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THE DETERMINATION OF THE FRACTURE ARCHITECTURE AND DEFORMATIONAL

BEHAVIOR OF THE FISH LAKE VALLEY FAULT ZONE, CALIFORNIA:

A DIGITAL APPROACH USING PHOTOREALISTIC

SURFACE MAPPING WITH LIDAR

Rebecca Aguilar

APPROVED BY SUPERVISORY COMMITTEE:

______Carlos L. V. Aiken, Chair

______John S. Oldow

______William R. Griffin

Rebecca Aguilar

Dedicated to my advisor Dr. Carlos Aiken, my lab mates Tiffany Savage, Tara Urbanski,

Mansour Alhumimidi, my colleagues Bach Pham, Lionel White and my Mom, Lulu, Dad, Joel

and sister, Amaya for their support and incredible advice.

THE DETERMINATION OF THE FRACTURE ARCHITECTURE AND DEFORMATIONAL

BEHAVIOR OF THE FISH LAKE VALLEY FAULT ZONE, CALIFORNIA:

A DIGITAL APPROACH USING PHOTOREALISTIC

SURFACE MAPPING WITH LIDAR

REBECCA AGUILAR, BS

THESIS

Presented to the Faculty of

The University of Texas at Dallas

in Partial Fulfillment

of the Requirements

for the Degree of

MASTER OF SCIENCE IN

GEOSCIENCES

THE UNIVERSITY OF TEXAS AT DALLAS

August 2017

ACKNOWLEDGMENTS

I am eternally grateful for Dr. Carlos Aiken’s endless support and advice. I want to thank Bach

Pham for being an excellent field partner and friend that helped me brave long days in the field.

I also want to thank Tiffany Savage, Tara Urbanski, Mansour Alhumimidi, and Lionel White for their abundant intellect and advice.

I want to thank Dr. John Oldow, the Miles Foundation, and Pioneer for providing resources, equipment and for funding this work.

I would like to acknowledge R.W. Allmendinger for his Stereonet 9 software which was utilized in plotting fracture orientations. The concepts for the algorithms in Stereonet 9 can be found in

Structural Geology Algorithms: Vectors & Tensors (Allmendinger, R. W. et al.) and Spherical projections with OSXStereonet (Cardozo, N., and Allmendinger, R. W., 2013).

May 2017

THE DETERMINATION OF THE FRACTURE ARCHITECTURE AND DEFORMATIONAL

BEHAVIOR OF THE FISH LAKE VALLEY FAULT ZONE, CALIFORNIA:

A DIGITAL APPROACH USING PHOTOREALISTIC

SURFACE MAPPING WITH LIDAR

Rebecca Aguilar, MS The University of Texas at Dallas, 2017

ABSTRACT

Supervising Professor: Dr. Carlos L.V. Aiken

Located in the Eastern California Shear zone (ECSZ), the Fish Lake Valley fault zone (FLVFZ) is a 250 km right-lateral fault and the longest active structure of the ECSZ. It offsets Pre-

Cenozoic monzonite and metasediments and contains an estimated net displacement of 50 to 65 km. Just north of Death Valley, the FLVFZ passes through the Cucomungo Canyon Restraining

Bend (CCRB), a step over 15 km wide. Recent uplift of the CCRB within the last million years has exposed Miocene and Pliocene deposits and internal structures of the fractures in the

FLVFZ. The Mesozoic monzonites of the FLVFZ contain fault zones hundreds of meters wide consisting of alternating cataclasites and fractured rock. This work comprises a portion of the

Miles Project, a multidisciplinary project implementing field mapping, Geophysics and terrestrial lidar scanning techniques to determine the deformation regime and motion of the faults. Mapping the well-exposed fractures in the CCRB with terrestrial lidar scanning serves as an example of the application of photorealistic surface models and tools to extract fracture structures and

orientations in 3D. Building a photorealistic model consists of processing the lidar data, creating a triangulated irregular network mesh, georeferencing the site with global navigation satellite system coordinates and draping the photos onto the model. The mapped fracture patterns and orientations are utilized to determine the deformational behavior and movement in the fault zone.

These data are combined with fieldwork to enable geologists to resolve and revisit field sites at any time.

vii

TABLE OF CONTENTS

Acknowledgments…………………………………………………………………………….…..v

Abstract…………………………………………………………………………………………...vi

List of Figures…………………………………………………………………………………….ix

Chapter 1: Introduction…………………………………………………………….……………...1

Chapter 2: Geology of the Fish Lake Valley Fault Zone………………………………………….4

Chapter 3: Photorealistic Surface Modeling from Lidar…………………………………………10

Chapter 4: GNSS Control………………………………………………………………………..19

Chapter 5 Mount Calibration and Vegetation Removal…………………………………………23

Chapter 6: Generating the TIN and the Photorealistic Model…………………………………...26

Chapter 7: Digitizing Fractures on Photorealistic Models……………………………………….31

Chapter 8: Results………………………………………………………………………………..35

Chapter 9: Conclusion and Discussion…………………………………………………………..45

References………………………………………………………………………………………..47

Biographical Sketch……………………………………………………………………………...51

Curriculum Vitae………………………………………………………………………………...52

viii

LIST OF FIGURES

Figure 2.1. A view of the western United States. Walker Lane and the ECSZ highlighted as active deformation zones (Oldow et al., 2008)…………………………………………………4

Figure 2.2. Hillshade map of the Fish Lake Valley fault zone with a draped DEM. FLV, Fish Lake Valley; FLVFZ, Fish Lake Valley fault zone; HTH, Horse Thief Hills; WW, Willow Wash; SM, Sylvania Mountains; CCRB, Cucomungo Canyon Restraining Bend; EV, Eureka Valley; LCR, Last Chance Range; DV-FCFS, Death Valley Furnace Creek fault system. Faults are outlined in black, blue arrows point to the dominant Fish Lake Valley fault strand………….5

Figure 2.3. View of the FLVFZ. Faults are outlined in red……………………………………….7

Figure 2.4. Geologic map of the FLVFZ. Pliocene sediments: tan, mid-late Miocene sediments: brown, Jurassic monzonite: blue, Paleozoic and Proterozoic metasediments: green……………..7

Figure 2.5. Close-up view of the study area with the scan sites. Faults outlined in red, blue arrows point to the dominant Fish Lake Valley fault strand………………………………………9

Figure 3.1. a. Close-up views of fractures on a photorealistic model. b. Comparison to a point cloud. Sedimentary beds: green, fractures: red, blue arrows point to the same feature, meter stick for scale…………………………………………………………………………………….12

Figure 3.2. a. Scan position of the Monzonite Fracture North outcrop viewed from the scanner’s position. b. Rotating the scan 90° to the right reveals occluded data (indicated by white arrows). c. Adding the data from a different scan position fills the holes from Figure 3.2.b……………..14

Figure 3.3. Looking east from above the Monzonite Fracture South point cloud with respective scan positions. White arrows point to holes in the data. Scan positions: grey rectangles, reflectors: red circles, black background: no data……………………………………………….16

