Machine-Learning Methods for Earthquake Ground Motion Analysis and Simulation

Abstract

This paper presents a novel method of data-based probabilistic seismic hazard analysis (PSHA) and ground motion simulation, verified using previously recorded strong-motion data and machine-learning techniques. The procedure consists of three parts: (1) selection of an orthonormal set of basis vectors called eigenquakes to represent characteristic earthquake records; (2) estimation of response spectra for the anticipated level of shaking for a scenario earthquake at a site using Gaussian process regression; and (3) optimal combination of the eigenquakes to generate time series of ground acceleration consistent with the response spectral ordinates obtained in the second part. The paper discusses the benefits of applying such machine-learning methods to strong-motion databases for PSHA and ground motion simulation, particularly in large urban areas where dense instrumentation is available or expected. The effectiveness of the proposed methodology is exhibited using four scenario examples for downtown Los Angeles. Advantages, disadvantages, and future research needs for this machine-learning approach to PSHA are discussed.

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