ABSTRACT NEAR-SURFACE GEOPHYSICAL IMAGING OF THE INTERNAL STRUCTURE OF EL CAPITAN MEADOW ROCK AVALANCHE IN YOSEMITE NATIONAL PARK, CALIFORNIA

Rock avalanches are a large form of mass movement which, while uncommon, are particularly destructive due to their very high volumes and long flow distances. Due to their rarity, they are typically studied well after they occur. Mass movement events can subject humans to risks and casualties as well as cause massive infrastructural damage. The El Capitan Meadow rock avalanche lies at the foot of El Capitan in Western Yosemite Valley. I investigated this avalanche using Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) to image the internal structures, depth, and topography of the underlying paleo- surface of the valley. GPR and ERT surveys were conducted along three profile lines over the avalanche deposit. ERT results showed a strong but gradual resistivity contrast between the low resistive soil of the prior valley surface and the highly resistive rock avalanche deposits, however, detecting the interface with precision was difficult. GPR results revealed several sharp interfaces within the subsurface making it impossible to identify which one was the interface to the valley floor. Selected interfaces from the GPR model were incorporated into the ERT inversion process, where the GPR interfaces, which indicated the true location of the valley floor, was the one which produced the sharpest resistivity contrast in its ERT model. At the intersection points of the profiles, the estimated depths to the paleo-valley floor had similar elevation revealing a flat extension of the exposed valley floor beneath the rock avalanche deposits.

Christine Horngjen Liu August 2018

NEAR-SURFACE GEOPHYSICAL IMAGING OF THE INTERNAL STRUCTURE OF EL CAPITAN MEADOW ROCK AVALANCHE IN YOSEMITE NATIONAL PARK, CALIFORNIA

by Christine Horngjen Liu

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Geology in the College of Science and Mathematics California State University, Fresno August 2018 APPROVED For the Department of Earth and Environmental Sciences:

We, the undersigned, certify that the thesis of the following student meets the required standards of scholarship, format, and style of the university and the student's graduate degree program for the awarding of the master's degree.

Christine Horngjen Liu Thesis Author

Alain Plattner (Chair) Earth & Environmental Sciences

John Wakabayashi Earth & Environmental Sciences

Greg Stock National Park Services (Yosemite NP)

For the University Graduate Committee:

Dean, Division of Graduate Studies AUTHORIZATION FOR REPRODUCTION OF MASTER’S THESIS

I grant permission for the reproduction of this thesis in part or in its entirety without further authorization from me, on the condition that the person or agency requesting reproduction absorbs the cost and provides proper acknowledgment of authorship.

X Permission to reproduce this thesis in part or in its entirety must be obtained from me.

Signature of thesis author: ACKNOWLEDGMENTS I want to express my gratitude to Dr. Alain Plattner for his advising in my project. Alain’s guidance and enthusiasm has provided me the knowledge and ability to accomplish this research and go beyond my limits. I also want to thank Sandi Smart, Austin Robbins, Alex Briand, and Mikhael Bratanata for their services in helping me in the field. This work would not have been done without you and your support. I am grateful for Michelle Johnson's guidance in grammar writing skills and counseling me throughout my time at Fresno State. I want to thank Deborah Camper and Iesha Rosenboro for their assistance in proofreading. Thank you to Greg Stock for the open communications about this research project and providing more insight about my project area. Many thanks to the department members, Chris Pluhar, Alex Pytlak, Sue Delcroix, Amanda Ausman, Jerome De Graff, John Wakabayashi and many more for their friendship, assistance in completing paperwork, aiding me in presentations, and encouraging me to push forward. I also want to thank Dr. Laurent Montesi, Dr. Vedran Lekic, and Dr. Daniel Lathrop for their support for my educational advancement. Lastly I also want to thank Dr. Freja Nordsiek for her support, friendship, and mentorship throughout the early transition of my graduate career. This paper would not have been possible without the support of my mother, Susan Liu and my boyfriend, Paul Young, who both encouraged me to pursue my passion in geophysics. Thank you, Paul, for your steadfast patience when I needed to vent and emotional assistant throughout my thesis process. TABLE OF CONTENTS Page

LIST OF TABLES ...... vii

LIST OF FIGURES ...... viii

INTRODUCTION ...... 1

Geology of Yosemite Valley ...... 3

Near-Surface Geophysical Methods ...... 7

LITERATURE REVIEW ...... 10

METHODOLOGY ...... 14

Gravity Mapping ...... 14

Seismic Refraction ...... 15

Seismic Reflection ...... 16

ERT ...... 17

GPR ...... 21

Survey ...... 24

Field Deployment ...... 26

Data Processing ...... 36

Selecting Interfaces in GPR ...... 63

Combining ERT and GPR ...... 68

RESULTS ...... 71

ERT Inversion ...... 71

Ground Penetrating Radar ...... 73

Combining GPR and ERT ...... 79

DISCUSSION ...... 85

CONCLUSION ...... 88 vi vi Page

BIBLIOGRAPHY ...... 89

APPENDIX: SUPPLEMENTAL FIGURES ...... 95

LIST OF TABLES

Page

Table 1. Near Surface Geophysical Methods Versus Units ...... 15

LIST OF FIGURES

Page

Figure 1. Location of El Capitan situated in Yosemite Valley generated from a LiDAR-based digital elevation model (Brody et al., 2015, Stock, 2016)...... 5

Figure 2. Representation of a pseudosection displaying raw data...... 18

Figure 3. ERT inversion flowchart ...... 19

Figure 4. 2-D discretization of parameter mesh (Gunther et al., 2006) ...... 19 Figure 5. Applied regularization “smoothing” on three resistivity models (a, b, c) from the observed data (d, e, f). (Obtained from Oldenburg et al., 1999) ...... 20 Figure 6. Diagram of how common-offset GPR works to obtain a profile radargram...... 21

Figure 7. Raw radargram with white-black color pixels using the gray colormap. 22

Figure 8. Walkaway WARR procedure (modified from Morozov lecture, 2018) . 23

Figure 9. Representation of a raw WARR radargram...... 24 Figure 10. The southeast face of El Capitan, showing the location of the El Capitan Meadow rock avalanche...... 24 Figure 11. LiDAR image obtained from Google Earth of the El Capitan Meadow Rock Avalanche depicting three profile lines chosen for the data collection for both ERT and GPR (LiDAR image provided by Stock (2017))...... 25 Figure 12. Arrangement of current (A and B) and potential (C and D) electrodes for Dipole-Dipole, Wenner, and Schlumberger configuration arrays (Lowrie, 2007)...... 29 Figure 13. Raw and manually corrected DGPS measurements obtained along each profile of every electrode position for ERT...... 31

Figure 14. Profile distance along the elevation for each ERT transect...... 32 Figure 15. Raw and manually corrected DGPS measurements obtained along each profile for GPR...... 34

Figure 16. Profile distance along the elevation for each GPR transect...... 35 ix ix Page

Figure 17. Pseudosection of profile Line 1 depicting raw resistivity data for two different configurations...... 36

Figure 18. Pseudosection of Line 1 after the extreme data point was deleted...... 37 Figure 19. Pseudosection of profile Line 2 depicting the areas of electrical resistivity for two different configurations...... 38 Figure 20. Pseudosection of profile Line 2 after two extreme data points were deleted...... 38 Figure 21. Pseudosection of profile Line 2P depicting areas of electrical resistivity for two different configurations...... 39 Figure 22. Pseudosection of profile Line 2P after the extreme values were deleted...... 40 Figure 23. Pseudosection of profile Line 2P after the second removal of data outliers...... 40 Figure 24. Pseudosection of profile Line 3 depicting the areas of resistivity for two configurations...... 41 Figure 25. Perceptually uniform color scheme “Viridis” (top) and Rainbow color scheme “Jet” (bottom)...... 42 Figure 26. ERT inversion models generated from inverting data utilizing the BERT software with the standard settings...... 43

Figure 27. Raw, unprocessed WARR radargram...... 44 Figure 28. Application of the DEWOW filtering. The stripes/bands disappear, and the radar waves appear weaker further in depth...... 46 Figure 29. Processed WARR radargram with DEWOW filtering and TPOW with parameter = 0.73 ...... 47 Figure 30. Semblance analysis results for WARR radargram shown in Figure 29...... 48

Figure 31. The WARR radargram with airwave and ground wave...... 49

Figure 32. “WARR hyperbola Semblance” plot...... 50 Figure 33. WARR figure depicting both the refraction/linear lines and the reflection (hyperbola) curves best fitted over the radar waves arrivals. . 51 Figure 34. Raw GPR radargram of Line 1 depicting a horizontal stripe and weak reflection signals in the subsurface...... 53 x x Page

Figure 35. Raw GPR radargram of Line 1 with the alignment depicting a horizontal stripe...... 54 Figure 36. GPR radargram of Line 1with the airwave removed and increased contrast...... 54 Figure 37. This GPR radargram of Line 1 depicts the applied TPOW with parameter set to= 1.25, enhancing the strength of the signals with later arrival times...... 55

Figure 38. Radargram of Line 1 with topographical correction...... 56 Figure 39. Bluewhitered colormap radargram of Line 1 with topographical correction...... 56

Figure 40. Raw GPR radargram of Line 2 with an observed horizontal stripe ...... 57 Figure 41. This processed GPR radargram of Line 2 depicts the applied TPOW gain strengthening the signals further in depth...... 58

Figure 42. Radargram of Line 2 with topographical correction...... 58 Figure 43. Bluewhitered colormap radargram of Line 2 with topographical correction...... 58

Figure 44. Raw GPR radargram of Line 3 with an observed horizontal stripe ...... 59

Figure 45. Processed GPR radargram of Line 3 with the TPOW gain ...... 60

Figure 46. Radargram of Line 3 with topographical correction...... 60 Figure 47. Bluewhitered colormap radargram of Line 3 with topographical correction...... 61 Figure 48. Processed radargram of Line 1 showing three ringing noise indicated by the red arrows...... 62 Figure 49. Processed radargram of Line 2 showing two ringing noise indicated by the red arrows...... 62 Figure 50. Processed radargram of Line 3 showing five ringing noise indicated in the red arrows...... 63

Figure 51. Close-up of a picked interface with a continuous reflection...... 64

Figure 52. The picked interfaces of the GPR Line 1 radargram...... 65

Figure 53. The picked interfaces of the GPR Line 2 radargram...... 66

Figure 54. The picked interfaces of the GPR Line 3 radargram...... 67 xi xi Page

Figure 55. The finalized ERT inversion of Line 1...... 71

Figure 56. The finalized ERT inversion of Line 2...... 72

Figure 57. The finalized ERT inversion of Line 3...... 72

Figure 58. Finalized radargram of Line 1 with topographical correction...... 74

Figure 59. Finalized radargram of Line 2 with topographical correction...... 75

Figure 60. Finalized radargram of Line 3 with topographical correction...... 75

Figure 61. Finalized GPR Line 1 radargram with selected interfaces...... 77

Figure 62. Finalized GPR Line 2 radargram with selected interfaces...... 78

Figure 63. Finalized GPR Line 3 radargram with selected interfaces...... 78 Figure 64. Line 1 inversion results allowing sharp transitions across the picked interfaces...... 80 Figure 65. Line 2 inversion results allowing sharp transitions across the picked interfaces...... 81 Figure 66. Line 3 inversion results allowing sharp transitions across the picked interfaces...... 82

Figure 67. Intersection points of the profile lines with map (below)...... 84

Figure 68. Volume estimation...... 96

INTRODUCTION

Investigating mass wasting occurrences provides insight on how they shape landforms. Rock falls, one example of mass wasting, are a natural geologic process wherein rocks become detached from higher elevations of a steep slope, fall rapidly due to gravity, and land at the base of the slope (Varnes, 1978). Rock fall debris, called the talus, usually consisting of angular fragments, will commonly rest at the base of the slope. This talus will often not extend beyond the mountainside onto the valley floor (Stock et al. 2014). Rock fall events are frequent and can pose a serious hazard to people and nearby areas. Nonetheless, rock fall events are not as destructive as rock avalanches, which occur rarely, but travel much farther from the mountainside and have much larger volumes (Wieczorek et al., 1999). Varnes (1978) defined rock avalanches as “complex landslides with a combination of rockfall avalanche and rockfall-debris flow.” Wieczorek et al. (1999) describe rock avalanches as rock falls that exceed 100,000 m3 in volume in a single event. Due to the rarity of rock avalanche events, they are seldom studied until after they occur. Mass wasting often occurs when a slope of a mountain, whose structure is weakened, experiences a trigger event. Over time, the structure of the rock on a mountain can be degraded through precipitation or the freeze/thaw cycle weathering process (Basahrat et al., 2015). Various mechanisms can trigger a rock avalanche, earthquakes being a common example. An earthquake can vigorously shake a tall steep-face mountain, and during this event, massive blocks of rock are broken off and travel at high speeds toward the base of the mountain (Coe et al., 2016). A more intense earthquake can cause more rock to fall, and a larger volume of rock will travel farther away from the mountain (Basharat et al., 2015). 2 2

