Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published February 2022 | public
Journal Article

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression


Ground‐motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free‐field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground‐motion recording instrumentation. In this study, given a set of observed motions, ground‐motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics‐based simulated ground motions of the 1906 M_w 7.9 San Francisco earthquake and M_w 7.0 Hayward fault scenario earthquake in northern California. The method's performance is further evaluated using the 2019 M_w 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground‐motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long‐period content of the ground motions as well as directivity pulses.

Additional Information

© 2022 Seismological Society of America. Manuscript received 21 February 2021. Published online 14 September 2021. This study was partially supported by the University of California, Los Angeles (UCLA) Graduate Fellowship to the first author, which is gratefully acknowledged. Partial supports of the National Science Foundation (Award Number 2025310), California Department of Transportation and Pacific Gas & Electric Company are also fully appreciated. Arthur Rodgers' work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE‐AC52‐07NA27344. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the supporting agencies. The authors would like to also thank Robert Graves for providing the simulated ground‐motion dataset of the 1906 event and Tadahiro Kishida for his efforts in organizing and processing it. Sean Ahdi and Pengfei Wang have kindly assisted in the estimation of V_(S30) values at the recording stations of the 2019 Ridgecrest earthquake. The authors also benefitted from constructive discussions with Silvia Mazzoni. The comments from two BSSA anonymous reviewers are greatly appreciated. DATA AND RESOURCES. The 1906 M_w 7.9 San Francisco earthquake simulated ground motions were provided by Robert W. Graves (Aagaard et al., 2008). The RotD50 and orthogonal directions linear response spectra of the ground motions were constructed using the R package for computation of earthquake ground‐motion response spectra (Wang et al., 2017), which is accessible through https://peer.berkeley.edu/peer-reports. The M_w 7.0 Hayward fault scenario earthquake simulated motions (Rodgers et al., 2019) were provided by Arthur J. Rodgers. The M_w 7.1 2019 Ridgecrest earthquake data recorded by the Community Seismic Network (CSN) were obtained from http://csn.caltech.edu/data/. The processed recorded motions for the 2019 M_w 7.1 Ridgecrest earthquake can be retrieved from https://www.risksciences.ucla.edu/nhr3/gmdata. The average shear‐wave velocity values, V_(S30)⁠, at each CSN station were provided by Pengfei Wang using the proxy‐based model (Ahdi et al., 2020). All websites were last accessed in February 2021. The authors acknowledge that there are no conflicts of interest recorded.

Additional details

August 20, 2023
March 15, 2024