S014
Machine-Learning-Based Earthquake Monitoring and Seismic Analysis

Session ID#: 157367

Session Description:
In recent years, machine learning (ML) techniques have been widely applied to earthquake monitoring and seismic analysis including seismic detection, seismic classification, seismic denoising, phase picking, phase association, earthquake location, magnitude estimation, ground motion prediction, earthquake early warning, source inversion, and subsurface imaging. ML-based phase picking has attracted much attention due to its remarkable performance in terms of accuracy and efficiency, and has been widely adopted for earthquake monitoring at local and regional scales, while global and regional (> 150 km) ML phase pickers have seen less attention. On the other hand, advances have also been made on waveform-based ML location algorithms, which can directly infer earthquake location information from continuous seismograms with the potential for near-real-time earthquake early warning. For these techniques, there is still room to improve the location accuracy and generalization capabilities, as well as for effectively accounting for variable seismic network geometry. ML-based first-motion polarity determination methods have shown promise in earthquake focal mechanism inversion, but full-waveform-based ML algorithms for focal mechanism inversion have still seen minimal attention. In addition, few ML-based methods are available to invert earthquake rupture processes from point sources to finite fault models. ML-based seismic tomography and imaging methods are also needed to better understand seismogenic structures. This session aims to highlight new ML methods for earthquake monitoring and seismic analysis and their applications in broad aspects of seismological studies. 
Index Terms:

7215 Earthquake source observations [SEISMOLOGY]
7219 Seismic monitoring and test-ban treaty verification [SEISMOLOGY]
7230 Seismicity and tectonics [SEISMOLOGY]
7270 Tomography [SEISMOLOGY]

Primary Convener:  Miao Zhang, Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada
Conveners:  Weiqiang Zhu, California Institute of Technology, Seismological Laboratory, Pasadena, United States, Ian W. McBrearty, Stanford University, Department of Geophysics, Stanford, CA, United States and Jannes Münchmeyer, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geophysics, Potsdam, Germany
Student/Early Career Convener:  Ian W. McBrearty, Stanford University, Department of Geophysics, Stanford, CA, United States
 
Discrimination of Anthropogenic and Natural Events Based on Deep Learning (1045103)
Céline Hourcade1, Mickael J. A. Bonnin1,2 and Éric Beucler1,2, (1)Laboratoire de Planétologie et Géosciences, CNRS UMR-6112, Nantes Université, Pays de la Loire, Nantes, France, (2)Observatoire des sciences de l’Univers Nantes Atlantique (OSUNA), CNRS-3281, Nantes Université, Pays de la Loire, Nantes, France
 
DiTing-IoT：deep-learning-enabled real-time seismic data processing via edge and cloud computing (1047565)
Ming Zhao1, Xiao Zhuowei2 and Shi Chen1, (1)IGP Institute of Geophysics, China Earthquake Administration, Beijing, China, (2)Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China
 
Comparison of Automated Seismic Event Detection Approaches in East Antarctica (1052202)
Long Ho1, Jake I. Walter2, Samantha Hansen1 and Zhigang Peng3, (1)University of Alabama, Tuscaloosa, AL, United States, (2)Oklahoma Geological Survey, Norman, United States, (3)Georgia Institute of Technology, Earth and Atmospheric Sciences, Atlanta, GA, United States
 
An On-site Earthquake Early Warning System through a Convolution Neural Network, a Transformer, and a Mixture Density Network (1052706)
Chieh Chen Chang1, Chih yu Chang2, Chung-Han Chan1,3 and Kuo-Fong Ma3, (1)National Central University, Earth Science, Taoyuan, Zhongli Dist., Taiwan, (2)National Central University, Earth Science, New Taipei City, Taiwan, (3)National Central University, Earthquake-Disaster & Risk Evaluation and Management (E-DREaM) Center, Taoyuan, Zhongli Dist., Taiwan
 
Similarity of shallow and deep earthquakes in seismic moment release: Insights from machine learning experiments (Invited) (1061366)
Zefeng Li1, Xin Cui2 and Yan Hu1, (1)University of Science and Technology of China, School of Earth and Space Sciences, Hefei, China, (2)University of Science and Technology of China, Hefei, China
 
