Abstract
Three-dimensional (3D) surface deformation data with high accuracy and resolution can help reveal the complex mechanisms and sources of subsurface deformation, both tectonic and anthropogenic. Detailed 3D deformation data are also beneficial for maintaining the position coordinates of existing ground features, which is critical for developing and advancing global positioning technologies and their applications. In seismically active regions, large earthquakes have repeatedly caused significant ground deformation and widespread damage to human society. However, the delay in updating position coordinates following deformation can hamper disaster recovery. Synthetic aperture radar (SAR) data allow high-accuracy and high-resolution 3D deformation measurements. Three analysis methods are currently available to measure 1D or 2D deformation: SAR interferometry (InSAR), split-bandwidth interferometry (SBI), and the pixel offset method. In this paper, we propose an approach to derive 3D deformation by integrating deformation data from the three methods. The theoretical uncertainty of the derived 3D deformations was also estimated using observed deformation data for each of these methods and the weighted least square (WLS) approach. Furthermore, we describe two case studies (the 2016 Kumamoto earthquake sequence and the 2016 Central Tottori earthquake in Japan) using L-band Advanced Land Observing Satellite 2 (ALOS-2) data. The case studies demonstrate that the proposed approach successfully retrieved 3D coseismic deformation with the standard error of ~ 1, ~ 4, and ~ 1 cm in the east–west, north–south, and vertical components, respectively, with sufficient InSAR data. SBI and the pixel offset method filled the gaps of the InSAR data in large deformation areas in the order of 10 cm accuracy. The derived standard errors for each pixel are also useful for subsequent applications, such as updating position coordinates and deformation source modeling. The proposed approach is also applicable to other SAR datasets. In particular, next-generation L-band SAR satellites, such as ALOS-4 and NASA-ISRO SAR (NISAR), which have a wider swath width, more frequent observation capabilities than the former L-band satellites, and exclusive main look directions (i.e., right and left) will greatly enhance the applicability of 3D deformation derivation and support the quick recovery from disasters with significant deformation.Graphical
Highlights
The ground surface can deform in three dimensions (3D) over a wide range of spatial and temporal scales and amplitudes owing to various causes ranging from large-scale natural phenomena, such as earthquakes and volcanic activity, to local landslides, surficial lateral spreading, and anthropogenic subsidence
The derived 3D deformation has the standard error of ~ 1, ~ 4, and ~ 1 cm in the EW, north– south (NS), and UD components, respectively, where sufficient InSAR data are available, with no significant gaps even in high-deformation areas owing to split-bandwidth interferometry (SBI) and the pixel offset method, the accuracy is in the order of 10 cm using the highest-resolution U mode data
Two case studies demonstrated that the proposed method can successfully retrieve accurate 3D coseismic deformations with theoretical standard errors comparable to the levels of uncertainty resulting from a comparison with external global navigation satellite system (GNSS), in situ offset measurement, and leveling data
Summary
The ground surface can deform in three dimensions (3D) over a wide range of spatial and temporal scales and amplitudes owing to various causes ranging from large-scale natural phenomena, such as earthquakes and volcanic activity, to local landslides, surficial lateral spreading, and anthropogenic subsidence. When an earthquake occurs and these existing features are significantly displaced, their location information (i.e., 3D position coordinates) should be updated based on the magnitude of the displacement, for which accurate and high-resolution 3D deformation data are required. Every time a significant deformation occurs, the 3D position coordinates of control points (i.e., triangulation points and GNSS Continuously Operating Reference Stations (CORS)) and heights of benchmarks within the deformed area are updated (e.g., Hiyama et al 2011; Nojiri et al 2019; Ootaki et al 2016). Field surveys (i.e., GNSS or leveling) of all existing control points or benchmarks are required when or where complicated deformation cannot be retrieved owing to incomplete coverage by the GNSS CORS network. Pointwise observations may miss unknown deformation signals between control points
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