Figure 3.4. Looking east on the Paleozoic Fracture point cloud. Scan positions: grey rectangles, reflectors: red circles. Black areas contain no data…………………………………..17

Figure 3.5. a. Looking east and from above the Monzonite Fracture North point cloud. b. Looking north into the canyon, Monzonite Fracture North is on the right side of the image. Scan positions: grey rectangles, reflectors: red circles, hill slope feature: highlighted in blue………..18

Figure 4.1. a. The GNSS positions of the base station and reflectors of the Paleozoic Fracture outcrop. b. The GNSS positions on Google Earth. The reflectors in Figures 4.1-4.3 correspond to the reflectors in Figures 3.3-3.5……………………………………………………………….20

Figure 4.2. a. The GNSS positions of the base station and reflectors of the Monzonite Fracture South outcrop. b. The GNSS positions on Google Earth. c. GNSS positions of the base station and the CORS P094 position. d. Comparison image on Google Earth…………………………..21

Figure 4.3. a. The GNSS positions of the base station and reflectors of the Monzonite Fracture North outcrop. b. The GNSS positions on Google Earth………………………………………...22

Figure 5.1.a. A side by side comparison of the reduction of pixel error before. b. After performing a mount calibration. Note how the “tp004” marker appears in the center of the reflector…………………………………………………………………………………………..24

Figure 5.2. The results of a mount calibration…………………………………………………...24

Figure 6.1. Close-up of the mesh exhibiting the surface artefact. Green circles: artefacts, red circles: “crumpled aluminum foil” features………………………………………………….26

Figure 6.2. a. Comparison of the TIN mesh before cleaning the mesh. b. The mesh after hole filling, and reducing the triangle count from 5.3 million to 1.3 million. White arrows point to the same feature……………………………………………………………………………….30

Figure 7.1. GAT’s Sedimentary Analysis Tools is utilized for outlining fractures and sedimentary beds. Red: Fractures, Blue-green: fault slip surface………………………………..31

Figure 7.2. a. From a distance, the fracture appears clearly defined (blue arrow) b. Up close, (blue arrow) part of the fracture is not discernable. Meter stick for scale………………………32

Figure 7.3. a. View with the model turned on. b. View with the model turned off. c. A profile view of the same fractures looking parallel to the fracture plane. View with the model turned on. d. View with the model turned off. Blue arrows point to the same feature, meter stick for scale………………………………………………………………………………………………33

Figure 7.4. GAT’s Structural Geology tools calculates the fracture’s orientation from the points placed on the fracture’s surface expression, assuming that fracture is a planar feature. It is not possible to obtain an orientation on nonplanar fractures or fractures that exhibited a change in direction……………………………………………………………………………….34

Figure 8.1. a. An overall view of the Paleozoic Fracture model. b. A close-up view of the fracture zone. Red: fractures, green: sedimentary beds………………………………………….35

Figure 8.2. a. Stereonet plots of fractures from the Paleozoic Fracture outcrop. Fractures dipping from 25 to 59 degrees. b. Fractures dipping from 60 to 90 degrees. c. Fractures exhibiting offset. d. All fractures that do not exhibit offset………………………………………………………...37

Figure 8.3. a. A close-up of the fractures in the fractured monzonite b. A close-up of the boulders in the cataclasite matrix. c. An overview of the Monzonite Fracture South outcrop. The fractured rock is on the northern side of the outcrop and the cataclasite is in the south. Fractures: red, blue arrows point to the fault slip surface, fault slip surface: blue……………………………………38

Figure 8.4. a. Ground measurements taken on-site from the slickenside surface (blue lines in Figure 8.3.c.) compared to measurements taken on the photorealistic model. b. Stereonet measurements taken on the photorealistic model………………………………………………..39

Figure 8.5. a. Stereonet plots of fractures from the Monzonite Fracture South outcrop Fractures dipping from 0 to 9 degrees. b. Fractures dipping from 10 to 19 degrees. c. Fractures dipping from 20 to 29 degrees. d. Fractures dipping from 30 to 39 degrees. e. Fractures dipping from 40 to 49 degrees. f. Fractures dipping from 50 to 59 degrees. g. Fractures dipping from 60 to 69 degrees. h. Fractures dipping from 70 to 79 degrees. i. Fractures dipping from 80 to 90 degrees………………………………………………………41

Figure 8.6. a. Fractures dipping from 0 to 39 degrees. b. Fractures dipping from 40 to 90 degrees…………………………………………………………………………………………...42

Figure 8.7. a. Fractures dipping from 0 to 29 degrees. b. Fractures dipping from 30 to 59 degrees. c. Fractures dipping from 60 to 90 degrees………………………………………………………42

Figure 8.8. a. An overview of the Monzonite Fracture North outcrop. b. A close-up of the fracture zone. Fractures in red, meter stick for scale…………………………………………….43

Figure 8.9. a. Stereonet plots of fractures dipping from 0 to 29 degrees. b. Stereonet fracture plots of fractures dipping from 30 to 90 degrees………………………………………………...43

CHAPTER 1

INTRODUCTION

The right oblique Fish Lake Valley fault zone (FLVFZ) is an active 250 km long structure that is a major component of the Eastern California Shear Zone (ECSZ) (Oldow et al., 2001).

East of the White Mountains in California, the fault passes through the Cucomungo Canyon

Restraining Bend (CCRB), a 15 km restraining step over (Oldow et al., 2008). In the last 700,000 years, uplift and erosion have exposed sedimentary rocks, volcanics and internal structures in the pre-Cenozoic basement of the CCRB (Rehiess and Sawyer, 1997). The CCRB differs from a simple restraining bend model due to its lack of crustal shortening and high relief (Cunningham and Mann, 2007). West of the FLVFZ, the CCRB is characterized by Pre-Cambrian and

Paleozoic metasediments (McKee, 1968). East of the FLVFZ, the basement rocks consist of a faulted and fractured Jurassic monzonite nonconformably overlain by a series of Miocene basalt flows, rhyolite tuffs, and sediments. The FLVFZ consists of multiple strands, some still actively deforming the pre-Cenozoic basement and others buried by succeeding layers of volcanics and sediments. The monzonite contains cataclasite shear zones, tens to hundreds of meters wide that alternate with fractured rock (Mueller et al., 2016). These cataclasite zones are well exposed and contain fractured boulders several meters wide.