Blockly Earthquake Transformer: A Python Toolbox for Customizing Seismic Phase Pickers (1061545)
Hao Mai and Pascal Audet, University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada
 
Application of Supervised Machine Learning Classification Techniques for Preprocessing Passive Seismic Earthquake Data (1064378)
Nicholas Appiah, Texas Tech University, Lubbock, TX, United States and Harold Gurrola, Texas Tech University, Geosciences, Lubbock, TX, United States
 
Unsupervised Automatic Classification of Fast and Slow Earthquake Signals from Continuous Records with a Hierarchical Clustering Algorithm (1064446)
Yuki Kodera, Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan
 
Crustal Seismicity in the Southern Puna Plateau and Relation With Shallow Crustal Structures (1067320)
Sankha Subhra Mahanti, University of Arizona, Geosciences, Tucson, AZ, United States, Eric Kiser, University of Arizona, Department of Geosciences, Tucson, United States and Susan L Beck, University of Arizona, Tucson, United States
 
Rapid Source Characterization of the Maule Earthquake Using Prompt Elasto-Gravity Signals (1072150)
Gabriela Arias1, Quentin Bletery2, Andrea Licciardi3, Kévin Juhel1 and Jean-Paul Ampuero4, (1)Géoazur - Université Nice Sophia Antipolis, Valbonne, France, (2)Université Côte d'Azur, CNRS, OCA, IRD, Géoazur, Valbonne, France, (3)Université Côte d'Azur, IRD, CNRS, Observatoire de la Côte d'Azur, Géoazur, Sophia Antipolis, France, (4)Université Côte d’Azur, CNRS, Observatoire de la Côte d’Azur, IRD, Géoazur, Seismology, Valbonne, France
 
Convolutional Neural Network Classification of Earthquakes, Mine Collapses and Explosions (1076142)
Yanwei Zhang, Missouri University of Science and Technology, Rolla, MO, United States, Junhao Qu, Shangdong Seismic Agency, Jinan, China, Xiaoyi Fan, Tai'an Fiducial Seismic Station of Shandong Earthquake Agency, Tai'an, China, Shaohui Zhou, Shandong Seismic Agency, Jinan, China and Stephen S Gao, Missouri University of Science and Technology, Geology and Geophysics Program, Rolla, United States
 
SfNet: A deep-learning-based approach for seismic surface-wave dispersion inversion with application to the mainland of China (1077646)
Feiyi Wang, SinoProbe Laboratory, School of Earth and Space Sciences, Peking University, Beijing, China and Xiaodong Song, Hebei Hongshan National Observatory on Thick Sediments and Seismic Hazards, Beijing, China
 
A Deep Learning Analysis of Seismic Waveforms for the Estimation of Peak Ground Acceleration (1078014)
Nishtha Srivastava, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Hessen, GERMANY, Georg Rumpker, Goethe University Frankfurt, Frankfurt, Germany and Johannes Faber, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
 
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers (Invited) (1078123)
Jannes Münchmeyer1, Jack Woollam2, Andreas Rietbrock2, Frederik J Tilmann3, Dietrich Lange4, Thomas Bornstein5, Tobias Diehl6, Carlo Giunchi7, Florian Haslinger8, Dario Jozinovic9, Alberto Michelini10, Joachim Saul3 and Hugo Soto11, (1)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geophysics, Potsdam, Germany, (2)Karlsruhe Institute of Technology, Geophysical Institute (GPI), Karlsruhe, Germany, (3)Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Potsdam, Germany, (4)GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany, (5)Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, (6)ETH Zurich, Swiss Seismological Service, Zurich, Switzerland, (7)Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti 53, Pisa, Italy, (8)Swiss Seismological Service, ETH Zurich, Zurich, Switzerland, (9)Istituto Nazionale di Geofisica e Vulcanologia, Roma, Italy, (10)Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, via di Vigna Murata 605, Roma, Italy, (11)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
 
Teleseismic Earthquake Phase Association via MCMC and Deep Learning (1079884)
Lindsay Yuling Chuang1, Jesse Williams2, Louisa Barama3, Zhigang Peng1 and Andrew Vern Newman4, (1)Georgia Institute of Technology, Earth and Atmospheric Sciences, Atlanta, GA, United States, (2)Global Technology Connection Inc., Atlanta, GA, United States, (3)Georgia Institute of Technology Main Campus, Earth and Atmospheric Sciences, Atlanta, United States, (4)Georgia Institute of Technology, Atlanta, United States
 
