High-performance adaptive local kriging applied to recovering surface deformation associated with the fault zones

Differential Interferometric Synthetic Aperture Radar (DInSAR) is an effective technique to measures the surface displacement caused by strong earthquakes. In our previous work an adaptive local kriging (ALK) was proposed to recover surface deformation associated with the fault zones. The calculation of ALK needs a huge computing power. Thus a high-performance computing is needed. It can not only speedup the interpolation processes of ALK but also handle large volumes of widely distributed remote sensing dataset. As a result, a parallel image interpolation approach, referred to as the graphics processing unit (GPU) based ALK method, to the slant range motion maps derived by DInSAR is proposed in this paper. It makes use of the performance profiling to analyze the serial version of ALK and perform a parallel GPU computation for reducing the computation time efficiently. By employing the NVIDIA TITAN GPU, the proposed method achieves a speedup of 77.86× compared to its CPU counterpart part.