Data-driven Accelerogram Synthesis using Deep Generative Models

TL;DR

This paper introduces a deep generative model based on Wasserstein GANs to synthesize realistic earthquake accelerograms conditioned on physical parameters, enabling on-demand generation for engineering applications.

Contribution

The paper presents a novel GAN-based framework for conditioned accelerogram synthesis, extending existing models to incorporate physical variables and interpolate unseen conditions.

Findings

The model accurately reproduces statistical features of real accelerograms.

Synthesized waveforms display realistic P and S-wave characteristics.

Peak Ground Acceleration estimates align with observed data.

Abstract

Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic 3-Component accelerograms conditioned on magnitude, distance, and $V_{s30}$. Our model captures the expected statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized Peak Ground Acceleration (PGA) estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process's stability and tune model hyperparameters. We further show that the trained generator network can interpolate to conditions where no earthquake ground motion recordings exist. Our approach allows the on-demand synthesis of accelerograms for engineering purposes.