Mapping the structures in the fault zone is an objective of the Miles project, a multidisciplinary project utilizing field mapping, Geophysics and terrestrial lidar scanning (TLS) to determine the timing of historical deformation and geometry of the CCRB in the FLVFZ

(Oldow et al., 2016). Fractures can be indicative of whether a fault zone was in a brittle or ductile regime based on the presence of certain shear structures (Byerlee, 1968). Comparing

fracture patterns across the metasediments and the monzonite establishes whether or not the deformation is consistent across different lithologies. Also, comparing the fractures between the monzonite boulders and the fractured rock determines whether the boulders rotated in the cataclasite. The CCRB’s deeply incised canyons lend themselves to mapping fault structures with TLS and utilizing tools to extract fracture surface expression and orientations in 3D. The outcrops in the CCRB are freshly exposed rock with little to no vegetation, making it ideal for capturing with TLS. Though digitizing fractures on a colored point cloud is the most common approach, point clouds do not maintain detail upon zooming in closely (Silvia et al., 2015). Since photorealistic surface models utilize photos draped on the model, they retain pixel resolution.

The pixel resolution is determined by the focal length of the camera used to acquire imagery.

This enables users to examine surface details otherwise lost in a point cloud.

Building a photorealistic model consists of capturing lidar data, creating an optimized triangulated irregular network (TIN) mesh, and draping selected high-resolution photos onto the mesh (Bonnaffe et al., 2007). This method involves integrating TLS with imagery and a global navigation satellite system (GNSS) to create high-resolution photorealistic surface models of the outcrops where features can be digitized and georeferenced on the model (Wilson et al., 2011;

Minisini et al., 2014). GNSS consists of satellite constellations that enable a user with a receiver to position their location on the earth’s surface with respect to the center of the earth (El-

Rabbany, 2002). TLS provides the surface control of the outcrop, which is georeferenced to the global coordinate system with GNSS measurements accurate to a few centimeters. This approach incorporates a medium range (up to 350 m range) lidar scanner with an attached scanner mounted coaxial camera (Qihong et al., 2012). Transformation matrices tie photos to the lidar

data such that they can be draped on the surface model (Xu, 2000). Information such as shape, orientation, strike and dip can be acquired in detail on a photo-draped surface model (Minisini et al., 2014). Photorealistic surface models are intended to augment the extraction of data in addition to fieldwork by enabling geologists to rectify field measurements and “revisit” the site at any time (Xu, 2000; McCaffrey et al., 2010; White, 2010; Hodgetts, 2013).

CHAPTER 2

GEOLOGY OF THE FISH LAKE VALLEY FAULT ZONE

Located in western North America, the ECSZ and Walker Lane accommodate about 20 to

25% of relative movement between the North American and Pacific tectonic plates (Oldow et al.,

2001). This movement consists of 10 to 14 mm of displacement per year (Figure 2.1) (Oldow et al., 2008). A component of the ECSZ is the Furnace Creek-Fish Lake Valley fault system, a northwest striking right-lateral fault about 250 km long with a net displacement ranging from 50 to 65 km with some estimates as high as 100 km (Stewart, 1967; Snow and Wernicke, 1989;

Oldow et al., 2008). The FLVFZ is located north of Death Valley, California and consists of a segment of the Furnace Creek Fault (Figure 2.2) (Reheiss and Sawyer, 1997).

309 km

Figure 2.1. A view of the western United States. Walker Lane and the ECSZ highlighted as active deformation zones (Oldow et al., 2008).

Figure 2.2. Hillshade map of the Fish Lake Valley Fault zone with a draped DEM. FLV, Fish Lake Valley; FLVFZ, Fish Lake Valley fault zone; HTH, Horse Thief Hills; WW, Willow Wash; SM, Sylvania Mountains; CCRB, Cucomungo Canyon Restraining Bend; EV, Eureka Valley; LCR, Last Chance Range; DV-FCFS, Death Valley Furnace Creek fault system. Faults are outlined in black, blue arrows point to the dominant Fish Lake Valley fault strand.

At the southern end of the FLVFZ is the CCRB, a 15 km-wide bend consisting of the

Sylvania Mountains and the Last Chance Range (Oldow et al., 2016). The Sylvania Mountains, located east of the Fish Lake Valley Fault, consist of a distinctive Jurassic porphyritic quartz monzonite pluton (Figure 2.3) (Mckee, 1968). The monzonite also contains diorite inclusions that average between 10 and 20 cm in diameter. On the west side of the fault, the dominant rocks are an assemblage of Precambrian and Paleozoic quartzite and limestone metasediments

(McKee, 1968). The Fish Lake Valley Fault laterally offsets the monzonite pluton by an estimated 50 km juxtaposing the monzonite and metasediments (Mckee, 1968). Right lateral motion on the Fish Lake Valley fault started 8 to 12 million years ago (Reheiss and Sawyer,

1997). Transtension is estimated to have started around 4 million years ago (Mueller, 2016 personal conversation). Based on these offsets, the long-term slip rate on the Fish Lake Valley fault is estimated at 3 to 6 mm/yr with a maximum at 12 mm/yr (Reheiss and Sawyer, 1997).

The monzonite pluton of the Sylvania Mountains contains a fault zone 3 to 4 km wide consisting of alternating zones of cataclasite and fracture zones tens to hundreds of meters wide that mark transcurrent strike slip fault strands (Oldow, 2016). The cataclasite zone contains fractured monzonite boulders several meters wide in a cataclasite matrix. 10 to 12 million-year- old basalt and tuff flows nonconformably lie on the deformed monzonite plutons (Figure 2.4)

(Reheiss and Sawyer, 1997). Late Miocene to early Pliocene lacustrine and fluvial deposits overlay these basalt flows. Several of the strike slip fault strands in the monzonite offset the basalt and the Miocene and Pliocene units (Figure 2.3) (Reheiss and Sawyer, 1997).

2.0 km

Figure 2.3. View of the FLVFZ. Faults are outlined in red.

Figure 2.4. Geologic map of the FLVFZ. Pliocene sediments: tan, mid to late Miocene sediments: brown, Jurassic monzonite: blue, Paleozoic and Proterozoic metasediments: green.

Succeeding Pliocene units bury most of the faults in the monzonite except for the dominant

Fish Lake Valley Fault. Offset Pleistocene alluvium channels indicate that there was recent movement on the Fish Lake Valley Fault. The Miocene and Pliocene units are part of a basin fill several miles north of the restraining bend. In the CCRB, these units are deeply incised by canyons and well exposed due to recent uplift within the last 700,000 years, providing a cross section into the internal structure of the basin (Reheiss and Sawyer, 1997).

The relief of the Cucomungo Canyon restraining bend differs from what is considered a simple restraining bend model. Restraining bends typically form high relief structures and exhibit extensive crustal shortening (Cunningham and Mann, 2007). The Sylvania Mountains to the east and the Last Chance Range southeast of the restraining bend display high relief relative to the surrounding landscape while the Horse Thief Hills to the west displays low relief. Material eroded from the crushed monzonite from the Sylvania Mountains accumulates southwest of the fault, creating a massive alluvial fan in Eureka Valley (Figure 2.2) (Blair, 2003). Outcrops exhibiting well exposed fracture and cataclasite zones were selected for lidar scanning. The

Paleozoic Fracture outcrop, seen in Figure 2.5, consists of fractured metasediments and lies adjacent to the dominant Fish Lake Valley Fault strand. Located in the monzonite, the Monzonite

Fracture North and South outcrops lie along the fault strands parallel to the Fish Lake Valley

Fault.