Keefer et al. (1984) stated that a dearth of rock avalanches from earthquakes smaller than Mw 6.0 indicates that rock avalanches usually have higher thresholds. They are more prone to triggering by the longer duration and lower frequency shaking characteristics of large earthquakes. In Keefer et al. (1984)’s research, all of the reported rock avalanches induced by earthquakes with magnitude Mw 6.0 or greater had volumes over 0.5*106m3. There are many case studies conducted in regards to the volume of rock avalanche deposits, positively correlating the distance traveled by rock avalanche debris to the magnitude of seismic activity (Keefer et al., 1984). Examples include the 2005 Kashmir earthquake (Mw 7.6) event in Pakistan (Basharat et al., 2015), the 2002 Denali Fault earthquake (Mw 7.9) event in Alaska (Schultz et al., 2008), the Craigieburn Range earthquake (Mw 7) event in New Zealand (Whitehouse, 1981), and the 2007 Aysen earthquake (Mw 6.2) in Punta Cola of Southern Chile (Oppikofer, 2012). These earthquakes with magnitude greater than Mw 6.0 triggered rock avalanche events with large volumes over 1x106m3, with associated high casualties and large infrastructure costs. Another triggering mechanism that can induce rock avalanche events is a prolonged period of intense rainfall (Wieczorek, 2002, as cited in Evans, 2002). One example, a long period of intense rainfall triggered a rock avalanche from the north side of a mountain in Kings Canyon. This rock avalanche fell and blocked a nearby river. The rock avalanche debris formed a natural landslide dam which later failed and continued to travel towards lower elevation where it then flooded. In body of water environments, such as a flood, densely large rock debris may flow first and settle while the less dense rock debris slowly settle filtering through the large rock debris (Wieczorek, 2002, as cited in Evans, 2002). 3 3

Other triggering mechanisms also include weathering, fluvial undercutting the base of a slope which can reduce the stability of the hillside, and glacial downcutting that can erode downward steeping the slope of a hill (Wieczorek, 2002, as cited in Evans, 2002). The rocks that break off the slope or cliff can slide and bounce, breaking into many pieces before free falling and depositing where they land. The mechanics of rock avalanches depend on the mass of the debris and loose rock, the height of how high the mass has fallen, and the amount of impact during runout (Dade et al., 1998). These factors can attribute to the geometry of the spreading mass of debris during runout and deposit emplacement.

Geology of Yosemite Valley Yosemite Valley is located within Yosemite National Park in the Western Sierra Nevada of Central Eastern California. Topographically Yosemite is a U- shaped glaciated valley that have been through a number of glaciations within the Pleistocene epoch, but at least three has been documented in the Sierra Nevada (Huber, 1987). The youngest glaciation was the Tioga glaciation. The Tioga glacier grew over thousands of years and occurred roughly around 15 to 20 ka before the present. The Tioga glacier was responsible for removing fractured rock from the lower valley walls that had weathered and loosened since the previous glaciation. The Tahoe glaciation was older and slightly larger than the Tioga glaciation. This Tahoe glaciation had covered a total of 33 miles, and up to 1600 feet thick (Huber, 1987). Both the Tioga and Tahoe glaciation had minimal effect in shaping or smoothing the upper walls of Yosemite Valley (Huber, 1987). The glaciation before the Tahoe was the pre-Tahoe glaciation that lasted over 300 ka and was the most extensive in shaping and steepening the ~1 km tall granitic walls of Yosemite Valley (Huber, 1987). 4 4

Rock avalanche events in Yosemite Valley are rare, based on relatively few prehistoric rock avalanche deposits found there, but they pose a substantial hazard due to their large size and correspondingly long runout distances (Wieczorek et al., 1999). Wieczorek et al. (1999) estimated that only five rock avalanches have occurred in the valley since the last retreat of the Tioga glacier from the area 15,000 years ago. On the contrary, Stock et al. (2013) have estimated up to ten rock avalanche events within Yosemite Valley since the retreat of the Last Glacial Maximum glaciers 15-17 ka. Prior to the Tioga glacier, we are unable to assess older rock avalanche events because presumably, the glaciation left the valley floor free of rock avalanche debris. One of the rock avalanches after the Tioga glacier, the El Capitan Meadow Rock Avalanche, flowed out to the center of Yosemite Valley. The main focus of this paper is the El Capitan Meadow rock avalanche, located in Yosemite Valley in Yosemite National Park (Figure 1). The El Capitan Meadow rock avalanche was previously studied using topography (i.e., LiDAR maps), surface Beryllium exposure dating, and lithological and mineralogical analysis of the debris to find the source from where the avalanche broke off. These methods revealed information on when the rock avalanche event occurred and from which area of the mountain the rocks broke off. However, being restricted to surface observations, Stock and Uhrhammer (2010) do not offer a complete understanding of the internal structure of the rock avalanche, such as its depth and internal topography. The very steep cliff of El Capitan is composed of Cretaceous granite rock that is largely unjointed (Wieczorek et al., 1996). The Cretaceous granitic rocks originated from the Earth’s interior intruding older rocks, and crystallizing to create plutons (Putnam et al., 2015). These plutonic rocks of the cliff of El 5 5

Figure 1. Location of El Capitan situated in Yosemite Valley generated from a LiDAR-based digital elevation model (Brody et al., 2015, Stock, 2016). Note: 1.A denotes the location of Yosemite National Park (YNP) in California. 1.B shows the LiDAR map of Yosemite Valley. 1.C is a close-up LiDAR image of the El Capitan Meadow Rock Avalanche survey area of our study.

Capitan demonstrated assemblages of different formations of granite (El Capitan Granite and Taft Granite), diorite, and Tonalite of the Gray Bands that were aged to be between 106 and 103 Ma (Calkins, 1985; Peck, 2002; Putnam et al., 2015). Stock and Uhrhammer (2010) obtained five samples of granite from the large boulders atop the El Capitan Meadow rock avalanche to which they applied Cosmogenic Beryllium-10 age dating, which yielded “a mean age of 3.6 ± 0.2 ka” from four of the five samples. Stock and Uhrhammer (2010) confirmed that these rock samples were the result of a single rock avalanche event. The last of the five samples yielded an age of ~21.9 ka. The team surmised that it was sampled from part of a cliff face that was close to the surface at the time of the avalanche. Being close to the surface exposed the rock to cosmic radiation for a longer period than the rocks freshly exposed in the event (Stock and Uhrhammer, 2010). The team found that most of the samples broke off from the South-East face of El Capitan. 6 6

In searching for the trigger of the El Capitan Meadow rock avalanche, researchers have proposed that an earthquake event Mw 7.0 dated 3.6± 0.2 ka located in the neighboring area of the Owens Valley fault, a preexisting 1872 earthquake situated roughly 186 km SE from Yosemite Valley, maybe a possible candidate (Stock and Urhammer, 2010). However, there are numerous faults closer to Yosemite Valley that could potentially be candidates to trigger the rock avalanche event as well. Example of a nearby fault includes the Sierra Nevada Frontal Fault System with a slip rate (~1 mm/yr) is on the eastern flank of the Sierra Nevada and connects to other fault zones with a recurrence interval of 1-5 ka (Wakabayashi and Sawyer, 2000). There are other possibilities of numerous proximal internal faults within the Sierra Nevada Fault System that have smaller slip rates (> 1 mm/yr) and longer recurrence intervals of roughly 10 ka (Wakabayashi and Sawyer, 2000). From their visual examination, Stock and Uhrhammer (2010) speculated that the rock mass fell from ~650 m high and reached a maximum velocity of 100 ms-1. This fall impacted the talus slope and spread across the valley floor, extending 670 m from the base of the cliff (Stock and Urhammer, 2010). Stock and Uhrhammer (2010) also estimated the rock avalanche volume without the topography information of the underlying paleo-valley surface. They assumed that the underlying valley surface floor is a horizontal continuation of the current valley floor at 1204 m AMSL and calculated the volume in two parts - a distal portion, and a proximal portion to the cliff. They defined the distal portion as the part of the rock avalanche deposit which rests on top of the assumed flat valley floor, and the proximal portion as the section that rests on the base of the cliff, possibly having a wedge shape. Stock and Uhrhammer (2010) calculated both the distal and proximal portion volumes using LiDAR topography with the 7 7 assumed flat bottom at 1204 m. They estimated the volume of the distal portion to be 1.16x106m3, and the proximal portion to be 1.03x106m3, for a total of 2.19x106m3. The paleo-valley floor is situated at the base of El Capitan. The Tioga glaciation potentially played a part in shaping the valley floor, a time during which temperatures were fluctuating (Huber, 1987). Warmer climates caused the ice at the front of the glacier to melt faster than the glacier moving forward, and the ice front of the still-flowing glacier began to retreat up the valley. When the climate cooled, the ice in front of the glacier paused and temporarily stabilized near the El Capitan Meadow. There the Tioga glacier began to build a recessional moraine as it receded from its terminal position. Towards the end of the Tioga glaciation, a lake in Yosemite Valley was formed (known as Yosemite Lake) from the advancing Tioga glacier that excavated some of the pre-existing valley fill near the El Capitan moraine, which created a shallow lake basin (Huber, 1987). The El Capitan moraine dammed the shallow lake basin that was nearby the Merced River which flowed over a spillway through the moraine near the valley wall. Closer to the end of the Tioga glaciation, the glacier retreated up the valley walls with the melt-water debris coming from the glacier down to the shallow lake basin filling the lake with sediment, resulting in the shape of the valley floor to be a roughly horizontal plain (Huber, 1987).

Near-Surface Geophysical Methods Only the surface of El Capitan Meadow Rock avalanche has been examined, but we seek to investigate its internal structure without excavation or using any invasive methods. To choose non-invasive methods, we look at near- surface geophysical methods. Our research builds upon previous cases of near- 8 8 surface geophysical methods applied in mass wasting studies. One such case study employed Electrical Resistivity Tomography (ERT), Ground Penetrating Radar (GPR), and Seismic Refraction (SR) to map the subsurface interface along one profile of a talus deposit of a rock avalanche at Glacier Point in Yosemite Valley (Brody et al. 2015). Their results showed they were able to detect the basal boundary of the talus on top of the bedrock with the use of ERT and GPR, but the SR survey did not yield useful data. We chose to employ ERT and GPR to investigate the internal structure of El Capitan Meadow Rock Avalanche. Both these methods can be used to find the depths of the subsurface structures, including finding interfaces between dissimilar materials. This project will differ from the Brody et al. (2015) study because there is no prior investigated study of the internal structure of the El Capitan Meadow rock avalanche field area. Another difference is that Brody et al. (2015) examined one profile while our study examines three profiles, and our results from GPR were incorporated into the ERT inversion process whereas Brody et al. (2015) interpreted their data methods individually and visually compared their results. To study hazards related to rock avalanches, we would need to determine reliable ways to study individual rock avalanches. In particular, we would like to study their internal structures, including finding their volumes and depths. In this research, I focused on refining methods to study the internal structure of rock avalanches in Yosemite Valley. My research does not directly contribute to the hazard assessment of Yosemite Valley rock avalanches; rather, it lays a foundation that will allow future researchers to study the internal structure of rock avalanches in greater detail. There are additional case studies of near-surface geophysical methods applied in mass wasting studies that are listed in the literature review section. The 9 9 methods section contains greater detail on the near-surface geophysical techniques employed in our survey. LITERATURE REVIEW

In the past few decades, applications of near-surface geophysical methods have become popular for imaging the shallow subsurface. These methods are non- invasive and have made it feasible to conduct surveys to investigate features in the shallow subterranean area. These methods play a significant role in applications such as the study of landslides (Bichler et al., 2004; Göktürkler et al., 2008; Popescu et al., 2016) and rock avalanches (Otto and Sass, 2005; Sass, 2006;