A New Machine Learning Earthquake Catalog for the Pacific Northwest (1082297)
Yiyu Ni1, Alexander Hutko1,2, J Renate Hartog1,2 and Marine Denolle1, (1)University of Washington, Department of Earth and Space Sciences, Seattle, United States, (2)Pacific Northwest Seismic Network, Seattle, United States
 
Seismic Noise Analysis using Deep Learning (1086473)
Johannes Faber, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany, Georg Rumpker, Goethe University Frankfurt, Frankfurt, Germany and Nishtha Srivastava, Frankfurt Institute for Advanced Studies, Frankfurt am Main, Hessen, GERMANY
 
Earthquake Early Warning with 3 seconds of records on a single station (1087326)
Pablo Espinoza Lara1, Quentin Bletery1, Jean-Paul Ampuero2 and Adolfo Inza3, (1)Université Côte d'Azur, CNRS, OCA, IRD, Géoazur, Valbonne, France, (2)Université Côte d’Azur, CNRS, Observatoire de la Côte d’Azur, IRD, Géoazur, Seismology, Valbonne, France, (3)Instituto Geofisico del Peru, Lima, Peru
 
(Re)Discovering Seismicity in Antarctica (1089279)
Andres Felipe Felipe Peña Castro, University of New Mexico Main Campus, Earth and Planetary Sciences, Albuquerque, United States, Brandon Schmandt, University of New Mexico, Department of Earth and Planetary Sciences, Albuquerque, United States and Jenny Nakai, USGS Alaska Science Center, Anchorage, United States
 
An integrated approach to locating previously undetected off-shore small magnitude seismic sources (1090123)
Jordan H Graw and Warren T Wood, US Naval Research Laboratory, Ocean Sciences Division, Stennis Space Center, MS, United States
 
Application of Machine Learning techniques for Detection of earthquake events in Jalisco (Western Mexico). (1093632)
Juan José Martínez Ceseña and Gabriel Reyes Alfaro, University of Guadalajara, Guadalajara, Mexico
 
Detecting and locating aftershocks for the 2020 MW 6.5 Stanley, Idaho earthquake using convolutional neural networks (1094471)
Bingxu Luo, University of Texas at Dallas, Richardson, TX, United States, Hejun Zhu, University of Texas at Dallas, Geosciences, Physics, Richardson, United States, Jidong Yang, China University of Petroleum (East China), Department of Geophysics, Qingdao, China, Thorne Lay, University of California Santa Cruz, Santa Cruz, CA, United States, Lingling Ye, Southern University of Science and Technology, Department of Earth and Space Sciences, Shenzhen, China, Zhong Lu, Southern Methodist University, Dallas, United States and David E Lumley, University of Texas at Dallas, Geophysics, Physics, Richardson, TX, United States
 
Earthquake Early Warning with Graph Learning (1096416)
Tim Clements1, Elizabeth S Cochran2, Annemarie Baltay1, Sarah E Minson1 and Clara E Yoon2, (1)U.S. Geological Survey, Earthquake Science Center, Moffett Field, United States, (2)U.S. Geological Survey, Earthquake Science Center, Pasadena, United States
 
Evaluating a Long Short-Term Memory (LSTM) Network for GNSS High-Rate Time Series Analysis (1097646)
Jensen DeGrande, University of Washington Seattle Campus, Department of Earth and Space Sciences, Seattle, WA, United States and Brendan Crowell, University of Washington, Department of Earth and Space Sciences, Seattle, WA, United States
 
Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network for Seismogram Images (1098769)
Hiromichi Nagao1, Ryosuke Kaneko2, Shin-ichi Ito1, Hiroshi Tsuruoka1 and Kazushige Obara1, (1)The University of Tokyo, Earthquake Research Institute, Tokyo, Japan, (2)The University of Tokyo, Graduate School of Information Science and Technology, Tokyo, Japan
 
Discrimination of anthropogenic event in local distance using deep learning model (1099194)
Keun Joo Seo and Dong-Hoon Sheen, Chonnam National University, Gwangju, Korea, Republic of (South)
 
