Change Detection Using Airborne LiDAR: Applications to Earthquakes

We present a method for determining 3-dimensional, local ground displacements caused by an earthquake. The technique requires preand 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. Aravindhan K Krishnan Arizona State University, USA e-mail: [email protected] Edwin Nissen Arizona State University, USA e-mail: [email protected] Srikanth Saripalli Arizona State University, USA e-mail: [email protected] Ramon Arrowsmith Arizona State University, USA e-mail: [email protected] Alejandro Hinojosa Corona Carretera Ensenada-Tijuana, Mexico e-mail: [email protected]