Rinaldini et al., 2008; Socco et al., 2010; Brody et al., 2015), aquifer characterization (Doetsch et al., 2012), geomorphology (Schrott et al., 2008), weathering (Leucci, 2007), bedrock mapping (Chambers et al., 2012), and various other areas of interest. Each technique is sensitive to contrasts in certain physical properties which can reflect different characteristics within the subsurface. Such physical properties include density, the speed of sound, electrical resistivity, etc. Here are several case studies described wherein the researchers applied different types of near-surface geophysical techniques to investigate their proposed assumptions in their field surveys. Göktürkler et al. (2008) performed a two-dimensional ERT survey to reveal the internal structure of a landslide situated between two normal faults, ascertain the physical properties inside the subsurface material, and locate the failure surface geometry where the slope became unstable and began to slide. Bichler et al. (2004) used GPR, direct current (DC) resistivity, and seismic refraction and reflection surveys to image a rupture surface within a landslide to determine the location of the slope failure. Brody et al. (2015) employed two-dimensional ERT, GPR, and SR to delineate the interface of bedrock to avalanche deposits. Doetsch et al. (2012) applied ERT and GPR to characterize an aquifer on a gravel bar within a restored section of a river channel. 11 11

Chambers et al. (2012) used three-dimensional ERT to detect the bedrock surface below high heterogeneous river terrace deposits. Leucci (2007) used ERT, SR, and GPR to understand the characteristics of physical and mechanical weathering of the deposited rock mass caused by the collapse of a cliff in Italy. Otto and Sass (2005) applied GPR, SR, and ERT on two landform complexes in a high-alpine valley in Switzerland to investigate their internal structure to find the overall sediment thickness, and the interface between the talus deposits and rock glacier. Their research was to understand the paraglacial landform evolution in the valley of the Swiss Alps. Sass (2006) applied GPR, SR, and ERT to alpine talus deposits on the Lechtaler Alps in Austria. Sass (2006) wanted to investigate the inner structure of alpine sediment bodies, and estimate the thickness and volume of debris accumulated on the slopes. Popescu et al. (2016) employed ERT to investigate the internal structure, delineate sliding surfaces, and ascertain the slope stability of two landslides. Socco et al. (2010) used ERT and SR to image the volume of the deposits of two rock avalanches that occurred in Sandalp Valley in Switzerland in 1996. Within these case studies, the researchers encountered problems during data collection which negatively impacted the results. Leucci (2007) applied GPR to detect fractures within the subsurface of a rock mass (debris) deposit that was caused by a collapse of a mountain cliff. Unfortunately, the rock mass contained many heterogeneities which lead the researchers to interpret the debris as a fracture erroneously. Doetsch et al. (2012) used GPR to map the thickness of an aquifer and two subsurface regions with different depositional patterns. However, the wet clay deposits containing high water content strongly attenuated the GPR signals. The high water content made the authors unable to see past the clay layers, which impede the determination of the exact locations of the boundaries within 12 12 each of the region. Brody et al. (2015) applied SR to locate the interface of bedrock to avalanche deposits. The researchers found it difficult to locate a place for their seismic shot device from where a wave would strike the interface at the critical angle and refract along it. Seismic waves are refracted when a wave approaches the refracting boundary at the critical angle such that energy is refracted along the boundary instead of through it. Brody et al. (2015) also had difficulty getting good contact between their ERT electrodes and the ground, reducing data quality. The researchers had to wet the electrodes with salt water to mitigate the high contact resistance in the survey area. Otto and Sass (2005) had also encountered poor electrode connection that resulted in wetting salt water between the electrodes and rocks. Within their results, they were able to detect the bedrock on the first landform complex, but not on the second. Sass (2006) had problems with the ERT method and was unable to detect the bedrock surface when compared to the other methods for interpretation. Popescu et al. (2016) also encountered poor electrode connection when using ERT, resulting in noisy data, and affecting their data interpretation. Some of these case studies used two or more geophysical techniques combined to overcome limitations of individual methods. Doetsch et al. (2012) describe three approaches of integrating different geophysical data or models for interpretation. These approaches are i) Joint interpretation: methods are processed individually and then interpreted together, ii) Joint inversion: all methods are combined in a computer tomography, which fits all data of the different methods at the same time, and iii) Constrained inversion: information from one method is utilized to constrain the inversion of another method (Doetsch et al., 2012). These approaches are further elaborated in the methods section. Brody et al. (2015) 13 13 applied the "Joint interpretation" in their investigation whereas we apply the “Constrained inversion” approach in our research. Doetsch et al. (2012) applied the “Constrained inversion” approach and combined ERT and GPR to image the subsurface interface of two aquifer regions. They interpreted and suggested that the upper aquifer region is comprised of sediments that were rearranged by the river before the restoration, and the lower aquifer region to be highly conductive with possible high clay content. Bichler et al. (2004) applied the “Joint interpretation” and “Joint inversion” approach. They integrated GPR, direct current resistivity, and SR into a 3-dimensional model of an unstable landslide slope to map the surface of the rupture and separation. All of the case studies summarized above which employed geophysical methods demonstrated effective ways of interpreting and combining data from a selected area of interest. These case studies also discussed problems encountered in the field, and some detailed how they resolved these problems while some were unable to overcome them. Nevertheless, knowing about the potential problems beforehand allows us to plan for them. The information gained from these case studies informs us of what can be expected in our research. Our present study aims to apply the ERT and GPR near- surface geophysical techniques on the El Capitan Meadow rock avalanche deposit field area to investigate the internal subsurface topography and depth. Both the ERT and GPR methods were chosen because ERT can image strong resistivity contrast between the rock avalanche deposit and paleo-valley floor, whereas the GPR can image subsurface interface between the rock avalanche deposit and paleo-valley floor. METHODOLOGY

This project was undertaken to investigate the internal structure of the El Capitan Meadow rock avalanche, focusing on the interface to the underlying valley floor, using near-surface geophysical techniques. When selecting which geophysical technique to use for our investigation, different types of geophysical methods are best suited when they can efficiently image subsurface structures by exploiting differences in the physical properties of materials, such as seismic velocity, radar wave velocity, density, and electrical resistivity. Our first task, then, was to study the physical properties of the materials at the site. From Stock and Uhrhammer’s (2010) research, we know that the cliff face of El Capitan consists primarily of granite, and we expect that the avalanche deposit should be of the same material, yet porous, with a mixture of debris and air trapped in the combination. The exposed valley floor adjacent to the avalanche deposit is primarily composed of soils, sand, and clay, and so the valley floor under the deposit is expected to be similarly composed. Based on the local geology and the investigation of a different rock avalanche in Yosemite Valley, in which geophysical methods were applied by Brody et al. (2015), these are the assumed physical properties (Table 1). Here we examine a variety of near-surface geophysical methods that might suit the physical properties of our expected subsurface.

Gravity Mapping Variations in subsurface density are measured by their effect on local gravity. Gravity mapping is limited by ambiguity in Earth materials within the subsurface— many different materials have similar density. Without additional information about the subsurface, this method cannot be used to differentiate between areas of low density at shallow depths and areas of high density at deeper depths. 15 15

Table 1. Near Surface Geophysical Methods Versus Units Unit Electrical resistivity Seismic velocity GPR velocity Granite 4500-6000 m/s 0.13 m/ns (Literature ~103-105 Ω•m

values) Rock avalanche (Brody et al., ≥104 Ω•m 344-1344 m/s 0.14 m/ns 2015) Silt/Clay 1-102 Ω•m 400-1200 m/s 0.06-0.07 m/ns (Literature

values) Valley floor (Brody et al., ~103 Ω•m 1500-2400 m/s 0.08-0.1 m/ns 2015) Note: Literature values for wave velocities and resistivity were obtained from the website Geophysics for Practicing Geoscientists (Oldenburg et al., 2017). Other property information was obtained from Brody et al. (2015). The literature values of rock avalanche contains non-porous granite, whereas the values obtained from Brody et al., 2015 was porous because the rock avalanche had flow and deposited to a different location while not intact. (Brody et al. (2015) may have obtained values for GPR velocity from another source).

Seismic Refraction This method requires that the top layer (rock avalanche) have a lower wave velocity than the bottom layer (valley floor) for the seismic waves to critically refract. As the wave refracts along the interface, it sends out secondary waves which are collected by geophones. In our application, the rock avalanche resting on top of the valley floor is composed of granite. Brody et al. (2015) estimated that the p-wave velocity of the avalanche deposit was v= 344-1344 m/s. The velocity of the avalanche deposit is significantly lower than that of the valley floor, with an approximate propagation velocity of v= 1,500-2,400 m/s (Brody et al., 2015). The fact that the velocity of the wave increases with depth between the two layers could be due to increasing sediment compaction of the paleo-valley floor or the valley floor velocity may be lower than the rock avalanche velocity. However, the lower velocity 16 16 was not detected by the seismic refraction method, which might lead to errors in depth. In the geological setting at El Capitan, the rock avalanche deposit is composed of granite, which has a higher propagation velocity (v=4500-6000 m/s) than the Silt/Clay velocity (v=400-1200 m/s) of the paleo-valley floor. The wave will therefore refract downward at a steeper than incident angle, and is lost in the subsurface, unable to critically refract. As we cannot meet the requirement of critically refracting the waves, this method cannot be successful in such a setting.

Seismic Reflection Though similar to the ground penetrating radar method, these two methods are different in the types of waves used, and in the efficiency of wave coupling, propagation, reflection, and wavelength (Hildebrand et al., 2002). Performing the Seismic Reflection method would be time-consuming in both the field survey and the data processing. Furthermore, we would not expect to obtain better information from Seismic Reflection than from GPR because the seismic wavelengths for our specific site would be greater than the radar wavelengths, giving it a comparatively lower resolution. In this survey, GPR (100 and 50 MHz) has a wavelength of about 1- 2m (휆 = 푣) whereas Seismic Reflection has a wavelength of 11-24m (휆 = 푣) if a seismic 푓 푓 hammer (~120 Hz) was employed. Finding the methods listed above unsuitable for this survey, we chose to employ the ERT and GPR methods to investigate the internal structure of the El Capitan Meadow Rock Avalanche, as the materials at the site have sufficiently different resistivities (measured by ERT) and radar wave propagation velocities (measured by GPR) that these methods are well suited to study the interface between them. 17 17 ERT This is a non-invasive method used to measure electrical resistivity of Earth materials between sets of current electrodes and potential electrodes (Loke et al., 2013). ERT is used to distinguish between materials of different resistivities to show the resulting spatial variation of electrical resistivities underground. The ERT system consists of steel electrode stakes which are inserted into the ground at spaced intervals and connected to a cable that is hooked up to a control unit (Advanced Geoscience, Inc. Supersting R1 resistivity IP/SP system). During the survey, current is injected into the ground, and the receivers measure the voltage differences. If the material within the subsurface allows stronger current flow, then that material has low resistivity. If the material within the subsurface has weak current flowing through the material, then that material has high resistivity. After a survey is conducted, a data quality control is performed. The freely available software program, “Direct Current 2D Inversion and Resolution” (DC2DInvRes, http://www.resistivity.net/dc2dinvres/), is used. DC2DInvRes is a full inversion program for resistivity data that displays the raw data in a pseudosection (see Figure 2). The raw data are displayed as individual measurements and which configuration type is used. Each measurement involves two current electrodes, from which electrical current is injected, and two potential-field electrodes. Each measurement can be displayed as a crude estimation for the average subsurface resistivity at one location and depth, called the “apparent resistivity.” The apparent resistivity assumes that the subsurface has a homogeneous resistivity. A pseudosection, displaying the apparent resistivities at their center depth and location can be good for data representation and identification of data outliers. However, the user should not use pseudosections to interpret the true depth of the subsurface nor rely on interpreting shapes in areas of resistivity which may be distorted.

18 18

Figure 2. Representation of a pseudosection displaying raw data.

