View in EDS

Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.

Saved in:
Bibliographic Details
Title: Artificial neural network-based ground motion prediction equations for Sichuan-Yunnan area of China.
Authors: Cui, Jianwen1,2 (AUTHOR), Lu, Dawei3 (AUTHOR) ludawei@chinarecrm.com.cn, Xu, Shuo2 (AUTHOR), Lin, Guoliang2 (AUTHOR), Shen, Yahong2 (AUTHOR), Cui, Zhihao4 (AUTHOR)
Source: Acta Geophysica. Dec2025, Vol. 73 Issue 6, p5257-5277. 21p.
Subject Terms: *Artificial neural networks, *Ground motion, *Seismograms, *Geographical positions, *Seismology
Geographic Terms: China, Sichuan Sheng (China), Yunnan Sheng (China)
Abstract: This study presents a novel ground motion prediction equation (GMPE) for the Sichuan-Yunnan region of China based on artificial neural networks (ANNs). Utilizing data from 207 earthquake events and 3537 ground motion recordings collected since 2007, the ANN-based GMPEs predict peak ground acceleration (PGA) and pseudo-spectral acceleration (PSA) for periods ranging from 0.04 to 6.0 s. The model incorporates five key parameters: magnitude, epicentral distance, site conditions (represented by the predominant period T 0 derived from horizontal-to-vertical spectral ratios HVSR), hypocentral depth, and styles of faulting. A weighted loss function is employed during ANN training to address data imbalances, particularly the scarcity of near-field recordings. Residual analyses indicate that both inter-event and intra-event variabilities fall within acceptable limits. Comparisons with observed ground motions and existing GMPE commonly used in China confirm that the proposed ANN-based GMPE effectively captures key ground motion characteristics. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Description
Abstract:This study presents a novel ground motion prediction equation (GMPE) for the Sichuan-Yunnan region of China based on artificial neural networks (ANNs). Utilizing data from 207 earthquake events and 3537 ground motion recordings collected since 2007, the ANN-based GMPEs predict peak ground acceleration (PGA) and pseudo-spectral acceleration (PSA) for periods ranging from 0.04 to 6.0 s. The model incorporates five key parameters: magnitude, epicentral distance, site conditions (represented by the predominant period T 0 derived from horizontal-to-vertical spectral ratios HVSR), hypocentral depth, and styles of faulting. A weighted loss function is employed during ANN training to address data imbalances, particularly the scarcity of near-field recordings. Residual analyses indicate that both inter-event and intra-event variabilities fall within acceptable limits. Comparisons with observed ground motions and existing GMPE commonly used in China confirm that the proposed ANN-based GMPE effectively captures key ground motion characteristics. [ABSTRACT FROM AUTHOR]
ISSN:18956572
DOI:10.1007/s11600-025-01597-3