Aug 20, 2012 - namesake Spanish conquistador who entered what is now the United States via the nearby San Pedro Valley in the. 1540s. Evidence exists in ...
Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave,...
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Aug 20, 2012 Earth Science
Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave, Coronado National Memorial, Cochise County, AZ
John Lyons-Baral
Article Author(s): John Lyons-Baral Introduction Ninety-five miles southeast of Tucson, Arizona, and partway up the steep slope of Montezuma Peak, lies Coronado Cave (Figure 1). The cave is located in Coronado National Memorial, which pays tribute to its namesake Spanish conquistador who entered what is now the United States via the nearby San Pedro Valley in the 1540s. Evidence exists in the park for historic and prehistoric human presence, but none has been found in the cave to date (Graham, 2011). To access the cave, one must hike half a mile up a steep trail before climbing the limestone steps to the blocky entrance. The cave is mostly straight, heading west to east, with some undulations along the way. A short scramble down large Figure 1: Map showing the geology (Hayes and blocks leads into the first and largest room of Coronado Raup, 1968) and location of Coronado Cave (CC) Cave. The room is up to 10 meters high with a diameter in southwestern Cochise County, Arizona. of about 30 meters. Large to small blocks litter the floor of this room (Figure 2). As you continue along, the passage tapers down, with a small, deeply incised and sinuous side channel on the right. Just past this feature, a hole marks the entrance to a crawl passage that can be followed on hands and knees for about 25 meters before it dead ends in a plug of sediment.
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Back in the main passage, the other side of the cave has large hanging fins of dissolutional and erosional features. Continuing down the main passage, a section of “narrows” in the middle of the cave – a one meter wide section between steeply dipping bedding planes – leads to the other large room. Unlike the first room, there are few large blocks on its floor. The room gives the appearance of being a large underground sports arena, with a smooth oval-shaped floor and ceiling. This room also contains some speleothems; two large stalagmites (Figure 3) mark the entrance and two large columns (Figure 4) mark the east end of the cave. A steep climb to a gated back entrance and some short crawl passages are the last destinations in Coronado Cave.
Figure 2: Large fallen blocks in the first, western-most room.
Figure 3: Stalagmites at the entrance of the large east room.
Figure 4: Large columns at the east end of Coronado Cave.
Geology of Coronado Cave The geologic story of Coronado Cave is a multifaceted one, spanning three main episodes: Paleozoic formation of the limestone host rock, Jurassic intrusion of granite displacing and altering the host rock, and Quaternary speleogenesis and cave passage evolution. Coronado Cave is a limestone solution cave formed in the Paleozoic Naco Group limestone of southeastern Arizona. The cave’s host rock formed approximately 250 to 300 million years ago. Although the Jurassic (200 to 145 million years ago) Montezuma Caldera, in which Coronado National Memorial completely resides, produced collapse megabreccia with Paleozoic blocks up to 1 km in length, the block housing Coronado Cave is simply a roof pendant resting on top of the intruded Jurassic Huachuca Granite. The cave formed during the Quaternary period (2.6 million years ago to the present) through the process of acidic groundwater dissolution of the carbonate rock mass. Three factors contributed to the sizable rooms of Coronado Cave: carbonic acid from atmospheric and soil carbon dioxide; sulfuric acid from the iron sulfides in the contact
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Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave,...
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skarn along the margins of the intruded granite (Figure 5); and an estimated prehistoric groundwater flow rate of 50,000 gallons per minute (Graham, 2011; Hon and Lipman 1994; Hon et al. 2007).
Figure 5: Entrance to Coronado Cave. The limestone host rock is clearly bounded on the uphill side by a change in lithology, possibly skarnification because the Huachuca granite is exposed nearby, or perhaps another formation of the Naco Group. next ›
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Aug 20, 2012 Earth Science
Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave, Coronado National Memorial, Cochise County, AZ
John Lyons-Baral
Article Author(s): John Lyons-Baral A Role for LiDAR (Light Detection And Ranging) in Preserving Coronado Cave Preservation of Coronado Cave and creation of a cave management plan are primary geologic issues for Coronado National Memorial in its Geologic Resources Inventory Report (Graham, 2011). Due to its ease of access, the cave suffers from excessive dust, habitat degradation, broken and stolen speleothems and graffiti (Graham, 2011). In order to help the National Park Service protect and preserve the cave, we conducted terrestrial laser scanning (TLS or ground-based LiDAR) and a geomechanical assessment of the cave as a graduate research project for an Underground Geomechanics class. A TLS point cloud image provides detailed spatial mapping of natural and cultural resources amenable for future change detection research. A fly-through animation is planned as part of a virtual cave tour at the memorial visitor center for those unable to reach the cave in person. Importantly, geomechanical kinematic and numerical modeling analyses of the TLS product should yield insight into current and future cave breakdown processes that can inform park decisions to better protect cavern visitors.
Figure 6: Stitched point cloud image of the complete Coronado Cave decimated to 3 cm resolution.
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TLS
was chosen as a remote sensing method to collect geologic structural data and geometric measurements due to its ability to reach Coronado Cave’s high walls and roofs, and for the laser’s capability of piercing total darkness. Biosphere 2 loaned us a Leica ScanStation C10 TLS, with a range of 300 meters and a scan rate of up to 50,000 points per second (Leica, 2012). A team of six people conducted the scanning: W. Henderson and D. Millar from Biosphere 2; J. Mateljak and D. Schlichting from Coronado National Memorial; and R. Davenport and J. Lyons-Baral from the University of Arizona. In eight hours, we made13 separate medium resolution scans that were later stitched together into a continuous point cloud image of the approximately 200 m long by 10 m high by 20 m wide cave (Figure 6). Alternate scan positioning from one side of the cave to the other allowed for multiple perspectives to reduce shadowing effects and minimize data loss. The scanner was also placed strategically to obtain images of side passages whenever possible. Six spherical survey targets (Figure 7) were placed and leap-frogged through the cave length as the survey progressed to allow for efficient stitching of the individual scans during image processing. With an approximate average of 1 point per centimeter, using overlapping scans and given the close proximity of the walls, roof, and floor, we successfully captured high resolution images of almost every geologic surface and fracture. Although TLS surveys require relatively short field time for the quantity of data collected, processing the imaging data can require a significant amount of time and a battery of computing software. The first step was registering the scanned images and stitching the thirteen separate point clouds into one continuous 12.2 GB cloud using the scanner’s accompanying software. To ease the difficulty of working with the 12.2 GB file, the point cloud was divided into smaller sections based on location and rock mass similarities. Another more manageable image was created by decimating the stitched image down to a resolution of one point for every three centimeters.
Figure 7: Looking west through a narrow cave passage following the steeply dipping bedding with spherical survey target.
Further data processing using geotechnical point cloud processing software allowed for delineation of geologic fracture orientations with stereonets plots (Figure 8) for use in kinematic analyses (Split Engineering, 2012). Cave passage cross-sections were also produced for numerical modeling analysis (Figure 9). Other rock mass properties generated during point cloud processing included: joint spacing, block sizes, fracture persistence, rock quality designation (RQD), waviness and roughness/dilation angle. A field rock strength testing device, a Schmidt Hammer, was used to estimate the unconfined compressive strength (UCS) of intact rock, while lab tests were conducted to determine the density. We conducted kinematic and numerical modeling for sections of the cave hosting relatively recent fallen blocks. The west room (near the main entrance, see Figure 6), with fallen roof blocks up to 7x3x2.5 meters (Figure 9) and a scree slope under the north wall (Figure 8), was chosen to analyze for mass wasting potential.
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Figure 8: a) LiDAR point cloud image of section of cave wall, b) triangular mesh created from points, c) smooth fracture surface delineation creating “patches”, d) stereonet plot of patches and grouping into joint sets.