The DC2DInvRes program depicts the scale bar at the bottom showing a color scheme, ranging from minimum apparent resistivity on the left (dark blue) to maximum apparent resistivity on the right (dark red) in ohm-meters (see Figure 2). Once the user locates and deletes the data outliers, the remaining data are then inverted with the Boundless Electrical Resistivity (BERT) software package provided by Gunther et al. (2006) and Rucker et al. (2006). BERT generates a resistivity model from electrical resistivity data depicting resistivity (Ω•m) on a colorbar at the bottom of the model, with the along-profile position (m) on the x-axis and the elevation (m) on the y-axis. To obtain a model of the resistivities in the subsurface, the ERT method relies on a mathematical optimization scheme called an inversion. Figure 3 shows an inversion flowchart for an electrical resistivity tomography algorithm. In order to initiate the inversion process, the subsurface was split up into individual cells, each cell having a constant electrical resistivity data value, which is called discretization (see Figure 4). The shape of the cell can be quadrangular or triangular. 19 19

Figure 3. ERT inversion flowchart Note: The gray boxes specify user input either recorded in the field (solid boundary) or selected for the inversion (dashed boundary). Blue indicates algorithm controlled entities. Electrode information comprises electrode position and configuration. The algorithm iterates through the blue cycle until either the model explains the measured data within estimated errors, or the model update is insignificantly small. (Robbins et al., 2017)

Figure 4. 2-D discretization of parameter mesh (Gunther et al., 2006)

The data measured in the field is then fed into the inversion process. The user chooses a “Starting Model,” typically the same resistivity value for each cell given by the average apparent resistivity. The inversion program calculates how the data would look if the starting model were the actual subsurface resistivity. From the difference between the simulated data and the data collected in the field, the inversion program then calculates a better-suited resistivity model. In the next step, the inversion program uses the newly obtained resistivity model as a starting model, then again simulates the data and once more calculates an improved resistivity model from the difference between the simulated data and data from the field. This process 20 20 is repeated until either the resistivity model improvements are negligible or the simulated data matches the field data. However, calculating an updated model from a starting model and the field data is commonly “underdetermined,” meaning that there are more unknowns than equations. Therefore, the resistivity model resulting from an inversion is non-unique, meaning that many different subsurface models can explain the same data. To mitigate the “underdeterminism,” the user needs to provide additional constraints in the form of regularization. The two most popular types of regularization are damping and smoothing. Damping prevents the results of the updated model from being too different from the previous model. Smoothing forces neighboring cells to have similar values. The result depends on the regularization. Hence, it is important for the user to choose a reasonable regularization scheme (see Figure 5.a-c as an example for different resistivity models that fit the same data equally well). Inversion results (models) are usually shown as a cross-sectional profile of the subsurface (see Figure 5.a as an example).

Figure 5. Applied regularization “smoothing” on three resistivity models (a, b, c) from the observed data (d, e, f). (Obtained from Oldenburg et al., 1999) Note: a. “smoothing” is applied in all direction; b. “smoothing” is applied in the horizontal direction; c. “smoothing” is applied in the vertical direction; d-f. Observed data shown as a pseudosection 21 21 GPR This is a non-invasive method utilized to delineate the interfaces of subsurface Earth materials (Burger et al., 2006). GPR detects subsurface interfaces at places where a radar wave is reflected when materials with different electromagnetic properties meet. GPR consists of a transmitter and a receiver antenna. At each measurement location, the transmitter antenna injects electromagnetic pulses (radar waves) into the ground. When a reflector, such as a subsurface interface, scatters a wave back to the surface, the receiver antenna records the backscatter of the radar wave, and stores the recorded wave (see Figure 6). A single recorded wave is called a “trace.”

Figure 6. Diagram of how common-offset GPR works to obtain a profile radargram. Note: Image obtained from Jol and Smith (1991) depicting the step-like procedure of both the transmitter and receiver antenna at a constant spacing.

The trace is a single measurement which consists of a wave recorded over time. A single trace, at first displayed in the form of a wiggly line, is turned by the 22 22 software into a column of white-black color pixels. The white-black color pixels represent the positive and negative numeric values of the wave lobe within the subsurface; white is a positive value and black is a negative value. A gray pixel represents the value of zero. In the common-offset profiling approach, the user continues making measurements by moving the antennae at fixed intervals along the line, and the display shows the recorded traces beside each other. After taking measurements at several locations, the traces have built up a radargram, which appears as columns of white-black colored pixels. The radargram displays the position [meters] versus the recording time (two-way travel time) [nanoseconds] with all the recorded columns of white-black color pixels (see example in Figure 7).

Figure 7. Raw radargram with white-black color pixels using the gray colormap. 23 23

Interpreting radargrams can be challenging because they can contain a wave that travels directly from the transmitter to the receiver (airwave) and waves that scatter from objects above the surface. A velocity model of the subsurface is needed to turn “Two-way travel time [ns]” into depth and therefore interpret the GPR data and create a radargram with correct topography. A walkaway using the Wide Aperture Reflection and Refraction (WARR) method is performed to obtain a radar wave velocity at the El Capitan Meadow rock avalanche. The walkaway WARR procedure is performed by keeping the receiver antenna fixed at one position and moving the transmitter antenna away from the source-receiver antenna while taking measurements, hence increasing the offset between the transmitter and receiver antennae. The walkaway procedure can also be vice-versa, such as the moving of the receiver antenna away from the transmitter antenna while taking measurements. The walkaway WARR procedure is depicted in Figure 8. In the method section of GPR- Radar wave velocity determination, I will explain how the radar wave velocity is obtained from the WARR data. All GPR data processing and visualization in this thesis is done using the open-source Matlab based software GPR-O (https://github.com/ NSGeophysics/GPR-O/wiki).

Figure 8. Walkaway WARR procedure (modified from Morozov lecture, 2018) Note: Rx is the receiver antenna. Tx is the transmitter antenna. “d” is the depth of the subsurface. denote (׀׀׀, ׀׀, ׀) V1 and V2 are the radar wave velocities of the subsurfaces. The Roman numerals the incremental separation of the receiver antenna from the transmitter antenna. 24 24

Figure 9 shows a raw WARR radargram after the walkaway procedure has been performed.

Figure 9. Representation of a raw WARR radargram.

Survey The area of focus is the base of El Capitan located in Yosemite Valley of Yosemite National Park (Figure 10).

Figure 10. The southeast face of El Capitan, showing the location of the El Capitan Meadow rock avalanche. Note: The white dotted lines indicate the rock avalanche including the younger talus close to the rock face (Stock, 2008). 25 25

When choosing profile lines for ERT and GPR data collection, it is essential to have a plan to deploy the methods optimally, allowing for reliable interpretation while minimizing costs. It is also important to avoid damaging vegetation or the ERT and GPR equipment, properly transport and set up the instruments in the field, and abide by Yosemite National Park rules. Both ERT and GPR were employed along three survey lines covering the El Capitan Meadow rock avalanche (Figure 11) to compare results of the internal structure and use GPR-derived radar wave reflection interfaces to guide the ERT inversion as described in the section “Combining ERT and GPR.”

Figure 11. LiDAR image obtained from Google Earth of the El Capitan Meadow Rock Avalanche depicting three profile lines chosen for the data collection for both ERT and GPR (LiDAR image provided by Stock (2017)).

The survey location contained densely vegetated areas that presented an obstacle to conduct the surveys in a straight line. Because of this, measurements were conducted following a path, clear of vegetation, while attempting to have the 26 26 geophysical equipment set up and carried out in a straight pattern around the vegetation for all three profiles.  Line 1 was chosen because it followed a footpath that begins at the valley floor and traverses the rock avalanche in a South–North direction towards the base of El Capitan.  Line 2 was chosen because it is along another easy footpath that begins at the valley floor and perpendicularly crosses Line 1, traversing the rock avalanche in a West–East direction.  Line 3 was chosen because it covers an area that is not covered by lines 1 & 2. This line does not follow a footpath. However, it traverses the rock avalanche in an area with less vegetation. Line 3 is roughly parallel to Line 1 and intersects Line 2. The following sections detail the data collection methods and our implementation. The subsequent section deals with the processing of this data and our interpretations.

Field Deployment

Differential GPS Measurement Positioning Differential Global Positioning System (DGPS) coordinates in 3- Dimensions were collected at regular intervals along each transect to obtain accurate location information for GPR and ERT profiles. DGPS uses fixed public GPS stations to optimize the GPS locations recorded on site. In the ERT survey, DGPS was collected at each electrode position. In the GPR survey, DGPS was collected at every 3 m for Lines 1 and 3, while for Line 2 27 27 it was taken at every 6 m until around 439 m when the interval was increased to 12 m. Once obtained, the DGPS measurements were examined on Google Earth to assess the accuracy of each data point because raw data can contain errors due to vegetation, close-by mountains obstructing satellite line-of-sight, or simply a low number of visible satellites. Under ideal conditions, the DGPS system has a horizontal accuracy of 1 cm. A small number of our DGPS points were off by a meter. The surficial data points were carefully examined while disregarding the elevation data because differential GPS typically has higher Northing and Easting accuracy than it has elevation accuracy. In each survey, there were a few clearly incorrect surficial measurements which were later manually adjusted to the assumed corrected GPS positions on Google Earth. The corrected, adjusted DGPS data points were manually updated, and the unreliable elevation data was replaced with interpolated topography data (freely available high-resolution LiDAR data for this region was obtained from the website (http://opentopo.sdsc.edu/datasets)) to ensure accurate elevation information for each DGPS point of each transect. In the simplest 2-Dimensional profile form, ERT and GPR investigations both require that the data be collected along linear profiles. As explained above, because of dense vegetation it was not possible to conduct the profiles in straight lines. The 3D profile coordinates are turned into 2D profiles (distance, elevation) based on the incremental distances between the electrodes, transforming the irregular lines into linear profiles. 28 28 Electrical Resistivity Tomography (ERT) The ERT method is particularly suitable for our survey because the ERT technique is sensitive to a physical property for which the overlying rock avalanche presents a strong contrast to the underlying valley floor. The upper subsurface layer (i.e., rock avalanche) is composed of accumulated, jumbled up granite mixed with other sediments and air (Total resistivity ≥ 104 Ω•m). Below the rock avalanche deposits, the valley floor is mostly composed of finer-grained material (soil resistivity = 1-103 Ω•m), and possibly compacted clay, and has a lower resistivity. Other factors such as electrode spacing, and electrode array configuration type contribute to finding the target depth. Commonly, if the electrodes are spaced closer together, the resolution is better in the shallow subsurface. Conversely, if the electrode spacing is farther apart, the investigation depth is deeper, but the resolution is lower. For the field layout strategy, we have 28 electrode stakes, and we chose our spacing intervals to be 6 m apart. We estimated the depth of optimal resolution, which is commonly around 1/8 to 1/10 of the profile length (28 electrodes spaced by 6 m), to be 16-20 m below the surface. The configuration type (Figure 12) determines the signal-to-noise ratio and suitability to resolve horizontal layers or lateral variations. In each configuration type, each measurement involves four electrodes: two current injection electrodes and two potential electrodes. The ERT system altogether has 28 electrodes connected. For each measurement, the ERT system picks two injection electrodes to inject the current into the ground, and the electrical potential difference (Voltage) is then measured between another pair of electrodes based on the configuration type settings (Loke et al., 2013). This 29 29 process is repeated with other pairs of injection and potential electrodes as set by the selected configuration type.

Figure 12. Arrangement of current (A and B) and potential (C and D) electrodes for Dipole-Dipole, Wenner, and Schlumberger configuration arrays (Lowrie, 2007). Note: I is current. V is voltage. “a” is the distance between the electrodes. L is the distance between the midpoint of “a” in the Dipole-Dipole array, and in the Schlumberger array is the distance between the current electrodes. (Lowrie, 2007)

Figure 12 depicts the three most common array configuration types. In the Dipole-Dipole configuration, both potential electrodes are on the same side of the injection electrodes along the profile. In the Wenner configuration, the potential electrode pair is placed in the center, and one current electrode is placed on each side, with each electrode the same distance away from its neighbor. The Schlumberger configuration is similar to the Wenner configuration, except that in 30 30 the Schlumberger configuration, the distance between potential electrodes does not vary between the individual measurements. In this study, the Schlumberger and Dipole-Dipole configurations (see Figure 12) were utilized to achieve better vertical resolution, greater depth investigation, and generally high signal-to-noise ratio (Stummer et al., 2004). The Dipole-Dipole Array provided sufficient lateral resolution for valley floor detection at shallow subsurface depths (Stummer et al., 2004). Considering that the El Capitan Meadow rock avalanche covers a large area, a roll-along survey was employed to extend the length of the profiles without reducing resolution (determined by electrode spacing) and investigation depth (determined by maximum distance between active electrodes). In a roll-along strategy, after collecting a data set along a profile, the first segment of the two- segment cable is moved to the end of the second segment, and data collection is continued. This process is repeated until the desired profile length is reached.

ERT Survey Measurements were performed on Veteran’s Day weekend (Friday to Sunday, November 11-13, 2016). DGPS measurements were taken along the profile at every electrode position (Figure 13). ERT Line 1 used 28 individual electrodes with two roll-along sequences with an overlap of 14 electrodes for a total of 56 electrode positions (total distance of 345 m) in the direction from South to North (Figure 14.a). The first electrode was placed near the parking lot South of El Capitan meadow and subsequent electrodes were placed in a line moving north towards the talus of El Capitan. Five raw GPS data points fell outside the profile because of the weak GPS signal and were later corrected. 31 31

Figure 13. Raw and manually corrected DGPS measurements obtained along each profile of every electrode position for ERT. Note: Black dots representing the raw data point and magenta asterisks representing the manually corrected data point that were both plotted on Matlab. Overlapping of Line 2P on Line 2 is seen.

