Application of Pool‐Based Active Learning in Physics‐Based Earthquake Ground‐Motion Simulation

Research Article| January 09, 2019 Application of Pool‐Based Active Learning in Physics‐Based Earthquake Ground‐Motion Simulation Naeem Khoshnevis; Naeem Khoshnevis aCenter for Earthquake Research and Information, The University of Memphis, Memphis, Tennessee 38152 U.S.A., nkhshnvs@memphis.edu Search for other works by this author on: GSW Google Scholar Ricardo Taborda Ricardo Taborda bDepartment of Civil Engineering, Universidad of EAFIT, Colombia, Carrera 49, No. 7, Sur 50, Medelln, Antioquia, Colombia, rtaborda@eafit.edu.co 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, Memphis, Tennessee 38152 U.S.A., nkhshnvs@memphis.edu Ricardo Taborda bDepartment of Civil Engineering, Universidad of EAFIT, Colombia, Carrera 49, No. 7, Sur 50, Medelln, Antioquia, Colombia, rtaborda@eafit.edu.co Publisher: Seismological Society of America First Online: 09 Jan 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (2A): 614–622. https://doi.org/10.1785/0220180296 Article history First Online: 09 Jan 2019 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; Application of Pool‐Based Active Learning in Physics‐Based Earthquake Ground‐Motion Simulation. Seismological Research Letters 2019;; 90 (2A): 614–622. doi: https://doi.org/10.1785/0220180296 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 We present the results of the application of the active learning method in developing surrogates as physics‐based earthquake ground‐motion simulators. The surrogates, which map input parameters into output values without demanding intensive computations, are an essential part of any parameter optimization, sensitivity, and uncertainty analysis. Artificial neural networks (ANNs), as an example of surrogates, are very effective in estimating any complicated model. ANNs use a set of training data to learn the mapping process. Training data are a set of input parameters and their corresponding output values. Generating training data requires conducting numerous regional scale ground‐motion simulations. These numerical simulations are computationally challenging. Therefore, a step‐by‐step learning method should be employed to reduce the need for generating unnecessary training data. These methods are called active learning. In this study, we use a pool‐based query by committee (QBC) active learning method with effective initialization approach to study the performance of the models in the training process. We use a dataset that is generated for a moderate earthquake on a regional scale for anelastic attenuation studies with the focus on the estimation of peak ground velocity. The results show that active learning provides better performance in reducing generalization error than does passive learning while the same number of training data is used. Variation of performance with an increasing number of training data is significantly less in an active learning approach which indicates its stable and predictable behavior. This study, although limited to one earthquake and a metric, indicates that in developing surrogates as physics‐based earthquake ground‐motion simulators, application of active learning is an important step in reducing computational demands and generating stable predictors. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.