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Resolving Ridgecrest complex deformation modes characterized with differential LiDAR topography

Emmons McKinney, Adam Wade, Christopher M. Madugo, & Ozgur Kozaci

Published August 15, 2019, SCEC Contribution #9873, 2019 SCEC Annual Meeting Poster #225

Pre- and post-earthquake Airborne LiDAR-derived point clouds with a density of ~26 points/m2 were collected by Pacific Gas and Electric Company (PG&E) along sections of the M6.4 July 4 and M7.1 July 5 2019 surface ruptures and mapped Quaternary faults near Ridgecrest, California. Three-dimensional point cloud differential analyses were performed at three sites to help characterize the magnitude, style and width of surface deformation near utility infrastructure. Point clouds were differenced by applying Iterative Closest Point (ICP) methods using (1) bulk samples with CloudCompare and (2) as a moving statistical window in MATLAB (Scott et al., 2018). At the first site, along the 6.4 rupture, field mapping documented 0.3 m left lateral offset across a single fault strand and a 118m wide zone of secondary fractures with no discernible lateral and vertical separation. Point cloud differencing showed left lateral displacement and decimeter scale uplift on a pressure ridge. At the second site, field mapping documented 0.45 m of right lateral slip across two closely spaced primary faults and a 110m wide zone of fractures with no resolvable offset. Point cloud difference showed right lateral displacement and local bedrock uplift. The third site, located near mapped Quaternary faults ~4 km north-west of the 6.4 rupture, was characterized in the field by a broad zone of northwest oriented fractures with no measurable offset. Point cloud difference in this area showed the region effected by distributed deformation. Overall, in this region with extensive cracking of desert floor, LiDAR differencing serves to inform the larger pictures but is no replacement for ground truth. Advantages include the ability to image smaller and further afield change than the measurable offsets seen across ruptures. Detectable signals agree with tectonic geomorphology and inform gentler distributed deformation calculations.

Key Words
Ridgecrest, Lidar differencing, infrastructure, utility

McKinney, E., Wade, A., Madugo, C. M., & Kozaci, O. (2019, 08). Resolving Ridgecrest complex deformation modes characterized with differential LiDAR topography. Poster Presentation at 2019 SCEC Annual Meeting.

Related Projects & Working Groups
Ridgecrest Earthquakes