ERT Line 2 had a total of 84 electrode positions (total distance of 400 m) in the direction from West to East. The data for this line was collected during a two- day survey; Line 2 on the first day, with a single set of 28 electrodes, and Line 2P on the second day. For Line 2P the 14 first electrodes are overlapped from the previous day and continued from there with two roll-alongs for a total of 56 electrode positions. The 28 positions from day 1 and the two roll-alongs from day 2 add up to 84 total electrode positions for the line, with a total distance of 400 m. The overlap of 14 electrodes on days 1 and 2 is shown in Figure 14.b. The electrodes were laid out along a path. Twelve raw GPS data points fell outside the profile because of weak GPS signal and were later corrected. 32 32

Figure 14. Profile distance along the elevation for each ERT transect. Note: a. Line 1 elevation ranging from 1206 to 1228 m. The zero begins at the southern-most point along profile 1 in Figure 13; b. Line 2+2P elevation ranging from 1210 to 1218 m. Line 2P overlaps Line 2 at 80 m. The zero begins at the western-most point along profile 2 in Figure 13; c. Line 3 elevation ranging from 1216 to 1220 m. The zero begins at the northern-most point connecting with profile 2 along in Figure 13.

ERT Line 3 had a total of 28 electrode positions in the direction from North to South for a total distance of 170 m, with no roll along. The electrodes were placed beginning near the parking lot area in a Northward track towards the Eastern side intersected with Line 2 (Figure 14.c). Three raw DGPS data points fell outside the profile because of the unreliability of the satellite signal data and were later corrected. The corrected elevation data plotted along with the distance along each profile form nonlinear lines. The 3D profile coordinates (Easting, Northing, elevation) were turned into a 2D profile (profile distance, elevation) by appending incremental electrode separations. 33 33 GPR Survey In this study, the Sensors & Software PulseEKKO Pro system was used, with 50 MHz and 100 MHz antennas. We used the 100 MHz antennae along Line 2, where we were able to utilize the trigger wheel. A trigger wheel is a device that measures distance moved along a profile and triggers a measurement at given spatial intervals. We set the trigger wheel to collect a measurement at every 0.25 m. For the 50 MHz antenna that we used for Lines 1 and 3, we were not able to use the trigger wheel due to the obstructed vegetated field conditions. The path for Line 2 was too narrow to use the wider 50 MHz antenna with the trigger wheel. Measurements were taken during Spring Break 2017 (Monday and Tuesday, April 10-11, 2017). GPR data were collected along the same profile lines as the ERT data. Collection among the same profile lines enables us to compare the two data sets and incorporate them (Figure 15). Line 1 was measured at 0.6 m intervals using a 50 MHz instrument, with a total distance of 323 m in a South-North direction. DGPS data was collected every 3 m (107 total measurements) (Figure 16.a). Five raw DGPS data points fell outside the profile because of weak GPS signal and were later corrected. Line 2 was measured at 0.25 m intervals using a 100 MHz instrument with a trigger wheel for a total distance of 490 m in a West-East direction. DGPS data were collected every 6 m (Figure 16.b). However, we were not able to collect the DGPS data at every 6 m intervals towards the end of Line 2 due to lack of time and weather conditions. The last data point was taken around 30.5 m, and the intervals were changed from 6 m to 12m. One raw DGPS data point fell outside the profile.

34 34

Figure 15. Raw and manually corrected DGPS measurements obtained along each profile for GPR. Note: Black dots represent the raw data points, and magenta asterisks represent the manually corrected data points, all of which were plotted on Matlab.

Line 3 was measured at 0.6 m spaced intervals using a 50 MHz instrument, with a total distance of 140 m in a South-North direction. DGPS data were collected every 3 m over a total distance of 140 m (Figure 16.c). There were no raw points that fell outside the profile. We only used one antenna set per profile. Each radargram shows a profile at position [meters] against two-way travel time in nanoseconds. In order to transform the two-way travel time into depth and create a radargram with correct topography, a velocity model of the subsurface was needed. A walkway was performed using the Wide Aperture Reflection and Refraction (WARR) method, utilizing a 100 MHz instrument with a trigger wheel, as a procedure to obtain a 35 35

Figure 16. Profile distance along the elevation for each GPR transect. Note: a. Line 1 elevation ranging from 1206 to 1228 m. The zero begins at the southern-most point along profile 1 in Figure 15; b. Line 2 elevation ranging from 1208 to 1220 m. The zero begins at the western-most point along profile 2 in Figure 15; c. Line 3 elevation ranging from 1213 to 1220 m. The zero begins at the northern-most point connecting with profile 2 in Figure 15. velocity model. The walkaway WARR procedure is performed by keeping the source-receiver antenna fixed at one position and moving a transmitter antenna away from the source-receiver antenna while taking measurements. In this procedure, the walkaway WARR method was performed at a path beginning at the intersection of Lines 2 and 3 heading East with the initial distance between the transmitter antenna and source-receiver antenna at 0.61 m. As the transmitter antenna moved away from the source-receiver antenna, the final distance was 16.15 m. 36 36 Data Processing

Electrical Resistivity Tomography I performed a data quality control check using a freely available software program, Direct Current 2D Inversion and Resolution (DC2DInvRes), to view the pseudosection of my raw data. In my survey, the top panel displays the data collected using the Schlumberger (s) array and the bottom panel displays data from the dipole-dipole (dd) array (see Figure 17). The x-axis depicts the center of the four electrode configuration, and the y-axis depicts the electrode separation (wider electrode separation typically leads to deeper measurements). Several of these pseudosections contains missing data points (white blank points). Data gaps can happen when current injected into the ground does not lead to a potential difference at the measurement electrodes because of poor electrical coupling of the electrodes with the ground.

Figure 17. Pseudosection of profile Line 1 depicting raw resistivity data for two different configurations.

Figure 17 shows the raw electrical resistivity data of profile Line 1 in a pseudosection. The apparent resistivity ranges from 10 to >31623 Ω•m. Figure 17 37 37 shows a point with extremely low resistivity in the dipole-dipole (bottom) plot near 10 Ω•m. This point is a data outlier and was removed from this data set. The removal of the extremely low resistivity point resulted in a change in the range of the color bar. The minimum of the apparent resistivity was adjusted to a higher minimum in the color scheme. The new apparent resistivity ranges from 100 to >31623 Ω•m. Both panels show high resistivity beyond 31623 Ω•m (Figure 18).

Figure 18. Pseudosection of Line 1 after the extreme data point was deleted. Note: The apparent resistivity was adjusted ranging from 100 to > 31623 Ω•m.

Figure 19 shows the raw electrical resistivity data for Line 2 displayed in a pseudosection. The data for Line 2 contains two data outliers, both with apparent resistivities below 32 Ω•m. After deleting two extreme data points, Figure 20 shows the new apparent resistivity color scheme ranging from 100 to > 31623 Ω•m. Figure 21 shows the raw electrical resistivity data for Line 2P displayed in a pseudosection. The dipole-dipole array (bottom panel) shows very low resistivity data points near 100 Ω•m. The Schlumberger array (top panel) shows very high resistivity data points near 100000 Ω•m associated with one or two electrodes. 38 38

Figure 19. Pseudosection of profile Line 2 depicting the areas of electrical resistivity for two different configurations.

Figure 20. Pseudosection of profile Line 2 after two extreme data points were deleted. 39 39

Figure 21. Pseudosection of profile Line 2P depicting areas of electrical resistivity for two different configurations. Note: Apparent resistivity ranges from 100 to >31623 Ω•m.

The original data of Line 2P had 56 electrodes and 940 measurements (Figure 21). The Schlumberger array (top panel) had 24 outliers, and the dipole- dipole array (bottom panel) had 3 outliers. The data outliers from both configurations of Line 2P were removed and resulted in a total of 913 measurements. Figure 22 shows the resulting pseudosection of Line 2P after the data removal. After removing the outliers, Figure 22 shows five more data outliers in the dipole-dipole array of Line 2P. The five excess data outlier points were removed from the dipole-dipole data-set, and the removal resulted in a change in the color scheme of the color bar to >1000 to >31623 Ω•m. Figure 23 shows the bottom portion of the dipole-dipole array, which now appears less resistive. Figure 24 shows the raw electrical resistivity data for Line 3 displayed in a pseudosection. The apparent resistivity ranges from <1585 to >39811 Ω•m. This pseudosection did not contain any data outliers that needed to be deleted (Figure 24). 40 40

Figure 22. Pseudosection of profile Line 2P after the extreme values were deleted.

Figure 23. Pseudosection of profile Line 2P after the second removal of data outliers.

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Figure 24. Pseudosection of profile Line 3 depicting the areas of resistivity for two configurations.

After examining several data points in the pseudosections in the DC2DInvRes program and removing data outliers from each pseudosection, all of the undeleted data points from each of the pseudosections were turned into a profile of electrical resistivity distribution using the inversion program “Boundless Electrical Resistivity Tomography” (BERT). See Figure 3 (p. 19) for a flowchart of how inversion works. Each inversion model is shown as a cross-sectional profile of the subsurface within the studied site which illustrated areas of high to low resistivity concentrations within the subsurface. To better optimize the visibility of the model, a perceptually uniform color scheme called “Viridis,” was applied to make a human perception of the model more accurate. A perceptually uniform color scheme is designed so that the “difference” between two colors is perceived by humans as being the same as the difference between the two numbers those colors represent. Humans do not perceive differences in color, as measured by differences in the wavelength of light, uniformly. Given two pairs of colors whose differences in wavelength are the same, there is no guarantee, and in fact, it is unlikely, that a human would perceive them as having equal differences. The 42 42

“Rainbow” color scheme, for example, maps data linearly to the wavelength of the visible color spectrum, and so it is particularly perceptually non-uniform. A perceptually uniform color scheme such as “Viridis” is mindful of human color perception, and using such a scheme ensures that a color map is perceived properly (Crameri). Figure 25 shows the two color schemes.

Figure 25. Perceptually uniform color scheme “Viridis” (top) and Rainbow color scheme “Jet” (bottom). Note: The numbers in both color schemes can be adjusted to represent the data or model. The “jet” color scheme can show unequal color transitions in the model. If the data using the “jet” color scheme range from 0 to 10, the viewer would see a small transition. Conversely, if the data using “jet” were from 60 to 70, the viewer would see a large transition. The “Viridis” color scheme can show an equal color transition in the model.

Figure 26 depicts ERT inversion models for Lines 1-3. These are the inversion results using the standard settings for regularization and discretization from the BERT software. The standard settings for the inversion default a very coarse mesh (large size triangles) in all of the profile lines. In Figure 26, we were not able to see a clear delineation of the resistivity contrasts. The white color in the surrounding areas indicates that these cells are not well resolved by the data.

Ground Penetrating Radar-Radar Wave Velocity Determination Once we obtained the GPR data, we needed to know the radar wave velocity in the subsurface to interpret our data and correct for topography along the profile. The GPR system allows us to keep the receiver antenna in a fixed location and move the transmitter antenna independently away from the receiver. As the transmitter antenna moves away, one radar wave travels along the surface 43 43

Figure 26. ERT inversion models generated from inverting data utilizing the BERT software with the standard settings. Note: (a) Line 1. (b) Line 2. (c) Line 3 with the propagation velocity of the subsurface (ground wave) (Plattner, 2017). We can measure the travel time of this wave for several transmitter-receiver offsets, and therefore calculate a radar velocity of the shallow subsurface. In our survey, we kept the receiver antenna fixed, attaching the transmitter antenna to the trigger wheel, and pulling the transmitter antenna along a straight line, recorded a trace every 0.1 m. This practice is the “Wide Aperture Reflection and Refraction” (WARR) approach that produces the WARR model as a radargram (Figure 27) (Plattner, 2017). The radargram displays the profile position [m] versus two-way travel time [ns]. 44 44

Figure 27. Raw, unprocessed WARR radargram.

Figure 27 shows our raw WARR radargram obtained at position 320 m towards the end of profile 2, on top of the rock avalanche. WARR radargrams will always record a ground wave, and a wave traveling through the air from the transmitter to the receiver with velocity of 0.3 m/ns, the air wave. If there is a sharp interface in the subsurface that allows waves to reflect, then the WARR radargram will also record a wave traveling into the subsurface, reflecting off the sharp interface, and traveling back to the receiver. Such a wave allows us to determine the average radar velocity of the subsurface down to the sharp interface. In the WARR radargram, the direct ground wave and airwave show up as straight lines. The reflected wave appears as a hyperbola. The slope of the air wave is 1/

(0.3 m/ns). The slope of the ground wave is 1/Vground, where Vground is the radar velocity of the shallow subsurface. The shape of the hyperbola depends on the average radar velocity between the surface and the sharp interface, and on the 45 45 depth of the sharp interface. Therefore we can obtain the radar velocity of the subsurface from the slope of the ground wave and the shape of the hyperbola. Once the subsurface propagation velocity was acquired, we used it to convert the two-way travel times from the three profile radargrams into depths, which were finally used to create the topography for each profile. Since we know that our subsurface is not solid granite, velocity values obtained from a table or a textbook would most likely be considerably different from those at the site. We used an in- house developed freely available software package GPR-O (https://github.com/ NSGeophysics/GPR-O/wiki) to extract velocity information from the WARR radargram (Figure 27). In the GPR-O software, processing methods, DEWOW filtering, and contrast are used to reduce difficulties in data interpretation and improve the visibility of the radargram. DEWOW is a low-cut filter along each individual trace that can remove large-scale trends (Figure 28). As a result, the vertical bands visible in Figure 27 are removed. When analyzing the WARR radargram, any radar waves arriving later than after 300 ns were hard to see. A gain can be applied to make the details of the wave signals deep in the subsurface more visible. GPR-O has two types of gains:

T-power gain (TPOW). This gain increases the strength by multiplying the later arrival times with large numbers and the early arrival time with small numbers.

Automated Gain Control (AGC). This gain makes the signal strength the same at all two-way travel times. This process is done by defining a time (depth) window and having the energy of the signal be the same within each window.

46 46

Figure 28. Application of the DEWOW filtering. The stripes/bands disappear, and the radar waves appear weaker further in depth. Note: Dewow filtering was applied with window length 1899 samples to Figure 27

The processed WARR data are plotted, with the DEWOW filtering and TPOW gain, to produce a WARR radargram in position [meters] versus two-way travel time [nanoseconds] (Figure 29). Observing the WARR radargram, I noticed several different waves within the subsurface. I used the “GPR-O” software package which contains tools to plot different intercepts and slopes and observe which alignment was the best. One reliable way to find a line that fits the wave on the WARR radargram is to run a linear semblance analysis. In a linear semblance analysis, a line is drawn on top of the WARR radargram and all the radargram pixels along that line are summed up. If the pixels have the same negative or positive values, the sum of either will be 47 47

Figure 29. Processed WARR radargram with DEWOW filtering and TPOW with parameter = 0.73 large. If my chosen line crosses lines of positive and negative values, or through noise, the values would either be small or cancel out. A semblance plot is obtained by calculating the semblance for many intercepts and slopes and plotting them in a figure to see which intercept and slope best fits a line in the WARR radargram. Figure 30 shows the linear semblance result for the WARR radargram of Figure 29. Each pixel in Figure 30 corresponds a line on the radargram with the two-way travel time (intercept) on the y-axis and velocity (inverse slope) on the x-axis. The blue colors in Figure 30 represent low semblances indicating that the linear arrival times will not fit any lines on the WARR radargram plot. The light yellow pixels indicate velocities/two-way travel times, for which the radargram 48 48

Figure 30. Semblance analysis results for WARR radargram shown in Figure 29. contains lines. In Figure 30, we see two light yellow scores. The light yellow score near the top on the right of the plot is approximately 0 ns and velocity 0.30 m/ns. The light yellow score closer to the left of the plot is approximately 7 ns at velocity 0.10 m/ns. In Figure 31, the corresponding lines for these light yellow semblances were plotted on top the WARR radargram and perfectly overlap the first two dark lines. These two highest semblance scores were interpreted as the air wave and the ground wave. The speed of radar waves in air is approximately 0.30 m/ns, which confirms that our system and computer program correctly identified the airwave. The airwave is the fastest wave and travels from the source antenna to the receiver antenna without going through the ground. The other linear wave is the direct ground wave below the surface (Figure 31). 49 49

Figure 31. The WARR radargram with airwave and ground wave. Note: The airwave (vo =0.30 m/ns) and ground wave (v1=0.10 m/ns) which is very close to the surface but still within the subsurface.

Figure 31 shows additional wave arrivals that do not have a linear shape. Arrivals of waves that do not travel directly from the transmitter antenna to the receiver antenna but get reflected off a horizontal interface follow have a hyperbolic shape for which the curvature depends on the velocity and the apex depends on the depth of the reflector. To find the average velocity of the material above the reflector, I run a similar semblance analysis as explained above but with hyperbolae instead of straight lines. Figure 32 shows the results of the hyperbola semblance analysis. From the hyperbola semblance analysis, I found three high-scoring hyperbolic arrivals. All three high scores have a velocity of 0.09 m/ns, which is slower than the typical velocity for granite (0.13 m/ns). Figure 33 shows the 50 50

Figure 32. “WARR hyperbola Semblance” plot. Note: This plot depicts a reflected wave velocity in red to be around v=0.09 m/ns. There are three yellow hyperbola scores along the vertical v=0.09 m/ns axis at different two-way travel times. hyperbolae corresponding to the three high-scoring pixels in Figure 32 plotted over the WARR radargram. The shallowest reflector was (0.09 m/ns, 13ns), middle reflector (0.09 m/ns, 92 ns), and deepest reflector (0.09 m/ns, 149 ns). These scores were all along the velocity at 0.09 m/ns and calculated into depth above 10 m of the subsurface (Figure 33). On this plot, the reflected waves can be seen below the ground wave. These reflected waves (v=0.09 m/ns) have a velocity lower than both the air and ground wave velocities that best fit the reflected signals. The top reflected wave with a two way travel time of 13 ns is very close to the ground wave. The equation (퐷 = 푣 ∗ 푡), where D is defined as depth, v is velocity, and t is the two- way travel 2 51 51

Figure 33. WARR figure depicting both the refraction/linear lines and the reflection (hyperbola) curves best fitted over the radar waves arrivals. time allows calculating depth of the reflector from the velocity and two-way travel time. The depth of the deepest reflector was calculated to be 6.7 m. The depth of the middle reflector was 4.14 m and the shallowest depth was 0.585m. This reflection wave velocity (v=0.09 m/ns) was used in constructing the topography for each transect for GPR. From the ground wave (linear), we obtained 0.1 m/ns. From the reflected waves (hyperbolae) we attain 0.09 m/ns. We choose the value of the reflections (hyperbolae) for topography correction of each GPR profile because the ground wave (0.1 m/ns) only travels through the shallowest part of the subsurface and therefore maybe less representative than the reflective wave, which travels all the way through the significant volume of the rock avalanche before it gets reflected. 52 52

Consequently, we found two hyperbolic arrivals that could be reflectors. There are two possible interpretations. The first possibility is that the earlier hyperbolic arrival are waves reflected off the valley floor and the later arriving hyperbola is a double reflection (reflected off the valley floor, then reflected off the surface, back to the valley floor, then to the antenna). The other possibility is that the later hyperbolic arrivals are reflections of the valley floor and the earlier hyperbolic arrivals from reflections of some internal structure. The next step was to process the common offset GPR data collected along profiles 1, 2, and 3. Unlike the WARR data, we moved the antennae together at a common offset, as explained earlier in the section (GPR section). In the following, we used the GPR-O software package for processing the common-offset data.

Ground Penetrating Radar-Data Processing In this section, I discuss the processing of the individual common offset profile data (see Figure 34 as an example). Figure 34 shows raw data for a common offset configuration for profile 1. The x-axis is the distance along the profile, and the y-axis is the two-way travel time. The white-black pixels (gray colormap) are the recorded radar waves. At the top of Figure 34, I discern a white- black-white band at early two-wave travel times, which is an airwave. Typically, if the distance between the transmitter and receiver antennae are constant, the airwave will arrive at exactly at the same time. In our field survey, the transmitter and receiver antennae did not have the exact same separation distance at every measurement location because we were unable to mount the antennae on a fixed frame through the forest path. These offsets of the air wave arrivals due to the uneven antenna separation were a problem when interpreting reflectors in the subsurface because the reflector signals would also be offset. To fix this, we shift 53 53 each trace such that the maxima of the airwaves align. Figure 34 shows the data before and Figure 35 after the alignment. This alignment is called “time zero adjustment.” Line 1-In Figure 34, we see that the airwave dominates the radargram in the form of a horizontal stripe, but there are weaker reflections at later two-way travel times.

Figure 34. Raw GPR radargram of Line 1 depicting a horizontal stripe and weak reflection signals in the subsurface.

Figure 35 shows the alignment of the white-black-white color bands (airwave) appear more straightened and less ridged than Figure 34. This air wave (Figures 34-35) might hinder the discernment of signals underneath. Since the airwave is the same over all traces, the average of all traces is taken and removed from each trace. As a result wave signal in the subsurface is easier to see (Figure 36). 54 54

Figure 35. Raw GPR radargram of Line 1 with the alignment depicting a horizontal stripe.

Figure 36. GPR radargram of Line 1with the airwave removed and increased contrast. 55 55

The TPOW gain was applied to improve visibility to see any materials deeper in the subsurface (Figure 37). The AGC gain was not used because it made the profile of each radargram appear noisy and made it difficult to locate any clear interface reflections or signals, hindering the interpretation.

Figure 37. This GPR radargram of Line 1 depicts the applied TPOW with parameter set to= 1.25, enhancing the strength of the signals with later arrival times.

In Figure 37, we see a flat surface, yet the topography along Line 1 was not flat in the field survey. Since we want to be able to interpret the common-offset radargram for Line 1, we need to correct for topography by vertically shifting each trace such that the time zero adjustment aligns with the surface that is determined by the topography. With the subsurface radar velocity we obtained earlier from the WARR radargram, we transformed the two-way travel time into depth, and therefore shifted each trace by the corresponding topography amount in meters. Figures 38- 39 show the topography-corrected common-offset profile for Line 1. 56 56

Figure 38. Radargram of Line 1 with topographical correction.

Figure 39. Bluewhitered colormap radargram of Line 1 with topographical correction.

We applied the TPOW gain to all radargrams to enhance the subsurface signals. For the following lines, we picked the TPOW gain parameter =1.25 (Figures 37-39) for Line 1, TPOW gain parameter = 0.77 (Figures 41, 42 and 43) for Line 2, and TPOW gain parameter =0.73 (Figures 45, 46, and 47) for Line 3. For each of the radargrams of the profiles, we show the profile with a different colormap (bluewhitered) (Figures 39, 43, and 47). The red color represents the white color positive values, white color represents the gray color zeros, and the blue color represents the black color negative values. Figure 38 depicts the gray colormap radargram. We applied a TPOW gain with parameter =1.25 to this radargram and observed reflections beginning from ~10 m and continuing horizontally along the profile at ~1204m depth. Figure 39 depicts the “bluewhitered” colormap version of the radargram. From Figure 38, we also observe strong reflections beginning from ~10 m and 57 57 continuing horizontally across the position at ~1204 m in depth (Figure 39). The colormap accentuates the reflections with darker colors within the subsurface. We also see that the reflections appear to have interrupted noise in the shape of vertical bands in parts of the radargram, such as at ~255 m and 301 m (Figures 38- 39). Line 2-Figure 40 is an unprocessed radargram, and we observe weak reflection signals in the subsurface and a horizontal airwave stripe at approximately ~10 ns across the entire radargram.

Figure 40. Raw GPR radargram of Line 2 with an observed horizontal stripe

The horizontal stripe was removed, and the TPOW gain= 0.77 was applied to increase visibility to see signals further along the two-way travel time within the subsurface (Figure 41). The radargram with the obtained wave velocity (from WARR) was utilized to plot the elevation topography for this profile. We discern weak signals in the subsurface (Figure 42). 58 58

Figure 41. This processed GPR radargram of Line 2 depicts the applied TPOW gain strengthening the signals further in depth.

Figure 42. Radargram of Line 2 with topographical correction.

Figure 43. Bluewhitered colormap radargram of Line 2 with topographical correction. 59 59

The Bluewhitered colormap was applied, and the appearance was slightly enhanced. The reflections appear weak and uneven, beginning from ~50 m along profile position and continue across the profile until ~400 m (Figure 43). The reflections also to have interrupted noise in the shape of vertical bands in parts of the radargram, such as at ~185 m and 395 m (Figures 42-43). Line 3-Figure 44 is an unprocessed radargram with an observed horizontal stripe seen at approximately ~10 ns. Signals below the horizontal stripe are visible throughout the radargram.

