SCEC Award Number 22021 View PDF
Proposal Category Individual Proposal (Data Gathering and Products)
Proposal Title Improving Knowledge of Seismic Velocity Structure in the San Bernardino Basin Area
Investigator(s)
Name Organization
Clifford Thurber University of Wisconsin, Madison
Other Participants Hao Guo, postdoc
Erin Cunningham, postdoc
SCEC Priorities 3b, 4a, 4b SCEC Groups Seismology, CXM, CS
Report Due Date 03/15/2023 Date Report Submitted 04/01/2023
Project Abstract
The main goals of our work under SCEC award #22021 have been (1) evaluation and augmentation of our current Southern California seismic data set with data that has been collected in and around the San Bernardino Basin (SBB) and San Gabriel Basin (SGB) by other SCEC investigators, (2) applying an improved joint body wave-surface wave inversion code with the additional data to the SBB-SGB area, and (3) evaluation of the new model relative to previous models using wavefield simulations.
Intellectual Merit We have developed a new seismic velocity model for the San Bernardino Basin (SBB) and San Gregorio Basin (SGB) using new;y available data. The significant improvements of the joint body wave-surface wave inversion, both in terms of misfit reduction and waveform goodness of fit, indicate that the Guo 2023 model better represents the seismic velocity structure beneath the SBB-SGB area than previous models.
Broader Impacts Synthetic seismograms derived from our new model better replicate observed waveforms than the previous models tested. The implication is that our new model would yield better ground motion estimates for seismic hazard estimation.
Exemplary Figure Figure 7
Quantitative waveform comparison using the time-frequency misfit analysis. We compare the Guo 2023 (presented here) model with the CVM-H model for different events and stations. Black lines indicate the observed data, cyan the Guo 2023 model, and red the CVM model. Most stations show an envelope fit improvement and substantial improvement in vertical component (Z) the phase misfit.