Robust Seismic Phase Detection Method Modeling Both Global and Local Representations of Waveform (1101784)
Tomoki Tokuda, Earthquake Research Institute, The University of Tokyo, Tokyo, Japan and Hiromichi Nagao, Earthquake Research Institute, The Univerisity of Tokyo, Tokyo, Japan
 
Instantaneous tracking of earthquake growth with elastogravity signals (1104340)
Quentin Bletery1, Andrea Licciardi1, Bertrand Rouet-Leduc2, Jean-Paul Ampuero1 and Kévin Juhel1,3, (1)Université Côte d’Azur, IRD, CNRS, Observatoire de la Côte d'Azur, Géoazur, Sophia Antipolis, France, (2)Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan, (3)Université de Nantes, Laboratoire de Planétologie et Géodynamique, Nantes, France
 
Earthquake detection in subduction zones: Transfer learning with amphibious data from the Alaska Amphibious Community Seismic Experiment (1106655)
Grace Barcheck, University of Idaho, Moscow, ID, United States, Geoffrey A Abers, Cornell University, Ithaca, United States, Emily C Roland, Western Washington University, Geology Department and Marine and Coastal Sciences Program, Bellingham, WA, United States and Susan Y Schwartz, University of California-Santa Cruz, Santa Cruz, CA, United States
 
Segmentation Characteristics of Tectonic Tremors in Shikoku, Japan, Using Machine Learning Approaches (1107715)
Kate Huihsuan Huihsuan Chen, National Taiwan Normal University, Department of Earth Sciences, Taipei, Taiwan, Hao-Yu Chiu, National Taiwan University, Department of Geosciences, Taipei, Taiwan and Kazushige Obara, The University of Tokyo, Earthquake Research Institute, Tokyo, Japan
 
A Generalized Transfer-Learned OBS Phase Picker (1110376)
Alireza Niksejel, Dalhousie University, Halifax, NS, Canada and Miao Zhang, Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada
 
A Deep First Motion Polarity Focal Mechanism Catalogue for Switzerland (1113260)
Maria Mesimeri1, Men-Andrin Meier2, Tobias Diehl3, John F. Clinton3 and Stefan Wiemer3, (1)ETH Swiss Federal Institute of Technology Zurich, Swiss Seismological Service, Zurich, Switzerland, (2)Swiss Seismological Service, Department of Earth Sciences, Zuerich, Switzerland, (3)ETH Zurich, Swiss Seismological Service, Zurich, Switzerland
 
Rapid Estimation of Single-Station Earthquake Magnitudes with Machine Learning on a Global Scale (1113594)
Sydney Dybing1,2, William L Yeck3, Hank Cole2 and Diego Melgar4, (1)University of Oregon, Department of Earth Sciences, Eugene, United States, (2)USGS National Earthquake Information Center Golden, Golden, United States, (3)USGS National Earthquake Information Center Golden, Golden, CO, United States, (4)University of Oregon, Department of Earth Sciences, Eugene, OR, United States
 
Improving Deep Learning Seismic Arrival Pickers using Wavelet Transforms (1116828)
Rey Koki1, Anne Sheehan2, Elizabeth Bradley3, Michael McCabe1 and Jed Brown1, (1)University of Colorado at Boulder, Computer Science, Boulder, United States, (2)University of Colorado Boulder, Department of Geological Sciences and Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, United States, (3)University of Colorado at Boulder, Computer Science, Boulder, CO, United States
 
Exploring Machine Learning-Based Detectors and Associators for Induced Earthquakes and Complex Network Geometries in the Oil and Gas Fields of Texas and Oklahoma (1120863)
Heather DeShon1, Asiye Aziz Zanjani1, Kivanc Sabunis1, Julia Rosenblit2 and Chris Justiniano3, (1)Southern Methodist University, Dallas, United States, (2)Southern Methodist University, Dallas, TX, United States, (3)University of Puerto Rico at Mayagüez, Mayagüez, PR, UNITED STATES
 
Auto-Encoder Based Discrimination of Mining Induced Seismic Events from Natural Seismicity in Central Utah (1122865)
Christopher Pankow, West High School, Salt Lake City, United States and Ben Baker, University of Utah, Salt Lake City, United States
 