Figure 44. Raw GPR radargram of Line 3 with an observed horizontal stripe

The horizontal stripe was removed, and the TPOW gain with parameter = 0.73 was applied to enhance the reflection signals (Figure 45). The processed radargram with the obtained velocity (from WARR) was utilized to plot the elevation topography for this profile. We detect nearly horizontal reflections that are visible across the position. These reflections also appear blurry, and the waves look interrupted (Figure 46). 60 60

Figure 45. Processed GPR radargram of Line 3 with the TPOW gain

Figure 46. Radargram of Line 3 with topographical correction.

61 61

Line 3- The Bluewhitered colormap enhanced the appearance of the radargram. We observe reflections that appear closely spaced together and uneven across the position. The reflections also appear to have interrupted noise in the shape of vertical bands in parts of the radargram, such as at ~5 m and ~17 m (Figures 46-47).

Figure 47. Bluewhitered colormap radargram of Line 3 with topographical correction.

Ringing When obtaining radar data, there were many areas not easily accessible with the equipment. In order to obtain good quality data, the GPR instrument should be lying flat on the ground. While traversing Lines 1-3, many of the areas contained boulders and large vegetation that made transporting the GPR instrument and collecting data difficult. The GPR instrument would obtain data while sitting on rocks, boulders, and vegetation, creating a gap between the antenna and the ground. After taking the measurement and producing a trace, because the airwave is the fastest signal in the radargram, ringing (noise) was 62 62 present in the radargram (Figures 48, 49, 50). Ringing is caused by radar waves repeatedly bouncing back and forth between the surface and the bottom of the antenna, and the return signal from a particular interface will not appear sharp in the radargram.

Figure 48. Processed radargram of Line 1 showing three ringing noise indicated by the red arrows.

Figure 49. Processed radargram of Line 2 showing two ringing noise indicated by the red arrows.

63 63

Figure 50. Processed radargram of Line 3 showing five ringing noise indicated in the red arrows.

Selecting Interfaces in GPR Upon examination, the GPR radargrams revealed numerous reflections which could be interpreted as the interface between the rock avalanche and the valley floor. Different contrast and gain settings can influence the visibility of the reflectors. Several different gain values for both TPOW and AGC were applied and adjusted to examine where the reflections were most consistent in all of the radargrams for each of the profile lines. The TPOW gain was selected to highlight the reflections for all the displayed plots instead of the AGC gain, because the AGC gain made the radargram appear very noisy, making the plots impossible for us to interpret. The gray colormap radargram was also difficult to interpret, as far as discerning the location of the continuous reflections across the subsurface profile. The application of the “bluewhitered” colormap aided the observation of these continuous reflections by accentuating the reflections in the radargram between the positive and negative values across the profile. 64 64

After applying the TPOW gain and the “bluewhitered” colormap, the radargrams for each profile were examined to find consistent reflections. Figure 51 shows an example of a reflection that is continuous across the radargram, and might be an interface. The interfaces were then carefully selected using the Matlab function ginput. The ginput function allows the user to pick points from the figure window, which can later be exported as x, z positions (along-profile position, elevation).

Figure 51. Close-up of a picked interface with a continuous reflection. Note. Red arrow indicates a continuous blue reflection across the radargram and I selected it as an example for an interface.

In GPR Lines 1- 3, several data points were selected for each interface using the ginput function based on a position versus elevation plot. A colormap “bluewhitered” was implemented to enhance the reflection in the subsurface (Figures 52, 53, 54). 65 65 Line 1 with Picked Interfaces

Figure 52. The picked interfaces of the GPR Line 1 radargram. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

The topography of each interface appeared relatively horizontal across the profile. The combined interfaces looked closely spaced together at profile position ~10 m and slightly separated from the profile position beyond ~75 m.

66 66 Line 2 with Picked Interfaces

Figure 53. The picked interfaces of the GPR Line 2 radargram. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

The topography of each interface appeared roughly horizontal across the profile. The lower interface shows a slightly rugged topography around the middle of the profile. The combined interfaces looked roughly parallel to each other. The reflections appear weak, but the upper interface highlights a slightly darker reflection than the lower interface within the subsurface.

67 67 Line 3 with Picked Interfaces

Figure 54. The picked interfaces of the GPR Line 3 radargram. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

The topography of each interface appeared easily visible and relatively horizontal across the profile. The lower interface shows a slightly rugged topography around the middle of the profile. The combined interfaces looked roughly parallel to each other. Line 3 had more traces exhibiting ringing than Lines 1 and 2, as the path took was more heavily forested and contained dense vegetation and numerous 68 68 boulders. Line 1 was also along a forested path, but this path was less obstructed than Line 3, which resulted in less ringing than Line 3, yet more than Line 2. Line 2, for which a trigger wheel was utilized, was taken along a flat and broad path with no vegetation or boulder obstruction, and its trace exhibited less ringing than Lines 1 and 3.

Combining ERT and GPR Neither ERT nor GPR can alone allow us to precisely locate the subsurface interface between the rock avalanche and the paleo-valley floor. ERT inversion models show smooth transitions of varying resistivity, so it was impossible to identify the precise location of any subsurface object accurately. GPR radargrams identified numerous reflectors. However, it was difficult to determine which reflector was the interface between the rock avalanche deposit and the valley floor. As it was impossible to precisely locate the interface with either method by itself, we sought to combine them. Combining more than one method has been performed in many subsurface studies (Bichler et al., 2004; Doetsch et al., 2012). Doetsch et al. (2012) researched some studies that integrated more than one method and listed three basic principles in regards to combining different methods. The three basic principles of integration are:  Joint interpretation- The methods are processed individually and then interpreted together. This principle is easy to perform, yet least effective, as interpretation of models can be ambiguous when compared to each other or if their resolution properties greatly differ.  Joint inversion- All the methods are combined in a large computer tomography that fits the data from different methods at the same time. Different geophysical measurements are sensitive to different 69 69

properties of the subsurface, and are used to produce a single model that fits all the measurements within a predefined tolerance. The assumption is that the underlying models from the methods will have a common structure, such as if the changes occur at the same physical locations. This principle is difficult to perform, yet more effective because the results fit all given data.  Constrained inversion- Information from one method is used to constrain the inversion of another method (see example below). This principle is easier than joint inversion and more effective than joint interpretation. From the three principles in combining the ERT and GPR methods, the “Joint Interpretation” approach was first considered. However, we were unable to interpret the results of both methods independently to accurately detect the separation between the rock avalanche deposits and paleo-valley floor. The “Joint Inversion” approach was not considered because the current GPR tool we possess did not contain a full waveform inversion that could fit along with the ERT data at the same time to integrate for interpretation. In the last of the three principles of integration, the “Constrained Inversion” approach was chosen. Within this approach, the ERT and GPR methods were combined by first selecting consistent interfaces in the processed GPR, common- offset radargrams (see “Selecting interfaces in GPR” section) and then these interfaces were incorporated into the electrical resistivity inversion allowing sharp transitions of resistivity contrasts across the interface in an otherwise smoothed model. Since the sharp interfaces were allowed but not enforced in the electrical resistivity inversion, the resulting resistivity model may not show a sharp transition at the provided interface; indicating that the provided interface was not 70 70 the bottom of the rock avalanche. By analyzing the inversion models with the combined ERT and GPR survey, the transition from rock avalanche deposits to valley floor was determined, along with the shape of the topographic interface. With this, it could be concluded whether the interface between the valley floor and rock avalanche deposit was uneven or flat. RESULTS

ERT Inversion After various attempts in the BERT software were made to modify and generate an inversion model of each profile, the finalized inversion model of each profile was produced to present a clear delineation between the two subsurface Earth materials. Line 1-A layer of high resistivity (> 40,000 Ω•m) is seen on top of a low- resistive background. The interface between the two appears relatively horizontal and does not follow the topography. The inversion along the topography shows a large area of resistivity beginning after 50 m and continuing past 300 m up a moderate incline at 1200-1220 m depth. From 0 m to approximately 270 m along position, below ~1190 m depth an area of low resistivity can be seen. Towards the edge beyond 350 m, the data coverage is not good. Delineating the exact depth of transition is impossible because the resistivity values range from 5,000 Ω•m to 50,000 Ω•m over an elevation change of 10 m (Figure 55).

Figure 55. The finalized ERT inversion of Line 1.

Line 2-A layer of high resistivity is seen on top of a low-resistive background, similarly to Line 1. The interface between the two appears uneven in the beginning until near 200 m, where the interface continues to appear relatively horizontal and does not follow topography. The inversion shows a large area of 72 72 resistivity beginning around 40 m and continuing approximately 400 m at ~1190- 1220 m depth. Roughly below 1190-1200 m at depth, low resistivity continues with depth. Towards the edge beyond 400 m, the data coverage is not good. Delineating the exact depth of transition is impossible because the resistivity values range from 5,000Ω•m to 50,000Ω•m over an elevation change of 10 m (Figure 56).

Figure 56. The finalized ERT inversion of Line 2.

Line 3- A layer of moderate resistivity is seen on top of a low-resistive background. The interface between the two appears uneven in an undulating pattern. Delineating the exact depth of transition is impossible because the resistivity values transition from 5000 Ω•m to 50,000 Ω•m over an elevation change of 10 m. The inversion shows that the resistive zone is approximately above 1200 m elevation between profile position 0 m to roughly 150 m. Roughly below 1190 -1200 m at depth is an area of low resistivity (Figure 57).

Figure 57. The finalized ERT inversion of Line 3. 73 73

Overall the ERT method in each of the profiles showed strong resistivity transitions where the valley floor is expected to be seen. The topography between the rock avalanche deposits overlying the valley floor appeared relatively horizontal for all profiles. Sharp transitions were not visible. For profile Line 1 and 2, the inversion showed a smooth transition from high to low resistivity material; while for profile line 3, the inversion showed a smooth transition from moderate to low resistivity material. Without a-priori information for all three profiles, ERT inversions cannot detect sharp transitions because the method is based on potential fields, which have a diffusive moveout. The expected sharp interface becomes smoothed leaving us unable to detect its location precisely.

Ground Penetrating Radar The GPR method was suitable in the field for displaying consistent reflections and providing sufficient resolution in the subsurfaces for each of the profiles. Based on the radargrams, the 50 MHz antennae produced more visible outputs than the 100 MHz antennae because the signals exhibit less attenuation and therefore stronger signals of the radarwave reflections deeper within the subsurface of each profile. The 100 MHz antennae had higher resolution than the 50 MHz, yet more attenuation, resulting in weaker signals. In each radargram, we increased the figure contrast to improve the visibility of the plot, and we applied the TPOW gain to enhance the subsurface signals (Line 1: parameter =1.25, Line 2: parameter =0.77, and Line 3: parameter =0.73).

Plotting Each Profile as Position Versus Two Way Travel Time

Line 1. In the gray colormap scheme (Figure 58.a) and bluewhitered colormap scheme (Figure 58.b) radargrams, we noticed more visibility of 74 74 reflections beginning from ~10 m and continuing across the position at ~1204 m depth in both profiles. Figure 58.b shows the colormap accentuating the rugged reflections with darker colors within the subsurface.

Figure 58. Finalized radargram of Line 1 with topographical correction. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram

Line 2. In both the gray colormap scheme (59.a) and bluewhitered colormap scheme (59.b) radargrams, we see reflections with less visibility, yet a slightly uneven topography beginning from ~50 m and continues across the profile shortly after ~400 m. Figure 59.b shows darker reflections roughly around 1210 m.

Line 3. In both the gray colormap scheme (60.a) and bluewhitered colormap scheme (60.b) radargrams, we see reflections with high visibility within the subsurface. However, parts of the radargram are blurry due to a mixture of different radar wave reflections. Figure 60.b shows darker color reflections of signals that appear uneven and closely spaced. We also observe interrupted

“ringing” noise in parts of the profile for both figures. 75 75

Figure 59. Finalized radargram of Line 2 with topographical correction. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram

Figure 60. Finalized radargram of Line 3 with topographical correction. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram 76 76

To see the best interfaces from rock avalanche and valley floor, we utilized the TPOW gain and adjusted the gain values to improve the visibility. The “bluewhitered” colormap was also applied to complement the visibility of the radargrams.

Selecting Interfaces in GPR I applied the TPOW gain and increased the figure contrast to enhance the visibility, and to examine the consistent reflections in all the radargrams of the three profiles. After examining each radargram, I also applied the “bluewhitered” colormap to carefully selected reflections of the same color which were consistent across each of the radargrams.

