Broadband Ground Motion Synthesis by Diffusion Model with Minimal Condition

TL;DR

This paper introduces HEGGS, a diffusion-based model that efficiently generates realistic 3D earthquake ground motion waveforms across multiple regions, aiding seismic risk assessment and mitigation.

Contribution

The paper presents HEGGS, a novel end-to-end differentiable diffusion model capable of producing high-fidelity earthquake ground motion waveforms with minimal conditioning.

Findings

HEGGS achieves superior waveform realism across diverse datasets.

The model trains efficiently on a single GPU.

Generated waveforms accurately match seismological criteria.

Abstract

Shock waves caused by earthquakes can be devastating. Generating realistic earthquake-caused ground motion waveforms help reducing losses in lives and properties, yet generative models for the task tend to generate subpar waveforms. We present High-fidelity Earthquake Groundmotion Generation System (HEGGS) and demonstrate its superior performance using earthquakes from North American, East Asian, and European regions. HEGGS exploits the intrinsic characteristics of earthquake dataset and learns the waveforms using an end-to-end differentiable generator containing conditional latent diffusion model and hi-fidelity waveform construction model. We show the learning efficiency of HEGGS by training it on a single GPU machine and validate its performance using earthquake databases from North America, East Asia, and Europe, using diverse criteria from waveform generation tasks and seismology. Once trained, HEGGS can generate three dimensional E-N-Z seismic waveforms with accurate P/S phase arrivals, envelope correlation, signal-to-noise ratio, GMPE analysis, frequency content analysis, and section plot analysis.