Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning

Research Article| March 06, 2019 Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning David C. Bolton; David C. Bolton aDepartment of Geosciences, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., dcb31@psu.educhrisjmarone@gmail.com Search for other works by this author on: GSW Google Scholar Parisa Shokouhi; Parisa Shokouhi bDepartment of Engineering Science and Mechanics, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., pxs990@psu.edu, jvr5626@psu.edu Search for other works by this author on: GSW Google Scholar Bertrand Rouet‐Leduc; Bertrand Rouet‐Leduc cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Search for other works by this author on: GSW Google Scholar Claudia Hulbert; Claudia Hulbert cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Search for other works by this author on: GSW Google Scholar Jacques Rivière; Jacques Rivière bDepartment of Engineering Science and Mechanics, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., pxs990@psu.edu, jvr5626@psu.edu Search for other works by this author on: GSW Google Scholar Chris Marone; Chris Marone aDepartment of Geosciences, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., dcb31@psu.educhrisjmarone@gmail.com Search for other works by this author on: GSW Google Scholar Paul A. Johnson Paul A. Johnson cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Search for other works by this author on: GSW Google Scholar Author and Article Information David C. Bolton aDepartment of Geosciences, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., dcb31@psu.educhrisjmarone@gmail.com Parisa Shokouhi bDepartment of Engineering Science and Mechanics, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., pxs990@psu.edu, jvr5626@psu.edu Bertrand Rouet‐Leduc cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Claudia Hulbert cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Jacques Rivière bDepartment of Engineering Science and Mechanics, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., pxs990@psu.edu, jvr5626@psu.edu Chris Marone aDepartment of Geosciences, Pennsylvania State University, 201 Old Main, University Park, Pennsylvania 16802 U.S.A., dcb31@psu.educhrisjmarone@gmail.com Paul A. Johnson cGeophysics Group, Los Alamos National Laboratory, 30 Bikini Atoll Road, Los Alamos, New Mexico 87545 U.S.A., bertrand.rouetleduc@gmail.com, claudia.hulbert@gmail.com, paj@lanl.gov Publisher: Seismological Society of America First Online: 06 Mar 2019 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2019) 90 (3): 1088–1098. https://doi.org/10.1785/0220180367 Article history First Online: 06 Mar 2019 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation David C. Bolton, Parisa Shokouhi, Bertrand Rouet‐Leduc, Claudia Hulbert, Jacques Rivière, Chris Marone, Paul A. Johnson; Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning. Seismological Research Letters 2019;; 90 (3): 1088–1098. doi: https://doi.org/10.1785/0220180367 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 Recent work shows that machine learning (ML) can predict failure time and other aspects of laboratory earthquakes using the acoustic signal emanating from the fault zone. These approaches use supervised ML to construct a mapping between features of the acoustic signal and fault properties such as the instantaneous frictional state and time to failure. We build on this work by investigating the potential for unsupervised ML to identify patterns in the acoustic signal during the laboratory seismic cycle and precursors to labquakes. We use data from friction experiments showing repetitive stick‐slip failure (the lab equivalent of earthquakes) conducted at constant normal stress (2.0 MPa) and constant shearing velocity (⁠10 μm/s⁠). Acoustic emission signals are recorded continuously throughout the experiment at 4 MHz using broadband piezoceramic sensors. Statistical features of the acoustic signal are used with unsupervised ML clustering algorithms to identify patterns (clusters) within the data. We find consistent trends and systematic transitions in the ML clusters throughout the seismic cycle, including some evidence for precursors to labquakes. Further work is needed to connect the ML clustering patterns to physical mechanisms of failure and estimates of the time to failure. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.