SCEC Award Number 21074 View PDF
Proposal Category Individual Proposal (Integration and Theory)
Proposal Title Earthquake accelerogram synthesis with conditional Generative Adversarial Networks
Name Organization
Zachary Ross California Institute of Technology
Other Participants
SCEC Priorities 4c, 4a, 4b SCEC Groups GM
Report Due Date 03/15/2022 Date Report Submitted 03/14/2022
Project 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 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.
Intellectual Merit We developed a deep learning approach to synthesizing earthquake ground motion records, which can be used for various aspects of engineering seismology.
Broader Impacts The project supported a postdoctoral scholar, Manuel Florez-Torres, who is from an underrepresented STEM group. The project can help to reduce seismic hazard by generating more reliable ground motion time series for engineering applications.
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