Exciting news! We're transitioning to the Statewide California Earthquake Center. Our new website is under construction, but we'll continue using this website for SCEC business in the meantime. We're also archiving the Southern Center site to preserve its rich history. A new and improved platform is coming soon!

Group A, Poster #227, Ground Motions

Ground Motion Synthesis via Generative Adversarial Network Operators: A Paradigm Shift from Simulated Broadband Ground Motions?

Domniki Asimaki, Yaozhong Shi, Grigorios Lavrentiadis, & Zachary E. Ross
Poster Image: 

Poster Presentation

2022 SCEC Annual Meeting, Poster #227, SCEC Contribution #12258 VIEW PDF
We present a novel approach for developing on-demand synthetic ground motions for engineering applications. Leveraging the increase of ground-motion data from seismic networks, the availability of physics-based ground motion simulations, and recent advancements in Machine Learning, we train a Generative Adversarial Network Operator (GANO) (Rahman et al., 2022) to produce realistic three-component acceleration time histories conditioned on moment magnitude, rupture distance, Vs30, tectonic environment type of earthquakes. Neural operators allow for the sampling of functions by learning push-forward operator maps in infinite-dimensional spaces, rendering our model resolution-invariant.

We verify the architecture by training the GANO on ground motions produced by stochastic time-domain and frequency-domain approaches (Boore 2003; Thrainsson and Kiremidjian 2002; Bayless and Abrahamson 2019). We illustrate that the proposed framework can recover the imposed magnitude, distance, and duration scaling. We then harvest 50K events of M 4.5 to 8.0 from the KiK-Net strong-motion dataset and train the GANO for shallow crustal and subduction events. Qualitative comparison of our results to empirical ground-motion models (GMMs) for spectral accelerations, duration, Arias intensity, and phase derivative distributions suggests so far that the GANO-generated time histories capture scaling relationships in the 0.1 to 50 Hz frequency range, which is important for their adoption in engineering applications.

Potential applications of the presented framework include: (i) generation of design ground motions for earthquake scenarios not adequately represented in empirical datasets, (ii) generation of risk-targeted ground motions, and (iii) spatial interpolation of recorded ground motions for the risk analysis of distributed infrastructure systems.

Bayless, J., & Abrahamson, N. A. (2019). Summary of the BA18 ground‐motion model for Fourier amplitude spectra for crustal earthquakes in California. BSSA 109(5), 2088-2105.
Boore, D. M. (2003). Simulation of ground motion using the stochastic method. PAGEOPH, 160(3), 635-676.
Thrainsson, H., & Kiremidjian, A. S. (2002). Simulation of digital earthquake accelerograms using the inverse discrete Fourier transform.EESD, 31(12), 2023-2048.
Rahman, M. A., M. A. Florez, A. Anandkumar, Z. E. Ross, and K. Azizzadenesheli, 2022, Generative Adversarial Neural Operators, arXiv:2205.03017