Seismic Source Characterization using Physics-Guided Graph Convolutional Neural Network (1124255)
Will Reichard-Flynn1, Xitong Zhang1, Miao Zhang2 and Youzuo Lin1, (1)Los Alamos National Laboratory, Los Alamos, United States, (2)Dalhousie University, Department of Earth and Environmental Sciences, Halifax, NS, Canada
 
Towards building reliable earthquake catalogs with neural phase pickers: The prediction inconsistency should not be overlooked (1126192)
Yongsoo Park, Gregory C Beroza and William L Ellsworth, Stanford University, Department of Geophysics, Stanford, CA, United States
 
Source Untangler Guided by Artificial Intelligence Image Recognition (SUGAR) (1130953)
Fengzhou Tan1, Honn Kao2, Kwang Moo Yi3, Chet Goerzen1, Jesse Hutchinson4, Dawei Gao5, Amir M Farahbod2 and Edwin Nissen1, (1)University of Victoria, School of Earth and Ocean Sciences, Victoria, BC, Canada, (2)Geological Survey of Canada, Sidney, BC, Canada, (3)University of British Columbia, Department of Computer Science, Vancouver, Canada, (4)University of Victoria, Ocean Networks Canada, Victoria, Canada, (5)Central South University, School of Geosciences and Info-physics, Changsha, China
 
A deep-learning-based catalog of long-period teleseismic phase arrivals (1133999)
Jun Su, Christine T Houser and John W Hernlund, Earth-Life Science Institute, Meguro, Tokyo, Japan
 
Muti-Station Analysis of Icequakes and Earthquakes in Southern Alaska using Random Forests (1134656)
Akash Kharita, Indian Institute of Technology Roorkee, Roorkee, India, Marine Denolle, University of Washington, Department of Earth and Space Sciences, Seattle, United States and Michael Edwin West, Univ Alaska Fairbanks, Fairbanks, AK, United States
 
Microearthquakes detection in the Korean Peninsula using machine learning algorithm (1135911)
Byeongwoo Kim, Tae-Kyung Hong, Song Park and Han Ah Park, Yonsei University, Earth System Sciences, Seoul, South Korea
 
DNN Model for Near Real-Time Location and Origin Time Determination Using a Global Network (1139209)
Louisa Barama1, Jesse Williams2, Lindsay Yuling Chuang3, Andrew Vern Newman1 and Zhigang Peng4, (1)Georgia Institute of Technology, Atlanta, United States, (2)Global Technology Connection Inc., Atlanta, GA, United States, (3)Georgia Institute of Technology, Earth and Atmospheric Sciences, Atlanta, United States, (4)Georgia Institute of Technology, Earth and Atmospheric Sciences, Atlanta, GA, United States
 
A Deep Catalogue of Marsquakes (1141368)
Nikolaj L Dahmen1, John F. Clinton2, Men-Andrin Meier3, Simon C Staehler4, Savas Ceylan5, Alexander Stott6, Doyeon Kim7 and Domenico Giardini5, (1)ETH Zurich, Institute of Geophysics, Zurich, Switzerland, (2)ETH Zurich, Swiss Seismological Service, Zurich, Switzerland, (3)ETH Swiss Federal Institute of Technology Zurich, Department of Earth Sciences, Zurich, Switzerland, (4)ETH Zurich, Institute for Geophysics, Zurich, Switzerland, (5)ETH Zurich, Department of Earth Sciences, Institute of Geophysics, Zurich, Switzerland, (6)Institut Supérieur de l’Aéronautique et de l’Espace SUPAERO, Toulouse, France, (7)ETH Zurich, Zurich, Switzerland
 
Ground motion models using ensemble learning: application to RESORCE database (1141536)
Sreenath Vemula, Indian Institute of Technology Madras, Chennai, India and S.T.G Raghukanth, Indian Institute of Technology-Madras, Chennai, India
 
Use of Legacy, Seismological Domain-Knowledge Improves the Performance of a Deep-Learning, Seismic Arrival-Time Picker (1141779)
Sonja Gaviano1,2, Anthony Lomax3, Matteo Bagagli4, Spina Cianetti5, Carlo Giunchi5, Valentino Lauciani6, Alberto Michelini6 and Christopher Zerafa5, (1)University of Florence, Earth Science, Via La Pira 4, Florence, Italy, (2)Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti, 53, Pisa, Italy, (3)ALomax Scientific, Mouans-Sartoux, France, (4)Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, via di Vigna Murata, 605, Rome, Italy, (5)Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Cesare Battisti 53, Pisa, Italy, (6)Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, via di Vigna Murata 605, Roma, Italy
 