Figure 9: Relatively less stable west room cross-section with sagging roof beams and large fallen blocks present on floor.
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Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave,...
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Aug 20, 2012 Earth Science
Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave, Coronado National Memorial, Cochise County, AZ
John Lyons-Baral
Article Author(s): John Lyons-Baral For the kinematic analysis, joint sets generated by point cloud processing were used in a geological orientation graphical and statistical analysis program (Rocscience, 2011) to evaluate the probability of toppling from the north wall as the main reason for the talus/scree slope. A moderately strong joint friction angle of 40° was chosen due to calcite cementation in some joints. Based on the analysis shown in Figure 10 below, we feel certain that toppling failures produce regular rockfall under this overhanging face. Two-dimensional numerical modeling (Rocscience, 2011) analysis using a point cloud cross-section yields insight into collapse patterns. Based on the shallow depth of the cave and present-day conditions, the initial modeling results showed very little stress, deformation or fractured rock beams. Because the cave is proximal to the epicenter of the 7.4 magnitude, 1887 northeastern Sonoran, Mexico, earthquake, and because shallow caves can be impacted by seismicity, an analysis with a seismic coefficient of 0.1 was chosen based on seismic probability maps (Natali and Sbar, 1982; www.edgetech-us.com, 2012; Figure 10: Kinematic toppling analysis for the north wall of the www.landfilldesign.com, 2012; Melo and west room. Sharma, 2004). After adding a seismic coefficient, the results changed significantly – separation of roof beams showed failure and displacements up to 25 cm (Figure 11).
Figure 11: Displacement plot of the western cave room after the Stage 2 seismic analysis.
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As a check on the accuracy of our analysis of Coronado Cave, the geometric measurements of Coronado Cave’s roof span and hydraulic radius were plotted on an underground mining Modified Stability graph (Johnson et al., 2000) to assess its general stability. The cave falls on the boundary of the stable and transition zones (Fig. 12). This indicates that underground rooms of these dimensions are mostly stable but transition into the region where cave-ins occur. This assessment is congruent with the presence of fallen blocks present and supports the 2D stress and deformation modeling results.
Our research indicates that Coronado Cave is presently mostly stable, but sizable earthquakes could lead to rock failure. The modeling results displayed low stresses and deformations under present in situ conditions, but failure was demonstrated during modeling of a moderate seismic event. Only the kinematic analysis for toppling from the walls of the north face of the west room showed current instability in the form of small block falls forming a scree slope; this appears to be the only section of the cave where visitors should avoid prolonged exposure. Further field work will be conducted to obtain a more thorough evaluation by collecting more direct rock mass properties such as friction angles and joint infill materials. Future research will attempt to determine the rates of current and future cave breakdown processes; this is the primary scientific objective of this project. Through the use of time-dependent fracture mechanics methods, probabilities of rock failure can be projected into the Figure 12: Modified Stability Graph (Johnson et future based on the current condition of the intact rock and of the rock bridges in the joints that bind rock beams al., 2000) with red lines plotting Coronado together. One of the challenges retrieving accurate results Cave’s west room at the boundary of the Stable and Transition Zones. with time-dependent failure predictions is definitively characterizing partially concealed rock bridges that support rock beams. We propose that modal frequency measurements of rock beams in combination with 3D stress analysis modeling can be used to determine rock bridge geometries and distributions along joints that currently support beams (Kemeny, 2004; Kemeny and Kim, 2009). Accelerometer readings have already been recorded for vibrational analysis of the rock formations and beams in the cave (Figure 13), and the geometries required for accurate 3D modeling of critical rock beams can be easily obtained from the LiDAR data. In addition to using future failure calculations to determine cave breakdown rates, establishing the failure times of the existing fallen blocks could allow for correlation and calibration of the fracture mechanics models being tested. The challenge here is the difficulty of dating fracture surfaces of breakdown blocks.
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Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave,...