282 m

Figure 2.5. Close-up view of the study area with the scan sites. Faults outlined in red, blue arrows point to the dominant Fish Lake Valley fault strand.

CHAPTER 3

PHOTOREALISTIC SURFACE MODELING FROM LIDAR

Lidar offers a digital method where data can be captured, visualized, interpreted, archived and re-evaluated in the lab or the field (Xu, 2000; Wilson et al., 2011; Pless et al., 2015). The approach is to map the fractures on lidar-derived photorealistic models; a photo-draped triangulated surface mesh (Xu, 2000; White, 2010). Time-of-flight lidar scanners, which utilize a pulse echo, have a range of 250 m to 6000 m, an accuracy of 5 to 10 mm and are classified as medium and long-range scanners (Vosselman and Maas, 2010). Phase-measurement scanners can provide an accuracy of 1 to 3 mm, although with a range of 20 to 80 m. This project requires a terrestrial medium-range lidar scanner with photographs to generate photorealistic models (Xu,

2000). Lidar systems generate hundreds of thousands to millions of points per second, capturing greater detail than possible with laser guns or 2D photography (Alfarhan et al., 2008).

Candela and Renard (2012) utilize lidar to measure the roughness and fractal properties of

Dixie Valley slickenside surfaces to determine the geometric properties of deformed fault lenses and slip surfaces. In addition to on-site measurements of fault lenses and cataclasite sample collection, this analysis includes creating digital elevation models of the fault slip surfaces.

Unlike the irregular and eroded surface of the monzonite in Fish Lake Valley, the slickenside surfaces at Dixie Valley are smooth and relatively free of vegetation and debris. Some of the methods of digitizing and extracting fracture orientations include manual and semi-manual methods on point clouds and photorealistic surface models (Wilson et al., 2011). One of these techniques uses semi-manual methods to pick fractures from a point cloud, which allows the utilization of algorithms to digitize fractures. This technique identified five out of seven

recognized fracture sets in the outcrops, leaving two fracture sets underrepresented (Wilson et al., 2011). Unlike point clouds, the resolution of photorealistic models is not limited by point spacing rather; it is limited by the pixel resolution of draped images (Bistacchi et al., 2011).

Digitizing fractures allows for color contrast-based measurements on a surface model to recognize fractures and offsets (Kokkalas et al., 2007). These measurements can be archived, re- evaluated and displayed on charts and stereonets (Wilson et al., 2011).

Minisini et al. (2014) demonstrates the effectiveness of digitizing fractures on a high- resolution photorealistic surface model of the highly fractured Cretaceous Eagle Ford Shale in

West Texas. Utilizing a photorealistic model, bioturbated and lenticular structures are digitized within the Eagle Ford Shale and to denote fracture spacing and individual channels (Minisini et al., 2014). These features are extracted from the models with Geo Analysis Tools (GAT), an

ArcScene plug-in (White, 2010). GAT enables users to digitize geologic features, and calculate fold and bedding orientations.

Point clouds capture meter-scale fractures that exhibit topographic expression, though fractures that do not exhibit surface relief or are smaller than the point-to-point spacing on the

TLS dataset are not discernable (Pless et al., 2015). Utilizing surface models draped with high- resolution imagery allow the user to examine features while maintaining pixel detail. Pixel detail is constrained by the camera’s focal length. For example, an image from an 85 mm camera contains more detail than an image from a 20 mm camera (Bistacchi et al., 2011). The detail of the centimeter scale fractures is lost on a detailed point cloud (Figures 3.1.a-b). Lidar data generates the terrain of the outcrop’s surface while the pixel resolution of the photos provides color patterns for visual analysis (Silvia et al., 2015).

1m 1m

a b

Figure 3.1. a. Close-up views of fractures on a photorealistic model. b. Comparison to a point cloud. Sedimentary beds - green, fractures - red, blue arrows point to the same feature, meter stick for scale.

ACCURACY AND RESOLUTION OF TLS METHODS

Building high-resolution photorealistic models consists of capturing and cleaning the point cloud, creating and editing the TIN mesh, draping the photos and digitizing features on the model’s surface (Xu, 2000; White, 2010; Minisini et al., 2014). Two survey grade, high accuracy

GNSS Topcon HiPer Lite receivers provide control that tie the model to the global coordinate system. Acquiring the lidar data is the first step towards building a high-resolution photorealistic surface model.

Outcrops are selected based on their degree of exposure, relief and lithology. It is recommended to look for outcrops with freshly exposed rock surfaces and minimal vegetation.

Scanning outcrops with differing lithology enables a comparison of fractures in different rock types. The three selected outcrops are between 100 and 150 meters long in deeply incised canyons and exhibit high relief erosional surfaces. Data is captured with a camera-mounted

RIEGL VZ-400i, a terrestrial lidar scanner, which emits 122,000 points per second in high-speed

mode and has a maximum range of 350 m for targets with 90% reflectivity and 160 m for targets with 20% reflectivity (RIEGL LMS GmbH, 2014). In a scan, some areas are occluded as a result of objects, such as trees or rocks, blocking the scanner’s beam, creating “shadows” in the data

(Alfarhan et al., 2008). Outcrops are scanned from multiple angles to fill these “shadows” or gaps in the data (Figure 3.2.a-c). While it is possible to fill holes in lidar data, it is not possible to fill every hole in the data due to time constraints, vegetation, oblique angles or inaccessibility.

For example, Monzonite Fracture South in Figure 3.3, contains several gaps in the data at the top of the outcrop (center of the image). Since TLS positions are restricted to the ground, it is not possible to fill these gaps because the angle between the top of the outcrop and the scanner was too oblique.

The VZ-400i’s beam is precise to 3 mm and accurate to 5 mm at a distance of 100 m with a beam divergence of 0.35 milliradians (RIEGL LMS GmbH, 2014). The laser beam width is calculated with the following formula (White, 2010).

( ) (1)

The beam width increases in width over distance. The VZ-400i’s beam width is 1.7 cm at 50 m from the target and 12 cm at 350 m from the target. Each outcrop was scanned from multiple scan positions about 25 to 35 m away from the outcrop with 12 to 15 m spacing between scan positions to ensure data coverage in occluded areas. An array of cylindrical and disk reflectors mounted on tripods around each outcrop served as a reference frame to tie the scans together.

a N 10m

b 90° rotation

10m

The scanner and camera are operated from a laptop connected to the scanner equipped with

RIEGL’s RiSCAN PRO software. Scanning the outcrops at a high resolution consists of four main steps: a panorama scan, selecting reflector, reflector fine scan and an outcrop fine scan

(White, 2010; Minisini et al., 2014). Upon beginning a new scan position, the scanner is set up on a tripod and tilted to ensure the camera pictures cover the outcrop’s extent (Minisini et al.,

2014). Scanning starts with a low-resolution 360˚ panorama scan to get an overview of the scanner’s surroundings. The panorama scan is done at a .08˚ stepping angle, which results in a point spacing of 4 cm at 30 m from the scanner. Point spacing is calculated using the formula below (White, 2010; UNAVCO, 2013).

