MCPSHA: A New Tool for Probabilistic Seismic Hazard Analysis Based on Monte Carlo Simulation

Abstract

The utilization of the Monte Carlo method in conjunction with probabilistic seismic hazard analysis (PSHA) constitutes a compelling avenue for exploration. This approach presents itself as an efficient and adaptable alternative to conventional PSHA, particularly when confronted with intricate factors such as parameter uncertainties and diverse earthquake source models. Leveraging the Monte Carlo method and drawing from the widely adopted Cornell-type seismicity model in engineering seismology and disaster mitigation, as well as a seismicity model capturing temporal, spatial, and magnitude inhomogeneity, we have derived a formula for the probability of earthquake intensity occurrence and the mean rate of intensity occurrence over a specified time period. This effort has culminated in the development of a MATLAB-based program named MCPSHA. To assess the model’s efficacy, we selected Baoji City, Shaanxi Province, China, as our research site. Our investigation delves into the disparity between occurrence probability and extreme probability (a surrogate commonly employed for occurrence probability) in the Baoji region over the next 50 years. The findings reveal that the Western region of Baoji exhibits a heightened hazard level, as depicted in the maps, which illustrate a 10% probability of exceedance within a 50-year timeframe. The probability of earthquake occurrence under various intensities (VI, VII, and VIII) over 50 years follows a declining trend from west to east. Furthermore, the likelihood of seismic intensity exceeding VI, VII, and VIII indicates the lowest exceeding probability in the northeast and the highest in the northwest. Notably, for intensities VI-VII, the difference between occurrence probability and extreme probability approaches twice, gradually diminishing with increasing intensity. This study underscores the MCPSHA model’s efficacy in providing robust technical support for mitigating earthquake risk and enhancing the precision of earthquake insurance premium rate calculations.