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LiDAR has proved valuable in obtaining abundant and accurate data for use in the stability analysis of Coronado Cave. Repeated scans in caves over time can yield mass wasting, depositional and erosional rates of change, increasing our knowledge of cave evolution. Cave scans can be tied to the surface and incorporated into Geographic Information Systems (GIS) databases for mapping and spatial analysis. As TLS devices become smaller and more mobile, more constricted and longer caves can be studied. Research using LiDAR in caves in conjunction with GIS spatial analysis of the surface and subsurface could provide insight into how and where cave-related karst features and hazards manifest both in the subsurface and on the surface. Figure 13: Author placing accelerometer for measuring induced vibrations in loose, hanging bedding plane.
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Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave,...
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Arizona Geological Survey | Staff | Archived Issues
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Aug 20, 2012 Earth Science
Using terrestrial LiDAR to map and evaluate hazards of Coronado Cave, Coronado National Memorial, Cochise County, AZ
John Lyons-Baral
Article Author(s): John Lyons-Baral References Graham, J. 2011. Coronado National Memorial: Geologic Resources Inventory Report. Natural Resource Report NPS/NRSS/GRD/NRR—2011/438. National Park Service, Fort Collins, Colorado. http://nature.nps.gov/geology/inventory/publications/reports/coro_gri_rp... Hayes, P.T. and Raup, R.B., 1968, Geologic map of the Huachuca and Mustang Mountains, southeastern Arizona: U.S. Geological Survey, Miscellaneous Geologic Investigations Map I-509, scale 1:48000.: U.S. Geological Survey, Miscellaneous Geologic Investigations Map I-509, Hon, K.A. and P. Lipman. 1994. Jurassic calderas in southeastern Arizona – the surface manifestation of a composite batholith. Circular C 1103-A. U. S. Geological Survey, Reston, Virginia, USA. Hon, K.A., F. Gray, K.S. Bolm, K.A. Dempsey, and P.A. Pearthree. 2007. A digital geologic map of the Miller Peak, Nicksville, Bob Thompson Peak, and Montezuma Pass Quadrangles, Arizona. U.S. Geological Survey Scientific Investigations Map, SIM unpublished (scale 1:24,000). Johnson, R., Quaye, G.B. and Roberts, M.K.C. 2000. Stability and support requirements for stope backs in the shallow depth mining of steeply dipping vein/tabular deposits. CSIR Mining Technology Kemeny, J. 2004. Time-dependent drift degradation due to the progressive failure of rock bridges along discontinuities. International Journal of Rock Mechanics & Mining Sciences 42 (2005). Pp. 35–46. Kemeny, J. and Kim, C. 2009. Increasing our understanding of time-dependent rock mass behavior with ground-based LIDAR, 3D discontinuum modeling, and fracture mechanics. Rock Mechanics, Fuenkajorn & Phien-wej (eds) © 2009. Pp. 35–53. Leica Geosystems. 2012. Leica ScanStation C10, http://hds.leica-geosystems.com/en/LeicaScanStation-C10_79411.htm; Leica Cyclone, http://hds.leica-geosystems.com/en/LeicaCyclone_6515.htm. Accessed on July 2012. Melo, C. and Sharma, S. 2004. Seismic coefficients for pseudostatic slope analysis. 13th World Conference on Earthquake Engineering, Vancouver, B.C., Canada, August 1-6, 2004. Paper No. 369. Natali, S.G. and Sbar, M.L. 1982. Seismicity in the epicentral region of the 1887 Northeastern Sonoran Earthquake, Mexico. Bulletin of the Seismological Society of America, Vol. 72, No. 1. Pp. 181-196. Rocscience. 2011. Phase 2 version 8.0, Finite Element Analysis for Excavations; Dips version 5.1, Graphical and Statistical Analysis of Orientation Data. http://www.rocscience.com. Split Engineering. 2012. Split FX Point Cloud Processing Software, http://www.spliteng.com/split-fx/.
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