( ) (2)

The point spacing determines the surface resolution of the surface model. The smaller the point spacing, the higher the resolution of the model. The panorama scan is viewed to manually select reflectors, which serve as the control points. RiSCAN PRO offers an automatic method of searching for reflectors, though this can often pick up car reflectors and traffic signs. After selecting reflectors, each reflector is scanned at a high resolution to determine the exact center of each reflector, or the tie point. The tie point coordinates serve as the reference frame that ties the scans together. Finally, the outcrop is scanned at a higher resolution than the panorama scan to capture a greater level of detail. Each outcrop is scanned at a stepping angle between .02˚ and

.03˚. This results in 1 to 1.5 cm point spacing at a distance of 30 meters. This process is repeated at each scan position for consistency.

The data from a single scan is mapped in the scanner’s own coordinate system (SOCS)

(RIEGL LMS GmbH, 2013). This is a right-handed Cartesian coordinate system with the

scanner’s sensor located at the origin. Using the reflectors to tie the scans together transforms the data to project coordinate system (PRCS). This allows users to view the merged scans together, though the data is not georeferenced. Importing the reflector’s Global Positioning System (GPS) coordinates georeferences the data, transforming it to global coordinate system (GLCS) (Figures

3.4 and 3.5.a-b).

reflectors: red rectangles, grey positions: Scan point east cloud. on 4. Paleozoic the Fracture Looking .

Figure 3 Figure contain data. no areas Black circles.

CHAPTER 4

GNSS CONTROL

Due to logistics, an on-the-fly kinematic GNSS survey was utilized to position the scans at centimeter precision (Wyloe and Featherstone, 1995). Two HiPerLite dual frequency receivers are used to conduct the GNSS survey, with one serving as the base station and the other as the rover (El-Rabbany, 2002; Singels, 2012). On-the-fly kinematic GNSS surveys require the rover to occupy positions for tens of seconds up to two minutes while maintaining lock on the same satellites as the local base station (El-Rabbany, 2002). The receiver serving as the base station is set to collect measurements every 10 seconds. The HiPerLite units receive GPS and GLONASS signals to improve the accuracy of the collected data (Topcon, 2007). GPS is the US satellite system, while GLONASS is the Russian satellite system (El-Rabbany, 2002).

Prior to the start of the survey, the receiver acting as a local base station is set on a tripod in the vicinity of the survey area. After initializing the base station, it is left running for at least 15 minutes to download the satellite almanac before recording data (Topcon, 2007). Once initializing the rover, it is held stationary for a few minutes to lock on to four or more satellites before recording data. The rover occupies each reflector for two minutes while noting the number of visible satellites. The base station records data for two hours or more. The base station positions are corrected with Continuously Operating Reference Stations (CORS) using the

Online Positioning User Service (OPUS) provided by the National Geodetic Survey (Rizos,

2007; National Geodetic Survey, 2016). Following the survey, the base and rover positions are post processed with Topcon Tools. Static post processing of the base position in OPUS results in a horizontal Root Mean Square (RMS) of .001 m and a vertical RMS of .002 m at the Paleozoic

outcrop (Figure 4.1.a-b). The RMS is higher on the monzonite outcrops, with the horizontal

RMS averaging at .007 m and the vertical RMS at about .02 m. The monzonite outcrops are surrounded by steep cliffs, which reduces satellite visibility. The Monzonite Fracture South base station position is outside the polygon of reference CORS stations, since the CORS station in

Esmeralda County, Nevada is inactive. The base station position is post processed by referencing a CORS station 10 km away. (Figure 4.2 a-d). One of the reflector baselines for Monzonite

Fracture North appears red since the RMS value is less than .02 m (Figure 4.3.a-b). This is due to satellites being blocked by steep surrounding cliffs.

80m N

a b

73 m

a b

8.8 km

c d

85 m

a b

Figure 4.3. a. The GNSS positions of the base station and reflectors of the Monzonite Fracture North outcrop. b. The GNSS positions on Google Earth.

CHAPTER 5

MOUNT CALIBRATION AND VEGETATION REMOVAL

The point clouds are tied together with stationary reflectors that serve as a reference frame for the project. This results in an RMS of 1 to 2 cm between scan positions. The photos from the mounted camera are used to colorize the point clouds, since the original scans do not include color information (RIEGL LMS GmbH, 2013). To colorize the point cloud from photos from the mounted camera, a mounting calibration must be assigned to the photos. The mount calibration is a 4x4 transformation matrix that transforms the RGB pixel color from the Camera Coordinate

System (CMCS) to the Scanner’s Own Coordinate system (SOCS) thus determining which RGB value corresponds to which point (RIEGL LMS GmbH, 2013). Since the camera’s position slightly differs from the original calibration position, the mounting calibration has to be adjusted each time the camera is mounted on the scanner, to reduce errors in the pixel coordinates. Not adjusting the mount calibration results in errors where the pixel color is “shifted” from the features in the point cloud (Figure 5.1.a-b) (RIEGL LMS GmbH, 2013; UNAVCO, 2013).

Adjusting the mounting calibration ensures that draped photos match features on the finished, draped model. Figure 5.2 illustrates how readjusting the mount calibration reduces the error from tens of pixels (Several decimeters on the model) to 1 to 3 pixels. It is recommended to adjust the mounting calibration before starting the photo draping process.

a b

Figure 5.1. a. A side by side comparison of the reduction of pixel error before. b. After performing a mount calibration. Note how the “tp004” marker appears in the center of the reflector.

Figure 5.2. The results of a mount calibration.

The VZ-400i is fitted with a Nikon D700 camera with two lenses, a 20 mm lens and an 85 mm lens. The photorealistic model’s image detail is dependent on the camera’s pixel resolution.

Pixel resolution is calculated with the formula below (White, 2010).

( ) ( ) ( ) (3) ( )

Using the formula above, the Nikon D700’s pixel dimension is 8.45 μm (Mansurov, 2012).

At 25 m from the target, the pixel resolution for the 20 mm lens is about 10.6 mm per pixel compared to about 2.5 mm per pixel with the 85 mm lens (White, 2010; Mansurov, 2012). Since the 85 mm lens offers a higher pixel resolution than the 20 mm lens, it is the lens selected for capturing imagery. Due to the smaller field of vision of the 85 mm lens, only the top half of the point cloud is colorized, but the resolution is about four times better than that of the 20 mm lens.

After readjusting the mount calibration and coloring the point cloud, GNSS coordinates from the reflectors are imported to georeference the project. As a result of georeferencing the project, features extracted from the photorealistic models are positioned and geo-oriented properly.

