Dynamic Strains for Earthquake Source Characterization

Research Article| February 01, 2017 Dynamic Strains for Earthquake Source Characterization Andrew J. Barbour; Andrew J. Barbour aEarthquake Science Center, U.S. Geological Survey, 345 Middlefield Road, MS977, Menlo Park, California 94025‐3561 U.S.A.abarbour@usgs.gov Search for other works by this author on: GSW Google Scholar Brendan W. Crowell Brendan W. Crowell bDepartment of Earth and Space Sciences, University of Washington, Johnson Hall Rm‐070, Box 351310, 4000 15th Avenue NE, Seattle, Washington 98195‐1310 U.S.A. Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2017) 88 (2A): 354–370. https://doi.org/10.1785/0220160155 Article history first online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Andrew J. Barbour, Brendan W. Crowell; Dynamic Strains for Earthquake Source Characterization. Seismological Research Letters 2017;; 88 (2A): 354–370. doi: https://doi.org/10.1785/0220160155 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT Strainmeters measure elastodynamic deformation associated with earthquakes over a broad frequency band, with detection characteristics that complement traditional instrumentation, but they are commonly used to study slow transient deformation along active faults and at subduction zones, for example. Here, we analyze dynamic strains at Plate Boundary Observatory (PBO) borehole strainmeters (BSM) associated with 146 local and regional earthquakes from 2004–2014, with magnitudes from M 4.5 to 7.2. We find that peak values in seismic strain can be predicted from a general regression against distance and magnitude, with improvements in accuracy gained by accounting for biases associated with site–station effects and source–path effects, the latter exhibiting the strongest influence on the regression coefficients. To account for the influence of these biases in a general way, we include crustal‐type classifications from the CRUST1.0 global velocity model, which demonstrates that high‐frequency strain data from the PBO BSM network carry information on crustal structure and fault mechanics: earthquakes nucleating offshore on the Blanco fracture zone, for example, generate consistently lower dynamic strains than earthquakes around the Sierra Nevada microplate and in the Salton trough. Finally, we test our dynamic strain prediction equations on the 2011 M 9 Tohoku‐Oki earthquake, specifically continuous strain records derived from triangulation of 137 high‐rate Global Navigation Satellite System Earth Observation Network stations in Japan. Moment magnitudes inferred from these data and the strain model are in agreement when Global Positioning System subnetworks are unaffected by spatial aliasing. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.