Computational Approach for Improving Three-Dimensional Sub-Surface Earth Structure for Regional Earthquake Hazard Simulations in the San Francisco Bay Area

Technical Report · Mon Sep 25 04:00:00 EDT 2017

DOI:https://doi.org/10.2172/1396195· OSTI ID:1396195

Rodgers, A. J. ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

In our Exascale Computing Project (ECP) we seek to simulate earthquake ground motions at much higher frequency than is currently possible. Previous simulations in the SFBA were limited to 0.5-1 Hz or lower (Aagaard et al. 2008, 2010), while we have recently simulated the response to 5 Hz. In order to improve confidence in simulated ground motions, we must accurately represent the three-dimensional (3D) sub-surface material properties that govern seismic wave propagation over a broad region. We are currently focusing on the San Francisco Bay Area (SFBA) with a Cartesian domain of size 120 x 80 x 35 km, but this area will be expanded to cover a larger domain. Currently, the United States Geologic Survey (USGS) has a 3D model of the SFBA for seismic simulations. However, this model suffers from two serious shortcomings relative to our application: 1) it does not fit most of the available low frequency (< 1 Hz) seismic waveforms from moderate (magnitude M 3.5-5.0) earthquakes; and 2) it is represented with much lower resolution than necessary for the high frequency simulations (> 5 Hz) we seek to perform. The current model will serve as a starting model for full waveform tomography based on 3D sensitivity kernels. This report serves as the deliverable for our ECP FY2017 Quarter 4 milestone to FY 2018 “Computational approach to developing model updates”. We summarize the current state of 3D seismic simulations in the SFBA and demonstrate the performance of the USGS 3D model for a few selected paths. We show the available open-source waveform data sets for model updates, based on moderate earthquakes recorded in the region. We present a plan for improving the 3D model utilizing the available data and further development of our SW4 application. We project how the model could be improved and present options for further improvements focused on the shallow geotechnical layers using dense passive recordings of ambient and human-induced noise.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC52-07NA27344
• OSTI ID: 1396195
• Report Number(s): LLNL--TR-739167
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES