Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas

Abstract

Research Article| December 27, 2018 Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas Farid Khosravikia; Farid Khosravikia aDepartment of Civil Architectural and Environmental Engineering, The University of Texas at Austin, 301E E Dean Keeton Street C1700, Austin, Texas 78712 U.S.A., [e-mail], [e-mail], [e-mail] Search for other works by this author on: GSW Google Scholar Patricia Clayton; Patricia Clayton aDepartment of Civil Architectural and Environmental Engineering, The University of Texas at Austin, 301E E Dean Keeton Street C1700, Austin, Texas 78712 U.S.A., [e-mail], [e-mail], [e-mail] Search for other works by this author on: GSW Google Scholar Zoltan Nagy Zoltan Nagy aDepartment of Civil Architectural and Environmental Engineering, The University of Texas at Austin, 301E E Dean Keeton Street C1700, Austin, Texas 78712 U.S.A., [e-mail], [e-mail], [e-mail] Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2019) 90 (2A): 604–613. https://doi.org/10.1785/0220180218 Article history first online: 27 Dec 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Farid Khosravikia, Patricia Clayton, Zoltan Nagy; Artificial Neural Network‐Based Framework for Developing Ground‐Motion Models for Natural and Induced Earthquakes in Oklahoma, Kansas, and Texas. Seismological Research Letters 2018;; 90 (2A): 604–613. doi: https://doi.org/10.1785/0220180218 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 This article puts forward an artificial neural network (ANN) framework to develop ground‐motion models (GMMs) for natural and induced earthquakes in Oklahoma, Kansas, and Texas. The developed GMMs are mathematical equations that predict peak ground acceleration, peak ground velocity, and spectral accelerations at different frequencies given earthquake magnitude, hypocentral distance, and site condition. The motivation of this research stems from the recent increase in the seismicity rate of this particular region, which is mainly believed to be the result of the human activities related to petroleum production and wastewater disposal. Literature has shown that such events generally have shallow depths, leading to large‐amplitude shaking, especially at short hypocentral distances. Thus, there is a pressing need to develop site‐specific GMMs for this region. This study proposes an ANN‐based framework to develop GMMs using a selected database of 4528 ground motions, including 376 seismic events with magnitudes of 3 to 5.8, recorded over the 4‐ to 500‐km hypocentral distance range in these three states since 2005. The results show that the proposed GMMs lead to accurate estimations and have generalization capability for ground motions with a range of seismic characteristics similar to those considered in the database. The sensitivity of the equations to predictive parameters is also presented. Finally, the attenuation of ground motions in this particular region is compared with those in other areas of North America. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.