Vegetation is cleaned off in the point cloud before exporting due to meshing issues with vegetation (White, 2010). Though vegetation was sparse, it is primarily hidden in crevices and small corners. Vegetation is manually cleaned up in a merged view with all scan positions visible to reduce having to remove the same feature more than once. Once the vegetation is removed, the data is exported in ASCII format in project coordinate system. Each exported file contains x, y, z coordinates and RGB values.

CHAPTER 6

GENERATING THE TIN AND THE PHOTOREALISTIC MODEL

After exporting the point cloud, the next step involves creating the TIN mesh with InnovMetric’s

PolyWorks software (InnovMetric Software Inc., 2007). It is recommended to remove the vegetation before importing into PolyWorks to avoid creating “crumpled aluminum foil” features

(Figure 6.1), the result of meshing vegetation. Due to the branching nature of vegetation, it does not mesh well, resulting in “crumpled aluminum foil” features. Users may notice holes in the mesh, these are either due to occluded data in the point cloud or holes from the removed vegetation (Figure 3.2.a-c). These holes can later be completely or partially filled during the hole filling process.

Figure 6.1. Close-up of the mesh exhibiting the surface artefact. Green circles: artefacts, red circles: “crumpled aluminum foil” features.

An interpolation grid is created from the point cloud, which is used to create the mesh. The scanning pattern from the VZ-400i in the detailed scans creates “ridge” artefacts on the mesh surface (Figure 6.1). This effect is removed by randomly subsampling 45% to 50% of the

original point cloud before creating the interpolation. Each fine scan contains between 1 to 4 million points. The immense amount of data results in TIN meshes with 4 to 5 million triangles each. Meshes this large do not load properly on viewing software, OpenSceneGraph and

ArcScene are only able to view TIN meshes with less than 1 to 1.5 million triangles. The

Monzonite Fracture South outcrop contains approximately 1.3 million triangles, though is still prone to crashing on ArcScene. Optimizing the mesh reduces the number of triangles through

PolyWorks’ IMCompress operation. This removes triangles over flat surfaces while retaining triangles over complex surfaces (Figure 6.2.a-b) (“PolyWorks V10 beginner’s guide,” 2007).

This works by entering a reduction percentage and incrementally reducing the mesh size. It is recommended to reduce the mesh to under 1 million triangles. Reducing the mesh to 10,000 triangles or less will result in loss of significant detail.

At this point, a decision is made whether to export the mesh or do additional cleanup.

Cleanup of the mesh involves removing leftover vegetation, hole filling and removing intersecting triangles. Hole filling is optional, and usually done for easier viewing or cosmetic reasons. Filling small holes closely approximates the real structure and makes the finished model easier to view. It is advisable to not fill large holes to avoid creating artefacts such as “balloons” and “mushrooms”. After cleaning and editing the mesh, it is exported as an OBJ Wavefront file.

The export yields two files: a Material Template Library (MTL) file and an OBJ. The OBJ file contains the triangle vertices’ UV coordinates and vertex normals of the triangles (White, 2010).

The MTL file contains information on the texture of the OBJ file. The PolyWorks export does not remove the RGB values from the OBJ, causing viewing difficulties in OpenSceneGraph. It is recommended to remove the RGB values from the OBJ to resolve this issue.

After exporting the OBJ, photos are draped onto the surface model with software developed by Geological & Historical Virtual Models LLC (GHVM LLC) (White, 2010). First, photos are selected from the images taken from the scanner-mounted camera. Photos for draping are selected based on their perpendicularity to the outcrop to reduce pixel smearing. The more skewed (the less perpendicular) the photo is to the surface, the greater the smearing effect. Pixel smearing is directly proportional to the angle of the photograph with respect to the model (White,

2010). The degree of pixel smearing is represented by the equation below.

(4)

The MTL file for the photodraped model will contain names of the images used in the draping and the color information for undraped triangles (White, 2010). Photos taken by the coaxial camera are distorted due to the optical error from the lens. Photos can either be left distorted or undistorted in RiSCAN Pro. After selecting photos, the draping process consists of four steps; creating the INI files, creating the PFLA/PFLD files, prescreening triangles for occluded geometry, and creating the photo drape list. PFLA/PFLD are photo data files used by

GHVM suite software for draping images onto surface models. The “A” and “D” are part of the file extension that denote files for distorted or distorted corrected photos. The extension PFLA stands for distortion corrected photos and PFLD represents undistorted photos. The INI files are created by clicking “Registration” on the top toolbar and clicking on “Multiple COP Export” in

RiSCAN PRO. The INI files contain the Camera Orientation and Position (COP), the mounting calibration and the Sensor’s Orientation and Position (SOP) matrices. The COP is a 4x4 transformation matrix that specifies the orientation of the camera relative to the SOCS. The SOP is a matrix that specifies the orientation and position of the scanner’s sensor Using file explorer,

navigate to the exported INI files, which can be found under the photo folder, and copy them into a single folder. The PFLAD program converts the INI files into PFLA or PFLD files. If working with undistorted photos, the INI files are exported into PFLD files. After creating the PFLD or

PFLA files, the OBJ triangles are prescreened for occluded geometry or hidden triangle surfaces.

This is to avoid draping photos on hidden triangles. After prescreening triangles, a list of photos is created in a TXT file. Photos are listed by filename and extension. The listed placement determines the order in which the photos are draped; i.e., the first photo is draped first. Photos are draped by mapping the vertices of triangles from the TIN mesh onto the photos using the orientation parameters from the PFLA/PFLD files (White, 2010). After the draping processed is finished, the updated MTL file will appear with the image names and the RGB value for undraped triangles.

cleaning the mesh. b. The mesh after hole filling, and reducing the the reducing filling, The b. and hole mesh. the after mesh cleaning

point same the to feature. arrows hite

a before mesh TIN of the omparison

Figure 6.2. 6.2. C a. Figure 5.3 from W count milliontriangle to million. 1.3

b a

CHAPTER 7

DIGITIZING FRACTURES ON PHOTOREALISTIC MODELS

After draping images onto the model, it is converted into segmented (Virtual Reality

Modeling Language) VRML files and imported into ArcScene, a 3D viewing application where users can overlay layers on 3D features (White, 2010; Esri, 2016). Fractures are digitized with

Geo Analysis Tools (GAT) an ArcScene add-on developed by GHVM LLC to delineate fracture lengths, sedimentary beds and extract fracture orientations on the photorealistic model (Figure

7.1) (White, 2010). It is important to note that users should have an understanding of geologic principles such as bedding plane orientations, folds, bed thickness, etc. when using tools such as

GAT.

Figure 7.1. GAT’s Sedimentary Analysis Tools is utilized for outlining fractures and sedimentary beds. Red: Fractures, Blue-green: fault slip surface.

1 m 0.1 m [

a b

Figure 7.2. a. From a distance, the fracture appears clearly defined (blue arrow). b. Up close, (blue arrow) part of the fracture is not discernable. Meter stick for scale.

