Work

Simulation of near-fault strong ground motion with generative adversarial network

Year: 2025

Type: article

Abstract: This paper proposes a generative adversarial network (GAN) for feature extraction and simulation generation of seismic data. Specifically, a Wasserstein-distance-based GAN model has been adopted using the well-acknowledged NGA-West2 ground motion American database of the Pacific Earthquake Engineering Research Center (PEER). Notably, this study adopts multi-dimensional convolution/deconvolution to extract and reconstruct multi-dimensional features from one-dimensional data, ensuring the generated data exhibit required features simultaneously. The validity and diversity of the generated data are verified by four aspects, such as time–frequency power spectrum, data similarity, and seismic intensity parameter distribution map. The results show that the generated data by the proposed method have good and diverse features and waveforms.

Source: Mechanical Systems and Signal Processing

DOI: 10.1016/j.ymssp.2025.113648

Authors: Mengxue YANG, Siyu ZHU

Institution: Xihua University

Language: English

Topic: Seismic Performance and Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,730

Funders: National Natural Science Foundation of China, Xihua University