Three-dimensional surface displacements and rotations from differencing pre-and post-earthquake LiDAR point clouds

The recent explosion in sub-meter resolution airborne LiDAR data raises the possibility of mapping detailed changes to Earth's topography. We present a new method that determines three-dimensional (3-D) coseismic surface displacements and rotations from differencing pre-and post-earthquake airborne LiDAR point clouds using the Iterative Closest Point (ICP) algorithm. Tested on simulated earthquake displacements added to real LiDAR data along the San Andreas Fault, the method reproduces the input deformation for a grid size of ∼50 m with horizontal and vertical accuracies of ∼20 cm and ∼4 cm, values that mimic errors in the original spot height measurements. The technique also measures rotations directly, resolving the detailed kinematics of distributed zones of faulting where block rotations are common. By capturing near-fault deformation in 3-D, the method offers new constraints on shallow fault slip and rupture zone deformation, in turn aiding research into fault zone rheology and long-term earthquake repeatability.