Fractures are defined as surfaces where there is a break or separation between surfaces where they can exhibit high relief and continuity across outcrops (Twiss and Moores, 2007). Fractures are identified by their surface and erosional expression. The resolution of the draped images aids in identifying fractures that exhibit little to no surface expression. Fractures less than 10 cm long and thinner than a few millimeters are smaller than the pixel resolution and are not measured.

When digitizing fractures, it is important to avoid digitizing fractures where the fracture surface is not clearly defined (Figure 7.2.a-b). The high relief of the photorealistic surface models provides a 3D cut into the fracture plane. When obtaining orientation measurements, it is best to use fractures mapped in deeply incised rock surfaces (Figure 7.3.a, c). The mapped surface trace of a high relief straight fracture tends to fit a plane within a 10% variance, allowing users to get an orientation measurement representative of the fracture’s behavior (Figure 7.3.b, d). If the mapped fracture surface trace is curved or does not exhibit enough relief, the resultant fitted plane has a variance of 20% or more. At a variance of 20% or more, the fitted plane is not representative the fracture’s orientation (Figure 7.4).

1 m 1 m

a b

1 m 1 m

c d

Fractures are categorized into two main categories: extension and shear fractures. Shear fractures are identified by indicators of motion with respect to the fracture’s surface. Indicators of shear fractures are offset sedimentary beds and veins and the presence of shear structures such as: Riedel shears, sigma and delta structures (Twiss and Moores, 2007). The outcrop models are also examined for sigma and delta structures to denote whether the deformation occurred in a ductile or brittle regime (Twiss and Moores, 2007).

Figure 7.4. GAT’s Structural Geology tools calculate the fracture’s orientation from the points placed on the fracture’s surface expression, assuming that fracture is a planar feature. It is not possible to obtain an orientation on nonplanar fractures or fractures that exhibited a change in direction.

CHAPTER 8

RESULTS

The fractures are digitized across two different lithologies within the FLVFZ: one outcrop in the metasediments and two outcrops in the monzonite. As seen in Figure 2.4, the Paleozoic model is located on the main active strand of the Fish Lake Valley Fault. The monzonite outcrops are located on nearby fault strand that was previously active, but later buried by succeeding sediments. A comparison is made between the fracture patterns in the two lithologies to examine whether they exhibit similar fracture patterns. Fracture and fault orientations are plotted as planes or poles to planes on stereonets to discern patterns of fault orientations

(Leyshon and Lisle, 1996). When plotting fracture orientations and densities, it is preferable to use a Lambert equal area projection.

25m a

b N

Figure 8.1. a. An overall view of the Paleozoic Fracture model. b. A close-up view of the fracture zone. Red: fractures, green: sedimentary beds.

PALEOZOIC FRACTURE OUTCROP

The southern section of the Paleozoic outcrop consists of fractured rock which gradually transitions into cataclasite in the north as seen in Figure 8.1.a. As the fractured rock transitions into cataclasite the beds become impossible to distinguish. No fractures surfaces were observed in the cataclasite. The beds are visible in the fractured section of the outcrop due to their alternating colors and erosional relief (Figure 8.1.b). Several of the sedimentary beds in the fractured section of the Paleozoic outcrop exhibit offset. The fractures in the Paleozoic outcrop consist of mode I and II fractures, evidenced by offset sedimentary beds (Figure 3.1.a) (Twiss and Moores, 2007). Offset fractures were mostly vertical, with no fractures shallower than 27 degrees (Figure 8.2.a-b). The fractures displayed an apparent offset between 10 and 30 cm. No deformed veins or sigma and delta structures were observed, indicating that the deformation occurred in a brittle regime. All the fractures mapped in the Paleozoic outcrop tend to dip towards the south (Figure 8.2.c-d). In the northern section of the outcrop the sedimentary beds are heavily fractured, tilted, and deformed; some are almost indistinguishable.

a b c

up of the boulders in the cataclasite boulders the of up cataclasite the in -

up of thefractures in the fractured monzonite.b. close A -

the of on is northern the side rock fractured of Monzonite SouthThe trix.outcrop. Fracture the overview An c.

Figure 8.3. 8.3. close A a. Figure ma fault slip slip point to surface, surface: fault the arrows blue in is cataclasite the red, Fractures: south. the and outcrop blue.

MONZONITE FRACTURE SOUTH

The monzonite east of the Fish Lake Valley Fault contains alternating zones of fractured monzonite and cataclasite. Both the Monzonite Fracture North and Monzonite Fracture South outcrops contain sections of fractured monzonite cliffs and cataclasite with scattered granite boulders several meters across (Figure 8.3.b). Moving north across the Monzonite Fracture South outcrop, the monzonite abruptly transitions into cataclasite (Figure 8.3.c). The outcrop contains several dark “veins” of resistant material, which cut vertically across the outcrop (blue lines in

Figure 8.3.a). These “veins” are actually slickenlines formed by the friction of fault slip movement. The slickenside surfaces in the monzonite fracture south outcrop exhibit Riedel shears. Measurements of the slickenside surfaces are taken on-site (Figure 8.4.a) and on the photorealistic model (Figure 8.4.b) for comparison. The Riedel shears indicate that the faults in the monzonite fracture south outcrop have a reverse component of motion (Oldow, 2017 personal conversation) (Figure 8.4.a). The ground measurements are congruent with the measurements on the photorealistic model. Surrounded by cataclasite, the slickenlines are roughly parallel to the abrupt transition from the fractured rock to the cataclasite.

a b

Figure 8.4. a. Ground measurements taken on-site from the slickenside surface (blue lines in Figure 8.3.c) compared to measurements taken on the photorealistic model. b. Stereonet measurements taken on the photorealistic model.

Fractures were plotted on a stereonet in increments of 10 degrees (dip) to discern orientation trends (Figure 8.5.a-i). Fractures dipping between 0 and 40 degrees plot in a bulls-eye cluster

(Figure 8.6.a). These are 10 cm to 2 m long continuous fractures exhibiting some splays (Figure

8.3.a). Most of the subhorizontal fractures strike southeast and dip towards the southwest. These fractures are likely exfoliation features that developed during exhumation. The fractures dipping between 40 and 90 degrees are between 20 cm to 1m in length and are often cut by subhorizontal fractures (Figure 8.6.b). The fractures from the boulders surrounded by cataclasite are 7 to 60 cm long with some fractures reaching 1 m. The horizontal fractures on the boulders dip in all four directions (Figure 8.7.a-b) while the vertical fractures on the boulders dip northeast and southwest (Figure 8.7.c).

a b c

d e f

g h i

Figure 8.5. a. Stereonet plots of fractures from the Monzonite Fracture South outcrop. Fractures dipping from 0 to 9 degrees. b. Fractures dipping from 10 to 19 degrees. c. Fractures dipping from 20 to 29 degrees. d. Fractures dipping from 30 to 39 degrees. e. Fractures dipping from 40 to 49 degrees. f. Fractures dipping from 50 to 59 degrees. g. Fractures dipping from 60 to 69 degrees. h. Fractures dipping from 70 to 79 degrees. i. Fractures dipping from 80 to 90 degrees.

a b

Figure 8.6. a. Fractures dipping from 0 to 39 degrees. b. Fractures dipping from 40 to 90 degrees.

a b c

Figure 8.7. a. Fractures dipping from 0 to 29 degrees. b. Fractures dipping from 30 to 59 degrees. c. Fractures dipping from 60 to 90 degrees.

