Using Simulated Ground Motions to Constrain Near‐Source Ground‐Motion Prediction Equations in Areas Experiencing Induced Seismicity

Research Article| September 25, 2017 Using Simulated Ground Motions to Constrain Near‐Source Ground‐Motion Prediction Equations in Areas Experiencing Induced Seismicity Samuel A. Bydlon; Samuel A. Bydlon aDepartment of Geophysics, Stanford University, Mitchell Building, 397 Panama Mall, Room B55, Stanford, California 94305, sbydlon@stanford.edu Search for other works by this author on: GSW Google Scholar Abhineet Gupta; Abhineet Gupta bDepartment of Civil and Environmental Engineering, Stanford University, Blume Center, Building 540, 439 Panama Mall, Room 206, Stanford, California 94305 Search for other works by this author on: GSW Google Scholar Eric M. Dunham Eric M. Dunham cDepartment of Geophysics, Stanford University, Mitchell Building, 397 Panama Mall, 3rd floor, Stanford, California 94305 Search for other works by this author on: GSW Google Scholar Author and Article Information Samuel A. Bydlon aDepartment of Geophysics, Stanford University, Mitchell Building, 397 Panama Mall, Room B55, Stanford, California 94305, sbydlon@stanford.edu Abhineet Gupta bDepartment of Civil and Environmental Engineering, Stanford University, Blume Center, Building 540, 439 Panama Mall, Room 206, Stanford, California 94305 Eric M. Dunham cDepartment of Geophysics, Stanford University, Mitchell Building, 397 Panama Mall, 3rd floor, Stanford, California 94305 Publisher: Seismological Society of America First Online: 27 Sep 2017 Online Issn: 1943-3573 Print Issn: 0037-1106 Bulletin of the Seismological Society of America (2017) 107 (5): 2078–2093. https://doi.org/10.1785/0120170003 Article history First Online: 27 Sep 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Samuel A. Bydlon, Abhineet Gupta, Eric M. Dunham; Using Simulated Ground Motions to Constrain Near‐Source Ground‐Motion Prediction Equations in Areas Experiencing Induced Seismicity. Bulletin of the Seismological Society of America 2017;; 107 (5): 2078–2093. doi: https://doi.org/10.1785/0120170003 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 SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract Recent seismic activity increases in historically quiescent areas such as Oklahoma, Texas, and Arkansas have spurred the need for investigation into expected ground motions associated with these seismic sources. The neoteric nature of this seismicity increase corresponds to a scarcity of ground‐motion recordings within ∼20 km of earthquakes Mw 3.0 and greater. To aid the effort of constraining near‐source ground‐motion prediction equations (GMPEs) associated with induced seismicity, we develop a framework for integration of synthetic ground‐motion data from simulated earthquakes into the GMPE development process. We demonstrate this framework by developing a GMPE for a target region encompassing north‐central Oklahoma and south‐central Kansas. We first gather a catalog of recorded ground motions from Mw 3–4 earthquakes that occurred in the target region. Using constraints on the region’s material structure, including well log data that provide insight into the characteristics of shallow sedimentary layers, we perform point‐source simulations intended to mimic a selection of recorded earthquakes from the target region. Simulated earthquake sources are constrained by available moment tensors and locations. Once we determine that our simulations produce realistic ground motions, we combine recorded and synthetic ground‐motion data to produce a composite ground‐motion catalog. We use this composite catalog to develop a regionally specific GMPE for our target region. This framework can be exported to other regions where near‐source ground‐motion data are sparse and can be used to improve constraints on near‐source GMPEs, which could directly benefit seismic‐hazard estimates. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.