A Fault Sources-Based Probabilistic Seismic Hazard Analysis for Next-Generation Seismic Ground Motion Parameters Zonation Map of China

Abstract

The probabilistic seismic hazard analysis (PSHA) method used in existing seismic ground motion parameters zonation map of China (the traditional PSHA-CN method) is based on a two-dimensional area seismic source framework and does not account for the rupture dimension of large earthquakes, which may lead to underestimation of seismic hazard at near-fault sites. By employing stochastic sampling to integrate three-dimensional fault sources and two-dimensional area seismic sources, a new PSHA-CN method was developed in recent years, but it faces limitations in accuracy and computational efficiency due to sampling constraints, particularly for low probability of exceedance scenarios or large earthquakes with long return periods. To enhance the computational efficiency of the new PSHA-CN method, this study developed a novel spatial integration algorithm for PSHA. The algorithm considers rupture dimension, enables efficient fault geometry modeling using the Frankel Fault Surface (FFS) and Stirling Fault Surface (SFS) models, and maintains compatibility with the traditional PSHA-CN framework. Validation against test cases from the Pacific Earthquake Engineering Research Center (PEER) demonstrated the algorithm’s reliability. Furthermore, the algorithm was applied to assess seismic hazard in the Changsha-Zhuzhou-Xiangtan metropolitan region in Hunan Province to validate its performance in regions with moderate seismic activity in China. A comparative analysis of the new algorithm results with those of the traditional PSHA-CN method revealed that the PSHA-CN method underestimates near-fault seismic hazards. The proposed algorithm will be implemented in next-generation seismic ground motion parameters zonation map in China.

Keywords

China / Probabilistic seismic hazard analysis / Seismic ground motion parameters zonation map / Seismic source / Three-dimensional fault source / Earth Sciences / Geophysics / Information and Computing Sciences / Artificial Intelligence and Image Processing

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