Line 1 with selected interfaces. The radargram for Line 1 using the 50 MHz antennae produced many reflections that mixed with each other. Using the Bluewhitered colormap, I noticed that the reflections appeared to show two interfaces across the transect. There was also a “middle” interface in between the lower and upper interfaces. However, the “middle” interface was very close to the lower interface and later merged with the lower interface. The elevation between the middle and lower interfaces was less than 1 meter apart. The gray colormap (Figure 61.a) and Bluewhitered colormap (Figure 61.b) radargrams both show that the interfaces are roughly parallel and closely spaced together at ~10 m. The interfaces continue across the transect and separate at ~50 m, where the distance between them increases, and extend towards the end of the profile, following a slightly linear topography. The colored radargram (Figure 61.b) highlights the darker reflections within the subsurface of the profile, and we discern ringing at ~255m and ~300m. 77 77

Figure 61. Finalized GPR Line 1 radargram with selected interfaces. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram

Line 2 with selected interfaces. The radargrams for Line 2 utilized the 100 MHz antennae which depicted the faintest and least clear reflections compared to the other profiles. The majority of the reflections appeared nearly horizontal across the profile. On each side of the profile, some of the reflections appeared stacked on top of each other. The gray colormap scheme (Figure 62.a) and bluewhitered colormap scheme (Figure 62.b) radargrams both show that the interfaces are roughly parallel. The bluewhitered colormap radargram (Figure 62.b) highlights the reflections with darker colors within the subsurface of the profile, and we discern some ringing at ~185m and ~395m.

Line 3 with selected interfaces. The radargram for Line 3, for which we utilized the 50 MHz antennae, revealed many horizontal reflections across the profile. In both the gray colormap scheme (Figure 63.a) and bluewhitered colormap scheme (Figure 63.b) radargrams, I observe that the interfaces have reflections that are strongly visible, roughly parallel, closely spaced together, and follow a relatively horizontal topography across the profile. I also notice that ringing is more visible in the bluewhitered colormap radargram than the gray colormap radargram. 78 78

Figure 62. Finalized GPR Line 2 radargram with selected interfaces. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram

Figure 63. Finalized GPR Line 3 radargram with selected interfaces. Note: (a) Gray colormap radargram. (b) Bluewhitered colormap radargram 79 79

From the results of the GPR method alone, I see that each radargrams of all the profiles has more than one horizontal interface reflection within the subsurface. Since it was impossible to delineate which horizontal interface reflection is the separation between the rock avalanche deposit and the paleo- valley floor in any of the profiles, I used the “Constrained inversion” approach to investigate if the picked GPR reflections would fit with the ERT data.

Combining GPR and ERT Once the GPR interfaces were selected and used as a-priori information incorporated into the ERT inversion process in the BERT software program, the program produced an electrical resistivity model of the rock avalanche and valley floor. If the GPR data fits with the ERT data, the BERT program will contain a sharp electrical resistivity transition of where that interface is. If the ERT data only allows one of the possible GPR interface to be an abrupt transition in the resistivity values, then we have a candidate for the valley floor. However, if the sharp interfaces obtained from the GPR data do not fit with the ERT data inversion, then there will be no sharp transitions in the ERT inversion result at the given GPR interface. The perceptually uniform color scheme “Viridis” was chosen for all models in BERT to see a clear transition between high resistivity versus low resistivity.

Line 1 Figure 64.b depicts a clear transition of high resistivity above the lower interface on top of the low resistive subsurface. Roughly below 1200 m elevation, the low resistivity continues to increase with depth. The interface between the two resistivity contrasts appears linear. In Figure 64.a, we see the upper interface in between the highly resistive subsurface. In Figure 64.c, we observe both the lower 80 80 and the upper interface are first merged together from ~10m towards ~150m where the two interfaces separate and follow a linear topography.

Figure 64. Line 1 inversion results allowing sharp transitions across the picked interfaces. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

Line 2 In Figure 65.a, we observe most of the highly resistive area lying over the lower interface which is mostly separated from the low resistivity material within the subsurface. The lower interface appears relatively horizontal. In Figure 65.b, we see the upper interface in between highly resistive materials within the subsurface. In Figure 65.c, we see a large majority of the highly resistive material lying above both the lower and upper interfaces. 81 81

Figure 65. Line 2 inversion results allowing sharp transitions across the picked interfaces. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

Line 3 In Figure 66.a, we observe a moderate amount of the highly resistive material lying over the lower interface, partially separated from the low resistivity material within the subsurface at approximately 1210 m depth. The interface between the two resistivity contrasts appears horizontal. In Figure 66.b, we see the upper interface in between moderately resistive material also at 1210 m depth within the subsurface. In Figure 66.c, we notice highly resistive material in between the lower and upper interfaces. However, we see moderate to low resistivity above and below both interfaces. In both Transects 1 and 2, we observe high resistivity above the lower interface and low resistivity beneath. Also, examining the plotted reflections in Transects 1 and 2, we were able to detect the location of the resistivity transition. In Transect 3, we saw moderate resistivity above the lower interface, and when I ran the inversion using both the interfaces, the moderate to low resistivity was above and beneath the two interfaces. The high resistivity was in between the two interfaces. 82 82

Figure 66. Line 3 inversion results allowing sharp transitions across the picked interfaces. Note: (a) Upper interface. (b) Lower interface. (c) Combination of the two interfaces.

In each transect, I interpreted some of the consistent reflectors as interfaces. From this interpretation, I obtained two interfaces per GPR profile. I detected high resistivity in both Lines 1 and 2 above both the two interfaces, and low resistivity beneath. The upper interfaces on both lines appeared within the highly resistive material. In Line 3, we mostly see moderate to low resistivity, and when both interfaces are incorporated into the inversion, there was not a clear separation between the resistive contrasts, compared to Lines 1 and 2. The “constrained 83 83 inversion” for transect 3 did not work well. While performing the two surveys on Line 3, the surface of the path was heavily vegetated, had less of the rock avalanche deposit exposed, and the deposits had finer clast size compared to Line 1 and 2. This may have resulted in the subsurface of Line 3 having more moderate resistivity. The surface of the path for Line 1 was covered with rock avalanche deposits, such as variable-size boulders, embedded in the ground. The surface of the path for Line 2 was mostly a road paved for visitors and there were some rock avalanche deposits in the area with variable clast size. For each of the profile intersections, I compared the resulting elevations of the underlying valley floor and found that their elevations match up with each other. At the intersection of Line 2 with Line 3, Line 2 showed the valley floor at 1209.3 m and Line 3 showed the valley floor at 1208.9 m. At the intersection of Line 1 with Line 2, Line 1 showed the valley floor at 1207.5 m and Line 2 showed the valley floor at 1207.1 m. Figure 67 shows the location of the intersections between the profile lines along with the assumed location of the underlying paleo- valley floor estimation at 1204 m AMSL and location of where the intersections were obtained. These paleo-valley floor measurements were slightly above the 1204 m AMSL estimation. 84 84

Figure 67. Intersection points of the profile lines with map (below). Note: a. Lines 1 and 2 intersect at 237 m along Line 1 and 142 m along Line 2 at “a.” The interface is at elevation is 1207.5 m on Line 1 and 1207.1 on Line 2. b. Lines 2 intersects Line 3 at 342 m along Line 2 and 0 m along Line 3 at “b.” The interface is at elevation 1209.3 m on Line 2 and 1208.9 m on Line 3. The horizontal red line indicates the assumed location of the underlying paleo-valley floor surface at 1204 m AMSL. The bottom shows the map location for “a” and “b.” DISCUSSION

Previous studies that involved the combination of more than one near- surface geophysical method (Bichler et al., 2004 and Doestch et al., 2012) to image the subsurface, Doestch et al. (2012) defined three approaches to locate transitions within subsurface materials possessing different physical properties that are sensitive to the geophysical technique. These three approaches were explained in the “Methods” section under the “Combining ERT and GPR” subsection. In this study, the “constrained inversion” approach was employed to integrate the ERT and GPR methods. In the ERT inversions, the paleo-valley floor was expected to have a relatively low resistivity due to the sediment composition and compaction of silts and clays, and possible water content. In contrast, the rock avalanche debris was expected to have a relatively high resistivity because the debris contains a porous mixture of granite deposits. In the GPR radargrams, each profile contained numerous relatively horizontal, consistent reflections across the profile. There were many horizontal reflections near the surface and close to the expected valley floor, and some reflections were closely spaced together. Since the wave coming from the transmitter antenna can travel in all directions, it may reflect off any interface between earth materials with different electrical properties, before being recorded by the receiver antenna. In cases where it reflects in two places, a double reflection can occur. As an example, the transmitted wave may reflect off the valley floor, travel back to the surface to then reflect off the surface, travel back again to the valley floor, and finally travel back to the surface to be recorded by the receiver antenna. If double reflection is the case, then the distance between the surface and valley floor should be consistent and parallel along the profile. Another possibility is that the later wave arrivals are 86 86 reflections from the valley floor and the earlier wave arrivals are reflections from some internal structure in between the valley floor and the surface. The internal structure from which the earlier waves arrive could be differentiation between clast sizes within the subsurface. At the profile intersections, the elevation of the underlying valley floor matches up with elevation ~1209 m where Lines 2 and 3 intersect and elevation ~1207 m where Lines 1 and 2 intersect. In figures of GPR Lines 1- 3 with the selected interfaces, we see several interrupted reflections of numerous layers within the subsurface. A common phenomenon observed in rock flows is that while the rock avalanche flows, small size sediment particles move to the bottom while the largest size sediment particles rise to the top. This phenomenon is known as “Granular Convection,” or the “Brazil nut effect” (Wibowo et al., 2016), and leads to the inverse grading of the deposit, where fine size sediments end up at the bottom, and larger size sediments end up at the top of the deposit. The reflections in the GPR profiles that are within the rock avalanche deposit could be from layers of finer clasts from the inverse grading. Waves get reflected instead of scattered from layers of sorted clasts when the clast size is significantly smaller than the wavelength. There is a possibility that the internal structures of the three profiles may show sediment stratification of finer and coarser layers. This principle may allow, in future research, to estimate the amount of small versus large clasts in the rock avalanche. Inverse grading can take place in different geological settings such as a submarine landslide, Olistostrome. In this example, this study was conducted in Northern Apennine, Italy to examine an exhumed sedimentary block of a mass transport deposit. Within the study, Festa et al., (2014) observed inverse grading of different sediment deposits increasing in size and thickness of the Olistostromes. 87 87

In future work, I intend to continue and complete a 3-dimensional ERT inversion of the three profile lines. Also in future work, I will calculate a volume estimation of the proximal portion of the rock avalanche using a model of the subsurface interface built from the GPR radargrams and ERT inversion. So far we have reconstructed Stock’s original volume estimation (Figure 68). This reconstruction was done in Matlab using the open source LiDAR data to take the surficial elevation data and subtract an elevation of 1204 m, assuming that the topography between the rock avalanche and valley floor is flat. I then interpolated the LiDAR topography into a regular 1 m by 1 m grid and height values outside the outline of the proximal portion of the rock avalanche to zero. I calculated each of the blocks’ volume and summed them up to obtain a volume estimation of 1.03x106m3 (see Figure 68 in Appendix). Once the volume estimation of the proximal portion of the rock avalanche deposit is completed, this volume estimation can aid hazard assessment in calculating how volume versus frequency of rock avalanche events can be used to determine recurrence intervals. Also, imaging the basal contact can provide information about the runout mechanics, such as how the excavation of the sediments on the valley floor would affect the runout distance.

CONCLUSION

The ERT and GPR surveys performed in the field were highly effective for obtaining information about the internal structure of the rock avalanche resting on top of the paleo-valley floor. The ERT inversion model revealed smooth transitions characterizing the valley floor beneath the rock avalanche deposit. The interface of the ERT inversion model for all three transects mostly appeared relatively horizontal. As expected, the paleo-valley floor had low resistivity while the rock avalanche deposit had high resistivity. The smooth transition from high to low resistivity in the ERT inversion did not allow for pinpointing the exact location of the valley floor underneath the rock avalanche. The GPR data image revealed many nearly horizontal reflections across each of the radargrams for each of the profiles making it impossible to identify the valley floor. Neither of the geophysical techniques we used could alone resolve the interface. Integrating the ERT inversion model with the GPR reflection interfaces allowed me to identify the contact between the rock avalanche deposits and the paleo-valley floor. Examining the points where the profiles crossed revealed that each of the elevations of the lower interfaces matched up with each other. There were also strong reflections within the highly resistive region that might indicate inverse grading.

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APPENDIX: SUPPLEMENTAL FIGURES

96 96

Figure 68. Volume estimation. Note: 68.a Stock’s volume estimation of the proximal portion of 1.03x106m3. 68. b Our reconstruction of Stock’s volume estimation of the proximal portion of 1.03x106m3.