Toward the Development of a High-Precision Seismic Phase Picker for Volcanic Seismicity using Deep Learning: Performance Tests with Various Learning Models (1145359)
Yuji Nakamura1, Ahyi Kim1, Hiroki Uematsu1, Yohei Yukutake2 and Yuki Abe3, (1)Yokohama City University, Yokohama, Japan, (2)Earthquake Research Institute, University of Tokyo, Tokyo, Japan, (3)Hot Springs Research Institute of Kanagawa Prefecture, Odawara, Japan
 
Deep Clustering of Tremor Episodes Can Reveal Different Eruptive Periods during the 2021 Fagradalsfjall Eruption, Iceland (1145565)
Zahra Zali1,2, Eva P. S. Eibl1, Matthias M Ohrnberger1, Fabrice Cotton1,2, S. Mostafa Mousavi3 and Frank Scherbaum1, (1)University of Potsdam, Potsdam, Germany, (2)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, (3)Stanford University, Department of Geophysics, Stanford, United States
 
Optimized earthquake analysis procedures based on deep learning phase picker (1147704)
YoonTaek Hong and Dong-Hoon Sheen, Chonnam National University, Gwangju, Korea, Republic of (South)
 
P-wave first-motion polarity determination using deep learning (1147990)
Ah-Hyun Byun and Dong-Hoon Sheen, Chonnam National University, Gwangju, Korea, Republic of (South)
 
Detecting converted seismic phases of Tonga deep earthquakes: insights from deep-learning methods (1152125)
Ziyi Xi1, Songqiao Shawn Wei2, Nooshin Saloor2, Weiqiang Zhu3 and Gregory C Beroza4, (1)Michigan State University, Department of Computational Mathematics, Science and Engineering, East Lansing, MI, United States, (2)Michigan State University, Department of Earth and Environmental Sciences, East Lansing, United States, (3)California Institute of Technology, Seismological Laboratory, Pasadena, United States, (4)Stanford University, Department of Geophysics, Stanford, CA, United States
 
Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals (1159153)
Timothy T Dittmann, University of Colorado at Boulder, Boulder, United States; UNAVCO, Inc. Boulder, Boulder, United States, Yunxiang Liu, University of Colorado at Boulder, Smead Aerospace Engineering Sciences Department, Boulder, CO, United States, Yu Morton, University of Colorado, Boulder, Aerospace Engineering Sciences, Boulder, CO, United States and David Mencin, UNAVCO, Inc., Boulder, United States
 
Peak Ground Acceleration and Spectral Amplitude Prediction using supervised Machine Learning algorithms: A study case in Central Italy (1169248)
Abel Daniel Zaragoza Alonzo1, Miller Zambrano2, Lucia Luzi3, Tondi Emanuele2,4 and Leonardo Colavitti3, (1)University of Camerino, School of Science and Technology - Geology Division, Camerino, Italy, (2)University of Camerino, School of Science and Technology, Geology Division, Camerino, Italy, (3)INGV National Institute of Geophysics and Volcanology, Milan, Italy, (4)Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Sismologia e Tettonofisica, Camerino, Italy
 
Cluster analysis for the study of stress field variations in the SE-Carpathians (1170719)
Lili Czirok1, Lukacs Benedek Kuslits2, István Bozsó2 and Katalin Eszter Gribovszki2, (1)Quantectum AG, Pfäffikon, Switzerland, (2)ELKH Institute of Earth Physics and Space Science, Sopron, Hungary
 
Seismically Active Fault Identification Using a Density-Based Clustering Algorithm: Northern Chile Case Study (1172792)
Marcia Ojeda1, Felipe Garrido1, Diana Comte2, Xavier Emery3, Nadia Mery3, Gisella Palma1,4, Felipe Navarro1, Gonzalo Díaz1 and Egaña Álvaro1, (1)AMTC-FCFM Universidad de Chile, Santiago, Chile, (2)AMTC-DGF, FCFM Universidad de Chile, Santiago, Chile, (3)AMTC-DIMIN FCFM Universidad de Chile, Santiago, Chile, (4)Universidad Mayor, Escuela de Geología, Providencia, Chile
 
