Complete three-dimensional near-field surface displacements from imaging geodesy techniques applied to the 2016 Kumamoto earthquake

Abstract The recent development of imaging geodesy, an advanced technique with a high spatial resolution and large-scale coverage, has enabled researchers to obtain multiple high-quality surface displacement estimates at low labor-cost, thereby improving the capability to monitor and manage geological disasters. The different sources (e.g., radar, optical and LiDAR sensors) and analysis approaches (e.g., differential interferometric synthetic aperture radar, DInSAR; multiple-aperture InSAR; pixel offset tracking; and iterative closest point, ICP) in imaging geodesy used to derive displacement estimates have unique benefits and drawbacks. However, the inherent differences among these data sources and methods in the construction of three-dimensional (3D) deformation maps, particularly in the near field, remain poorly understood and require further discussion. In this study, we acquired three pairs of ALOS-2 stripmap mode images, two pairs of Sentinel-1 TOPS mode images and pre- and post-event LiDAR data for the 2016 Kumamoto earthquake to explore the 3D near-field displacements using various imaging geodesy techniques with different types of image information, i.e., SAR phase data, SAR amplitude data and LiDAR point cloud data. Our results show that each image type is independently capable of producing a high-quality 3D deformation map for the 2016 Kumamoto earthquake with an on-fault accuracy of