Abstract
We present a method for determining 3-dimensional, local ground displacements caused by an earthquake. The technique requires pre- and post-earthquake point cloud datasets, such as those collected using airborne Light Detection and Ranging (Lidar). This problem is formulated as a point cloud registration problem in which the full point cloud is divided into smaller windows, for which the local displacement that best restores the post-earthquake point cloud onto its pre-earthquake equivalent must be found. We investigate how to identify the size of window to be considered for registration. We then present an information theoretic approach that classifies whether a region contains an earthquake fault. These methods are first validated on simulated earthquake datasets, for which the input displacement field is known, and then tested on a real earthquake. We show results and error analyses for a variety of different window sizes, as well as results for our fault detection algorithm.
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Krishnan, A.K., Nissen, E., Saripalli, S., Arrowsmith, R., Corona, A.H. (2013). Change Detection Using Airborne LiDAR: Applications to Earthquakes. In: Desai, J., Dudek, G., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 88. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00065-7_49
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DOI: https://doi.org/10.1007/978-3-319-00065-7_49
Publisher Name: Springer, Heidelberg
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