Data-driven synthesis of broadband earthquake ground motions using artificial intelligence

Manuel A. Florez, Michaelangelo Caporale, Buabthong Pakpoom, Zachary E. Ross, Domniki Asimaki, & Men-Andrin Meier

Published August 13, 2021, SCEC Contribution #11374, 2021 SCEC Annual Meeting Poster #038 (PDF)

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 Vs30. Our model captures most of the relevant 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.

Citation
Florez, M. A., Caporale, M., Pakpoom, B., Ross, Z. E., Asimaki, D., & Meier, M. (2021, 08). Data-driven synthesis of broadband earthquake ground motions using artificial intelligence. Poster Presentation at 2021 SCEC Annual Meeting.

Related Projects & Working Groups
Ground Motions