MONZONITE FRACTURE NORTH

In the Monzonite Fracture North outcrop (Figure 8.8.a-b), the cataclasite gradually transitions into fractured monzonite and cataclasite. Like the Monzonite Fracture South outcrop, this outcrop displays more horizontal fractures than vertical fractures (Figure 8.9.a-b). The outcrop is more weathered on the southern section than the Monzonite Fracture South outcrop. Most fractures dipping between 30 and 60 degrees are cut by horizontal fractures. Discontinuous fractures appear to be offset but do not exhibit offset features.

b N

Figure 8.8. a. An overview of the Monzonite Fracture North outcrop. b. A close-up of the fracture zone. Fractures in red, meterstick for scale.

a b

Figure 8.9. a. Stereonet plots of fractures dipping from 0 to 29 degrees. b. Stereonet fracture plots of fractures dipping from 30 to 90 degrees.

The monzonite outcrops did not exhibit deformed veins, sigma or delta structures; fractures appear to be mode 1 fractures. While the monzonite fracture south outcrop has a fault surface with Riedel shears, the rest of the fractured sections of the monzonite fracture north and south outcrops do not display shear structures. Though multiple fractures intersect and appear offset, none of the fractures exhibit offset (Twiss and Moores, 2007). Several of the diorite inclusions exhibit fracturing, but do not display offset.

CHAPTER 9

CONCLUSION AND DISCUSSION

The brittle fractures and lack of shear structures in the monzonite and Paleozoic outcrops indicate that the rocks underwent brittle deformation via the Fish Lake Valley fault at shallow depths. While several of the fractures in the Paleozoic show offset without ductile deformation, none of the monzonite outcrops contain any offset fractures. Though all three outcrops are within the FLVFZ, the fracture patterns between the Paleozoic and the monzonite are different. The

Paleozoic outcrop is dominated by steep fractures, with no fractures shallower than 27 degrees

(Figure 8.2.a-d). Both offset and non-offset fractures in the Paleozoic dip northeast or northwest, while a few dip southward. In contrast, more than half of the fractures in the monzonite are subhorizontal (between 0 and 40 degrees). Majority of the subhorizontal fractures mapped in the fractured section of the monzonite south outcrop strike southeast and dip southwest (Figure

8.6.a). The different fracture orientations observed in the Paleozoic and monzonite outcrops determine that the fracture patterns are different between differing lithologies, despite the outcrops’ location in the same fault zone. The subhorizontal fractures from the boulders in the monzonite south outcrop (Figure 8.7) and the monzonite north outcrop dip in all directions

(Figure 8.9). The fractures in the fractured rock and boulders of the monzonite south outcrop share similar patterns in their stereonet plots. The subhorizontal fractures (between 0 and 40 degrees) plot in a bull’s eye at or close to the center. Also, the steeply dipping fractures (between

60 and 90 degrees) in the fractured rock and cataclasite boulders dip northeast and southwest

(Figure 8.6.b and Figure 8.7.c). This indicates that the boulders probably did not rotate during

deformation, as the fracture patterns would have been different. Also, the subhorizontal fractures observed in the monzonite outcrops are likely exfoliation fractures.

In sum, this work is an example of utilizing a photorealistic model to rectify measurements on the model and field measurements. The fault slip surfaces in the monzonite south outcrop are mapped on the model and in the field. Field measurements determine that the fault has a reverse component, indicating that the fault is likely a reverse fault (Figure 8.4.a). The fault surface orientations from the photorealistic model closely match the field measurements.

Photorealistic models of fault zones allow geologists to precisely digitize fracture patterns on outcrops, enabling them to interpret the deformational behavior of the rocks in a fault zone. 3D photo draped surface models in combination with fieldwork are necessary for digitizing fractures in a fault zone. Geologists can correlate field measurements with measurements on the model or re-visit the site virtually to add new information. Users can also navigate around the model and extract measurements that are georeferenced. Photorealistic models by no means replace traditional fieldwork; they serve to augment the information that can be extracted from the models and fieldwork (Xu, 2000; McCaffrey et al., 2010). Utilizing a georeferenced model enables geologists to rectify field measurements with data from photorealistic models and build a comprehensive understanding of a study area (Minisini et al., 2014; Palmer, 2015).

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BIOGRAPHICAL SKETCH

Rebecca (Becky) Aguilar was born in Mexico City, Mexico in 1991, just in time for her mother’s birthday. In 1996 at the age of 5, she immigrated to the United States, not speaking a word of

English. While going to school in Texas she learned English while maintaining her native

Spanish at home. Throughout middle and high school, she displayed an affinity for Mathematics,

Natural Sciences, Music and visual arts. After graduating from Plano East Senior High School in

2009, she attended The University of Texas at Dallas. She received her degree in Geosciences with a minor in visual arts in 2013. In the fall of 2013, she began her studies at the University of

Texas at Dallas as a Master’s student in Geosciences. Her hobbies include, fossil hunting, reading about interesting topics, and drawing comics in her spare time.

CURRICULUM VITAE

Rebecca Jessica Aguilar

Master of Science in Geosciences August 2017

Speetra June 2013 - present

Wrote and designed whitepapers for distribution to potential clients/customers. Filmed and edited promotional videos for product mockups and demonstrations. Designed flyers and handouts in Illustrator and InDesign for Speetra-sponsored events such as TEDxSMU and numerous events for Dallas Business Journal.

3D data capture and LIDAR graduate course Fall 2014 - present

Managed training and scanning operations for running TLS surveys with a RIEGL VZ400i scanner and a RIEGL LPM800. Directed multiple TLS surveys in the Arbuckle Mountains OK, which involved collecting GPS and surveying control points with a Topcon Imaging Station.

Dinosaur Park, Alberta Canada TCU July 2014

Ellison Miles Project July 2014

Collaborated with a multidisciplinary Geosciences team to develop a kinematic model of Fish

Lake Valley. Directed and operated TLS scans in a square km area utilizing the RIEGL VZ400i and LMS-Z 620 scanners in southern Fish Lake Valley, Nevada. Processed and constructed three photorealistic models of fracture/cataclasite zones. This also included deriving 3D orientations and characteristics of fractures within the fault zones.

Bachelor of Science in Geosciences May 2013

Minored in Visual Arts

A Modest Proposal September 2011 - May 2013

Supervised contributors in assigning articles and illustrations. Developed skills with Photoshop,

InDesign and Illustrator to create layouts and graphics.

Conferences attended:

SPAR 3D April 2014, April 2015, April 2017,

Imaging & Geospatial Technology Forum (IGTF) April 2016

Geological Society of America October 2013, October 2015