Prioritizing Ground‐Motion Validation Metrics Using Semisupervised and Supervised Learning

Research Article| June 26, 2018 Prioritizing Ground‐Motion Validation Metrics Using Semisupervised and Supervised Learning Naeem Khoshnevis; Naeem Khoshnevis aCenter for Earthquake Research and Information, The University of Memphis, 3890 Central Avenue, Memphis, Tennessee 38152, nkhshnvs@memphis.edu Search for other works by this author on: GSW Google Scholar Ricardo Taborda Ricardo Taborda bDepartment of Civil Engineering,, and Center for Earthquake Research and Information, The University of Memphis, 3890 Central Avenue, Memphis, Tennessee 38152, ricardo.taborda@memphis.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Naeem Khoshnevis aCenter for Earthquake Research and Information, The University of Memphis, 3890 Central Avenue, Memphis, Tennessee 38152, nkhshnvs@memphis.edu Ricardo Taborda bDepartment of Civil Engineering,, and Center for Earthquake Research and Information, The University of Memphis, 3890 Central Avenue, Memphis, Tennessee 38152, ricardo.taborda@memphis.edu Publisher: Seismological Society of America First Online: 26 Jun 2018 Online Issn: 1943-3573 Print Issn: 0037-1106 © Seismological Society of America Bulletin of the Seismological Society of America (2018) 108 (4): 2248–2264. https://doi.org/10.1785/0120180056 Article history First Online: 26 Jun 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Naeem Khoshnevis, Ricardo Taborda; Prioritizing Ground‐Motion Validation Metrics Using Semisupervised and Supervised Learning. Bulletin of the Seismological Society of America 2018;; 108 (4): 2248–2264. doi: https://doi.org/10.1785/0120180056 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 It has become common practice to validate ground‐motion simulations based on a variety of time and frequency metrics scaled to quantify the level of agreement between synthetics and data or other reference solutions. There is, however, no agreement about the importance or weight that it ought to be given to each metric. This leads to their selection often being subjective, either based on intended applications or personal preferences. As a consequence, it is difficult for simulators to identify what modeling improvements are needed, which would be easier if they could focus on a reduced number of metrics. We present an analysis that looks into 11 ground‐motion validation metrics using semisupervised and supervised machine learning techniques. These techniques help label and classify goodness‐of‐fit results with the objective of prioritizing and narrowing the choice of these metrics. In particular, we use a validation dataset of a series of physics‐based ground‐motion simulations done for the 2008 Mw 5.4 Chino Hills, California, earthquake. We study the relationships that exist between 11 metrics and carry out a process where these metrics are understood as part of a multidimensional space. We use a constrained k‐means method and conduct a subspace clustering analysis to address the implicit high‐dimensional effects. This allows us to label the data in our dataset into four validation categories (poor, fair, good, and excellent) following previous studies. We then develop a family of decision trees using the C5.0 algorithm, from which we select a few trees that help narrow the number of metrics leading to a validation prediction into the four referenced categories. These decision trees can be understood as rapid predictors of the quality of a simulation, or as data‐informed classifiers that can help prioritize validation metrics. Our analysis, although limited to the particular dataset used here, indicates that among the 11 metrics considered, the acceleration response spectra and total energy of velocity are the most dominant ones, followed by the peak ground response in terms of acceleration and velocity. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.