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Poster #171, SCEC Community Models (CXM)

Comparison of ground motions in earthquakes with SCEC CVM predictions

Brianna Birkel, John E. Vidale, Alan Juarez, & Thomas H. Jordan
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Poster Presentation

2020 SCEC Annual Meeting, Poster #171, SCEC Contribution #10615 VIEW PDF
Southern California, and particularly the Los Angeles region, contains strongly heterogeneous crustal structures that have been carefully studied, mapped, and integrated into the SCEC Community Velocity Models (CVMs). Although the CVMs of Southern California are widely used for simulations of wave propagation and as a reference for many studies, their shortcomings in predicting ground motions are well recognized, especially in reproducing basin effects at high frequencies. The most recent SCEC model, CVM-S4.26, was inverted from waveform data at frequencies below 0.2 Hz. Our objective is to develop a model that can better predict higher frequency ground motions, which is a SCEC-stated priori...ty in physics-based hazard assessment. Using the forward modeling of several moderate magnitude earthquakes that recently occurred in the Los Angeles region, we construct synthetic-data comparisons that characterize the inaccuracies in the CVM-S4.26 predictions up to about 0.5 Hz, focusing on features associated with the boundaries of the deep sedimentary basins. Our next step will be to use the 3D waveform modeling to identify coherent groups of seismic energy that can be accurately measured and have path-localized Fréchet kernels with good sensitivity to basin and near-surface structures. From these waveforms, we plan to extract phase and amplitude data up to 0.5 Hz or higher. Our goal is to invert these data using full-3D tomography and thereby improve the predictive power of SCEC’s CyberShake ground-motion forecasts.
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Citation: Birkel, B., Vidale, J. E., Juarez, A., & Jordan, T. H. (2020, 08). Comparison of ground motions in earthquakes with SCEC CVM predictions. Poster Presentation at 2020 SCEC Annual Meeting.
Keywords: CVM, simulation, forward modeling, validation
SCEC Award: 20187