Automated Detection and Analysis of Earthquakes within the ALBACORE Ocean Bottom Seismometer Deployment using Machine Learning (1173008)
Allen L Husker, California Institute of Technology, Seismological Laboratory, Pasadena, CA, United States, Francesco Civilini, CalTech Seismological Laboratory, Pasadena, CA, United States and Monica D Kohler, Caltech, Pasadena, CA, United States
 
Methodology for Similarity Area Searching Using Statistical Distance Measures: Application on Geological Exploration (1175841)
Felipe Navarro1, Gonzalo Díaz1, Marcia Ojeda1, Felipe Garrido1, Diana Comte2, Alejandro Ehrenfeld1, Álvaro Francisco Egaña1 and Gisella Palma1,3, (1)AMTC-FCFM Universidad de Chile, Santiago, Chile, (2)AMTC-DGF, FCFM Universidad de Chile, Santiago, Chile, (3)Universidad Mayor, Escuela de Geología, Providencia, Chile
 
A Physics-informed Machine Learning (PIML) Model for Lab Earthquake Prediction using Time-lapse Active Source Ultrasonic Data (1177419)
Prabhav Borate, Pennsylvania State University Main Campus, University Park, United States, Jacques Riviere, Pennsylvania State Universtity, University Park, United States, Chris Marone, Pennsylvania State University, Department of Geosciences, University Park, United States, Ankur Mali, University of South Florida Tampa, Department of Computer Science and Engineering, Tampa, United States, Daniel Kifer, Pennsylvania State University, Department of Computer Science & Engineering, University Park, United States and Parisa Shokouhi, Pennsylvania State University, Engineering Science and Mechanics, University Park, United States
 
Unraveling the Complexity of M5-6 Earthquakes from Teleseismic Waveforms Improved Using Deep Learning (1183710)
Qibin Shi and Marine Denolle, University of Washington, Department of Earth and Space Sciences, Seattle, United States
 
Using a novel Deep Learning Model for Seismic Phase Picking to understand Mechanisms Driving Aftershock Sequences (1185846)
Saeed Yahya Mohanna, University of California Los Angeles, Department of Earth, Planetary and Space Sciences., Los Angeles, United States, Tian Feng, University of California Los Angeles, Earth, Planetary and Space Science, Los Angeles, United States and Lingsen Meng, University of California Los Angeles, Earth, Planetary and Space Sciences, Los Angeles, United States
 
Earthquake Aftershocks Pattern Prediction (1186821)
Mulugeta Tuji Dugda1, Alemayehu B Kassa2, Line Pouchard3, Yuewei Lin3 and Amanuel Seifu4, (1)Morgan State University, Electrical and Computer Engineering, Baltimore, United States, (2)Morgan State University, Computer Science, Baltimore, United States, (3)Brookhaven National Laboratory, Computational Science Initiative, Upton, United States, (4)University of Maryland College Park, Computer Science, College Park, United States
 
Assessing Machine Learning Assisted Phase Detection on the Alaska Seismic Network (1186851)
Sarah Noel, UNITED STATES and Michael Edwin West, Univ Alaska Fairbanks, Fairbanks, AK, United States
 
A New Method for Identifying Seismic Lineaments and Evaluation of Dihedral Angles Between Conjugate Strike-Slip Faults, With Application to Southern California (1188009)
Xiaoyu Zou1, Yuri A Fialko1, Andrew Dennhey2, Alexander Cloninger3 and Shabnam Jandaghi Semnani4, (1)Scripps Institution of Oceanography, University of California, San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States, (2)University of California, San Diego, Department of Mathematical Sciences, La Jolla, United States, (3)University of California, San diego, Department of Mathematical Sciences, La Jolla, United States, (4)University of California, San Diego, Jacobs School of Engineering, La Jolla, United States
 
Machine Learning Applied to Local and Regional Scale Seismic Network Operations (1189983)
Josh C Stachnik, Instrumental Software Technologies, Inc., Saratoga Springs, United States, Jeffrey M Leifer, Instrumental Software Tech Inc, Saratoga Springs, NY, United States and Paul A Friberg, Instrumental Software Technologies, Inc. (ISTI), New Paltz, NY, United States
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