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Title: Strong correlation between stress drop and peak ground acceleration for recent M1–4 earthquakes in the San Francisco Bay Area

Abstract

Theoretical and observational studies suggest that between-event variability in the median ground motions of larger ( M≥5 ) earthquakes is controlled primarily by the dynamic properties of the earthquake source, such as Brune-type stress drop. Analogous results remain equivocal for smaller events due to the lack of comprehensive and overlapping ground-motion and source-parameter datasets in this regime. Here in this paper, we investigate the relationship between peak ground acceleration (PGA) and dynamic stress drop for a new dataset of 5297 earthquakes that occurred in the San Francisco Bay area from 2002 through 2016. For each event, we measure PGA on horizontal-component channels of stations within 100 km and estimate stress drop from P-wave spectra recorded on vertical-component channels of the same stations. We then develop a nonparametric ground-motion prediction equation (GMPE) applicable for the moderate (M 1–4) earthquakes in our study region, using a mixed-effects generalization of the Random Forest algorithm. We use the Random Forest GMPE to model the joint influence of magnitude, distance, and near-site effects on observed PGA. We observe a strong correlation between dynamic stress drop and the residual PGA of each event, with the events with higher-than-expected PGA associated with higher values of stress drop.more » The strength of this correlation increases as a function of magnitude but remains significant even for smaller magnitude events with corner frequencies that approach the observable bandwidth of the acceleration records. Mainshock events are characterized by systematically higher stress drop and PGA than aftershocks of equivalent magnitude. Coherent local variations in the distribution of dynamic stress drop provide observational constraints to support the future development of nonergodic GMPEs that account for variations in median stress drop at different source locations.« less

Authors:
ORCiD logo [1];  [2]
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  1. Univ. of California, San Diego, CA (United States). Scripps Inst. of Oceanography, Inst. of Geophysics and Planetary Physics; Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of California, San Diego, CA (United States). Scripps Inst. of Oceanography, Inst. of Geophysics and Planetary Physics
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
National Science Foundation (NSF); USDOE
OSTI Identifier:
1430024
Report Number(s):
LA-UR-18-20708
Journal ID: ISSN 0037-1106 ; 1943-3573 (Electronic)
Grant/Contract Number:  
AC52-06NA25396; DGE-1144086; 16020
Resource Type:
Accepted Manuscript
Journal Name:
Bulletin of the Seismological Society of America
Additional Journal Information:
Journal Volume: 108; Journal Issue: 2; Journal ID: ISSN 0037-1106
Publisher:
Seismological Society of America
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Trugman, Daniel Taylor, and Shearer, Peter M. Strong correlation between stress drop and peak ground acceleration for recent M1–4 earthquakes in the San Francisco Bay Area. United States: N. p., 2018. Web. doi:10.1785/0120170245.
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Trugman, Daniel Taylor, & Shearer, Peter M. Strong correlation between stress drop and peak ground acceleration for recent M1–4 earthquakes in the San Francisco Bay Area. United States. https://doi.org/10.1785/0120170245
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Trugman, Daniel Taylor, and Shearer, Peter M. Tue . "Strong correlation between stress drop and peak ground acceleration for recent M1–4 earthquakes in the San Francisco Bay Area". United States. https://doi.org/10.1785/0120170245. https://www.osti.gov/servlets/purl/1430024.
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@article{osti_1430024,
title = {Strong correlation between stress drop and peak ground acceleration for recent M1–4 earthquakes in the San Francisco Bay Area},
author = {Trugman, Daniel Taylor and Shearer, Peter M.},
abstractNote = {Theoretical and observational studies suggest that between-event variability in the median ground motions of larger ( M≥5 ) earthquakes is controlled primarily by the dynamic properties of the earthquake source, such as Brune-type stress drop. Analogous results remain equivocal for smaller events due to the lack of comprehensive and overlapping ground-motion and source-parameter datasets in this regime. Here in this paper, we investigate the relationship between peak ground acceleration (PGA) and dynamic stress drop for a new dataset of 5297 earthquakes that occurred in the San Francisco Bay area from 2002 through 2016. For each event, we measure PGA on horizontal-component channels of stations within 100 km and estimate stress drop from P-wave spectra recorded on vertical-component channels of the same stations. We then develop a nonparametric ground-motion prediction equation (GMPE) applicable for the moderate (M 1–4) earthquakes in our study region, using a mixed-effects generalization of the Random Forest algorithm. We use the Random Forest GMPE to model the joint influence of magnitude, distance, and near-site effects on observed PGA. We observe a strong correlation between dynamic stress drop and the residual PGA of each event, with the events with higher-than-expected PGA associated with higher values of stress drop. The strength of this correlation increases as a function of magnitude but remains significant even for smaller magnitude events with corner frequencies that approach the observable bandwidth of the acceleration records. Mainshock events are characterized by systematically higher stress drop and PGA than aftershocks of equivalent magnitude. Coherent local variations in the distribution of dynamic stress drop provide observational constraints to support the future development of nonergodic GMPEs that account for variations in median stress drop at different source locations.},
doi = {10.1785/0120170245},
journal = {Bulletin of the Seismological Society of America},
number = 2,
volume = 108,
place = {United States},
year = {Tue Mar 06 00:00:00 EST 2018},
month = {Tue Mar 06 00:00:00 EST 2018}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1785/0120170245
Copyright Statement
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Citation Metrics:
Cited by: 53 works
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Figures / Tables:

Figure 1 Figure 1: Map view of the San Francisco Bay area study region: M ≥ 1:0 seismicity from the relocated catalog of Waldhauser and Schaff (2008); focal mechanisms for M ≥ 3:5 events, Northern California Seismic Network station coverage (triangular symbols), and mapped fault structures (see Data and Resources) are shownmore » for reference. The color version of this figure is available only in the electronic edition.« less
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Works referencing / citing this record:

Spectral decomposition of the Engineering Strong Motion (ESM) flat file: regional attenuation, source scaling and Arias stress drop
journal, February 2020

Spatial and Temporal Variations in Earthquake Stress Drop on Gofar Transform Fault, East Pacific Rise: Implications for Fault Strength
journal, September 2018

  • Moyer, Pamela A.; Boettcher, Margaret S.; McGuire, Jeffery J.
  • Journal of Geophysical Research: Solid Earth, Vol. 123, Issue 9
  • DOI: 10.1029/2018jb015942

Earthquake Stress Drop and Arias Intensity
journal, April 2019

  • Baltay, Annemarie S.; Hanks, Thomas C.; Abrahamson, Norman A.
  • Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 4
  • DOI: 10.1029/2018jb016753

Machine learning for data-driven discovery in solid Earth geoscience
journal, March 2019

  • Bergen, Karianne J.; Johnson, Paul A.; de Hoop, Maarten V.
  • Science, Vol. 363, Issue 6433
  • DOI: 10.1126/science.aau0323

Hybrid broadband ground motion simulation validation of small magnitude earthquakes in Canterbury, New Zealand
journal, February 2020

  • Lee, Robin L.; Bradley, Brendon A.; Stafford, Peter J.
  • Earthquake Spectra, Vol. 36, Issue 2
  • DOI: 10.1177/8755293019891718

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review
journal, June 2020

New Opportunities and Challenges of Geo-ICT Convergence Technology: GeoCPS and GeoAI
journal, August 2019

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