allowing a fit with lower error and
fewer artifacts (LaBrecque & Yang, 2001). Di↵erence inversion is also faster than standard
inversions since the new model is generally similar to the background model.
R2 is capable of performing singularity removal in the forward model, but only if the
model surface is represented by the straight line y=0, because singularity removal is only
available for analytical solutions. Because of the significant topographical variability at the
site, it was decided to forgo singularity removal and instead model the site topography as
recommended by the model creator (Personal communication, Andrew Binley).
A.2
Finite Element Mesh Design and Gmsh
Inversion requires the development of a finite element mesh (FEM) over which the model
space can be discretized and the forward solution calculated. Several guiding principles were
33
adopted for this project:
• Nodes must be placed at all electrodes.
• At least three nodes must be present between electrodes.
• Element spacing must be tighter in the shallow subsurface and in the areas of interest.
As the study area had regions of rather abrupt topography, a triangular element FEM
was tested first, because, in principle, triangular elements can handle more complex geometries than quadrilateral elements. The program Gmsh was used to generate the first
FEMs (Geuzaine & Remacle, 2009). Gmsh loads a user-created input script outlining the
model geometry and desired element size and outputs a FEM. A simple matlab script is
then used to transform the coordinates of the FEM to an appropriate format for R2. This
method requires a little more upfront time investment than the FEM imbedded into R2,
but it has several significant advantages, including flexible element design and stronger mesh
visualization tools. Drawbacks of using a triangular mesh are that R2 seems to have a more
stringent total element count for triangular meshes (around 15,000 as compared to 30,000 for
quadrilateral meshes), the output resolution matrices are less clean, and the computation is
increased. Final RMS error for the inversions also increased, even with finer element spacing
and the same error parameters. The disadvantages of the triangular mesh were perceived to
outweigh the advantages, and so a quadrilateral mesh was used instead for this study.
34
APPENDIX B - MISCELLANEOUS DATA
More data was collected than ultimately made it into the main body of the paper. Those
data are presented here for completeness, including:
• Resistivity time-steps that did not contribute significantly to the trends and so were
omitted from the final paper. All collected resistivity data are presented in Figure B.1
and Figure B.2.
• Data used to parse the resistivity signal, such as the borehole temperature and conductivity measurements (shown in Figure B.3, temperature averaged by depth in Figure B.4).
• Sensitivity modeling results, displayed in absolute resistivity changes (Figure B.5).
At depths greater than 5 m, the inverted anomalies of the sensitivity analysis show
substantial smoothing beyond the boarders of the synthetic injected anomalies. This
may be because of the substantial amount of weight given to the smoothing term in
the inversion, as indicated by the large alpha values (shown in Figure 2.7).
• Flow conditions in Peru Creek and meteorological conditions of the surrounding area
(locations show on Figure B.6). Data were collected using pressure transducers as a
part of an abandoned e↵ort to design a flow model of the site. The temperature and
pressure data recorded in Peru Creek are displayed in Figure B.7, while the correcting
air pressure and temperature are in Figure B.8. The stream was also gaged, and the
resulting discharge measurements are recorded in Table B.1. Note that as of 18 July
2013, transducers are still in the field, and the resulting data will be incorporated into
the final draft of this paper.
35
Figure B.1: All wetland inversions with fitting results. All changes are relative to the
background inversion of data from 12 July 2013.
Table B.1: Discharge measurements from Peru Creek.
Date
10/28/2013
11/13/2013
Gage Height
0.22 m
0.21 m
36
Discharge
0.14 m3 /s
0.13 m3 /s
Figure B.2: Resolutions of all inversions through the wetland.
37
8
A
GWC1
MW04
7
MW02
MW03
Temperature (C)
6
GW05
5
4
3
2
07/01/13
08/01/13
09/01/13
10/01/13
1400
11/01/13
B
1200
Conduc vity ( S)
1000
800
600
400
200
0
07/01/13
08/01/13
09/01/13
10/01/13
11/01/13
Figure B.3: Temperature (A) and conductivity (B) measurements taken from boreholes in
the wetland area.
38
8
< 1.5 m bgs
7.5
> 1.5 m bgs
7
Temperature (C)
6.5
6
5.5
5
4.5
4
3.5
3
07/01/13
08/01/13
09/01/13
10/01/13
11/01/13
Figure B.4: Average temperatures measured in the boreholes at shallow <1.5 m bgs., and
deep depths.
Figure B.5: Sensitivity modeling results.
39
> G W3
HOBO W1
>GWC1
D
Stream gage
D
HOBO A1
> G W5
>
>>MW02-04
HOBO A2
Pressure transducer
Electrode
>
Sample well
Resistivity survey
Pennsylvania Mine
Peru Creek
Surface inflow
Elevation contour (10 m)
Wetland area
0
0.025
0.05
0.1 Kilometers
±
Figure B.6: Additional measurement locations, including pressure transducers and stilling
well. HOBO W1 denotes the location of the transducer installed in Peru Creek. HOBO A1
denotes the location of the air pressure transducer from October to November. HOBO A2
denotes the air pressure transducer left at the site over winter.
40
A
10
9
8
Temperature (°C)
7
6
5
4
3
2
1
0
9/28/2013 0:00
10/8/2013 0:00
10/18/2013 0:00
10/28/2013 0:00
11/7/2013 0:00
B
22
21
20
Pressure (cm H20)
19
18
17
16
15
14
13
12
9/28/2013 0:00
10/8/2013 0:00
10/18/2013 0:00
10/28/2013 0:00
11/7/2013 0:00
Figure B.7: Water temperature (A) and pressure (B) measurements of HOBO W1. Pressure
has been corrected for air pressure and converted to cm water.
41
A
20
15
Temperature (°C)
10
5
0
-5
-10
-15
9/28/2013 0:00
10/8/2013 0:00
10/18/2013 0:00
10/28/2013 0:00
11/7/2013 0:00
B
69.5
69
Pressure (kPa)
68.5
68
67.5
67
66.5
9/28/2013 0:00
10/8/2013 0:00
10/18/2013 0:00
10/28/2013 0:00
11/7/2013 0:00
Figure B.8: Air temperature (A) and pressure (B) measurements of HOBO A1.
42
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