Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling

TL;DR

This paper introduces CGM-GM, an AI-based simulator that synthesizes detailed earthquake ground motions by learning complex wave physics and Earth heterogeneities, aiming to improve seismic hazard assessments.

Contribution

The paper presents a novel probabilistic autoencoder-based model that captures complex ground motion physics without explicit physics constraints, outperforming existing empirical models.

Findings

CGM-GM outperforms state-of-the-art empirical models in simulations.

The model effectively captures high-frequency and spatially continuous waveforms.

Evaluation on San Francisco data demonstrates its potential for seismic hazard assessment.

Abstract

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose a novel artificial intelligence (AI) simulator, Conditional Generative Modeling for Ground Motion (CGM-GM), to synthesize high-frequency and spatially continuous earthquake ground motion waveforms. CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, learning complex wave physics and Earth heterogeneities, without explicit physics constraints. This is achieved through a probabilistic autoencoder that captures latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. CGM-GM demonstrates a strong potential for outperforming a state-of-the-art non-ergodic empirical ground motion model and shows great promise in seismology and beyond.