Parallel Dislocation Model Implementation for Earthquake Source Parameter Estimation on Multi-Threaded GPU

Abstract

Graphics processing units (GPUs) have been in the spotlight in various fields because they can process a massive amount of computation at a relatively low price. This research proposes a performance acceleration framework applied to Monte Carlo method-based earthquake source parameter estimation using multi-threaded compute unified device architecture (CUDA) GPU. The Monte Carlo method takes an exhaustive computational burden because iterative nonlinear optimization is performed more than 1000 times. To alleviate this problem, we parallelize the rectangular dislocation model, i.e., the Okada model, since the model consists of independent point-wise computations and takes up most of the time in the nonlinear optimization. Adjusting the degree of common subexpression elimination, thread block size, and constant caching, we obtained the best CUDA optimization configuration that achieves 134.94×, 14.00×, and 2.99× speedups over sequential CPU, 16-threads CPU, and baseline CUDA GPU implementation from the 1000×1000 mesh size, respectively. Then, we evaluated the performance and correctness of four different line search algorithms for the limited memory Broyden–Fletcher–Goldfarb–Shanno with boundaries (L-BFGS-B) optimization in the real earthquake dataset. The results demonstrated Armijo line search to be the most efficient one among the algorithms. The visualization results with the best-fit parameters finally derived by the proposed framework confirm that our framework also approximates the earthquake source parameters with an excellent agreement with the geodetic data, i.e., at most 0.5 cm root-mean-square-error (RMSE) of residual displacement.

Surface deformation simulation for InSAR detection using a machine learning approach on the hantangang river volcanic field: A case study on the orisan mountain

“…In its application the algorithm is generally used in the case of infinite non-linear optimization, although this algorithm can be developed for constrained problems. Some of the region of belief methods used include the trust-region reflective and the Dogleg method (Lee and Kim, 2021). The second step is the iteration process using the Monte-Carlo restart method to obtain a linear inversion and avoid falling into local minima during the iteration process (Achmad et al, 2020).…”

“…Besides, classification on amplitude SAR images also has been combined using machine learning approaches for analyzing forest condition or object detections (Lapini et al, 2020;Garg et al, 2021;Li et al, 2022). In terms of application on InSAR data, several machine learning approaches has been employed such as optimization parameter on source deformation using cluster algorithms (Lee and Kim, 2021), volcanic deformation detection (Ghosh et al, 2021;Milillo et al, 2022). Several developments have been made on the InSAR method to increase its effectiveness and reliability in recent years, such as modeling the source of deformation or exploration (Iio and Furuya, 2018;De Novellis et al, 2019).…”