S11E:
Extracting Information from Geophysical and Geochemical Signals: Applying Machine Learning Through Data Science Challenges Posters


Submit an Abstract to this Session


Session ID#: 50342

Session Description:
Major breakthroughs in geophysics are anticipated as more researchers turn to machine learning and other data science techniques; however, owing to the inherent complexity of machine learning methods, they are prone to misapplication, may produce uninterpretable models, and are often insufficiently documented.  This combination of attributes hinders both reliable assessment of model validity and consistent interpretation of model outputs.  By providing documented datasets and challenging teams to apply fully documented workflows for machine learning approaches, we expect to accelerate progress in the application of data science to longstanding research issues in geophysics.  The session goals are to (1) announce machine learning challenges to the community that address earthquake detection and the physics of rupture/timing of earthquakes; and (2) solicit papers on the use of different machine learning techniques related to the identification of the physics contained in geophysical/chemical signals, as well as from images of geologic materials (minerals, fractures, etc.).
Primary Convener:  Laura J Pyrak-Nolte, Purdue University, Department of Physics and Astronomy; Department of Earth, Atomospheric and Planetary Sciences; Lyles School of Civil Engineering, West Lafayette, United States
Conveners:  Paul A Johnson, Los Alamos National Laboratory, Los Alamos, NM, United States, Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States and James R Rustad, Office of Basic Energy Sciences, U.S. Department of Energy, Germantown, MD, United States
Primary Liaison:  Laura J Pyrak-Nolte, Purdue University, Department of Physics and Astronomy; Department of Earth, Atomospheric and Planetary Sciences; Lyles School of Civil Engineering, West Lafayette, United States
Chairs:  Laura J Pyrak-Nolte, Purdue University, Department of Physics and Astronomy; Department of Earth, Atomospheric and Planetary Sciences; Lyles School of Civil Engineering, West Lafayette, United States and James R Rustad, Office of Basic Energy Sciences, U.S. Department of Energy, Germantown, MD, United States
OSPA Liaison:  Eugene S. Ilton, Pacific Northwest National Laboratory, Geochemistry, Richland, WA, United States

Cross-Listed:
  • MR - Mineral and Rock Physics
  • NG - Nonlinear Geophysics
  • T - Tectonophysics
Index Terms:

0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]
1910 Data assimilation, integration and fusion [INFORMATICS]
1942 Machine learning [INFORMATICS]
7223 Earthquake interaction, forecasting, and prediction [SEISMOLOGY]

Abstracts Submitted to this Session:

An automatic earthquake bulletin assistant system based on deep learning neural networks: application on seismic network of Chinese Capital and surrounding area (378579)
Ming Zhao, IGP Institute of Geophysics, China Earthquake Administration, Beijing, China, David A Yuen, Columbia University, Dept. of Applied Physics and Applied Mathematics, New York, United States; China University of Geosciences Wuhan, Department of Big Data, Wuhan, China, Shi Chen, IGP Institute of Geophysics, China Earthquake Administration, Peking, China and Lihua Fang, Institute of Geophysics, China Earthquake Administration, Beijing, China
Multi-cluster classification of long-period volcanic earthquakes (368895)
Polina Polunina1, Nikolai Shapiro2, Evgeny Burnaev3, Dmitry Droznin4, Svetlana Droznina4 and Sergey Senyukov5, (1)Skolkovo Institute of Science and Technology, Skolkovo, Russia, (2)Institut de Physique du Globe de Paris, Paris, France, (3)Skolkovo Institute of Science and Technology, Moscow, Russia, (4)Kamchatka Branch of the Geophysical Survey RAS, Petropavlovsk-Kamchatsky, Russia, (5)Geophysical Survey RAS, Petropavlovsk-Kamcha, Russia
A DEEP LEARNING ARCHITECTURE FOR INTERMEDIATE-DEPTH EARTHQUAKE DETECTION, PHASE PICKING AND MAGNITUDE ESTIMATION (466571)
Manuel Alberto Florez, Massachusetts Institute of Technology, Cambridge, MA, United States, German A Prieto, Universidad Nacional de Colombia, Bogota, Colombia and Marine Denolle, University of Washington Seattle Campus, Department of Earth and Space Sciences, Seattle, United States
A novel approach to generate research grade seismic catalog in real time; Machine learning application (392503)
Andrew Reynen1, Sepideh Karimi2, Dario Baturan1 and Neil Spriggs1, (1)Nanometrics Inc, Kanata, ON, Canada, (2)Nanometrics Inc, Seismic Monitoring Services, Kanata, ON, Canada
Machine learning reveals the spatial and temporal dynamics simulated slip events in a sheared granular fault gouge (468667)
Christopher Ren1, Omid Dorostkar2, Robert A Guyer3, Bertrand Rouet-Leduc4, Claudia Hulbert5, Jan Carmeliet2 and Paul A Johnson1, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)ETH Swiss Federal Institute of Technology Zurich, Department of Mechanical Engineering, Zurich, Switzerland, (3)University of Nevada Reno, Physics Department, Reno, NV, United States, (4)Los Alamos National Laboratory, Geophysics Group, Los Alamos, United States, (5)Geolabe, Los Alamos, United States
Automatic Seismic Denoising Method Based on Deep Neural Networks (420667)
Weiqiang Zhu, University of California, Berkeley, Department of Earth and Planetary Science, Berkeley, United States, S. Mostafa Mousavi, Stanford University, Department of Geophysics, Stanford, United States and Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States
Deep learning for seismic interpretation (382543)
Xinming Wu1, Yunzhi Shi1 and Sergey Fomel2, (1)The University of Texas at Austin, Bureau of Economic Geology, Austin, TX, United States, (2)University of Texas at Austin, Bureau of Economic Geology, Austin, United States
Derivation of empirical regression equation for on-site earthquake early warning system in the South Korea (458052)
Roungyi Kim, Pukyong National University, Division of Earth Environmental System Science, Busan, Korea, Republic of (South), Tae-Seob Kang, Pukyong National University, Division of Earth Environmental System Science, Busan, South Korea, Hyun Jae Yoo, Pukyong National University, Busan 48513, South Korea, Jung-ho Park, KIGAM Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea and In-Seub Lim, KIGAM Korea Institute of Geoscience and Mineral Resources, Earthquake Research Center, Daejeon, South Korea
Detecting Low-frequency Earthquakes in Western Japan Using Convolutional Neural Networks (411665)
Huiyun Guo, Peking University, Beijing, China, Hui Huang, UCLA, 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
Detecting Offshore Seismicity in Cascadia Using Logistic Regression Applied to Sub-Arrays of Ocean-Bottom Seismographs (394392)
Stephen Glenn Mosher, University of Ottawa, Ottawa, ON, Canada and Pascal Audet, University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada
Development of a Method for Reliability Estimation of Triggered P-arrivals using Convolutional Neural Network (432791)
SeongJin Kim1, GyeongMi Kim1 and Dong-Hoon Sheen2, (1)Chonnam National University, Gwangju, Korea, Republic of (South), (2)Chonnam National University, Dept. of Geological Environment, Gwangju, Korea, Republic of (South)
Development of Neural Network to Locate Earthquakes Based on Deep Learning with Theoretical Seismograms as Training Data (371491)
Seiji Tsuboi and Daisuke Sugiyama, JAMSTEC Japan Agency for Marine-Earth Science and Technology, Kanagawa, Japan
Toward laboratory earthquake prediction using unsupervised classification of acoustic emissions (419982)
Hope Abigail Jasperson, Rice University, Earth, Environmental and Planetary Sciences, Houston, TX, United States, David Chas Bolton, Pennsylvania State University, University Park, PA, United States, Paul A Johnson, Los Alamos National Laboratory, Los Alamos, NM, United States, Chris Marone, Pennsylvania State University, Department of Geosciences, University Park, United States and Maarten Dehoop, Rice University, Computational and applied mathematics, Houston, TX, United States
Rapid recognition of fracture-generated phase components using machine learning methods (427051)
Hongkyu Yoon, Sandia National Laboratories, Geomechanics Department, Albuquerque, NM, United States, Liyang Jiang, Purdue University, Department of Physics and Astronomy, West Lafayette, IN, United States and Laura J Pyrak-Nolte, Purdue University, Department of Physics and Astronomy; Department of Earth, Atomospheric and Planetary Sciences; Lyles School of Civil Engineering, West Lafayette, United States
Earthquake and emergent correlated noise detection from a dense seismic deployment on the San Jacinto fault zone (399312)
Christopher W Johnson, Scripps Institution of Oceanography, La Jolla, CA, United States, Haoran Meng, University of Southern California, Los Angeles, CA, United States, Frank Vernon, University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States and Yehuda Ben-Zion, University of Southern California, Department of Earth Sciences and Statewide California Earthquake Center, Los Angeles, CA, United States
FAST: a data mining approach to large-scale earthquake detection (384806)
Karianne J. Bergen1,2, Clara E Yoon3, Kexin Rong4, Peter Bailis4 and Gregory C Beroza5, (1)Stanford University, Institute for Computational and Mathematical Engineering, Stanford, United States, (2)Harvard University, Earth and Planetary Sciences, Cambridge, MA, United States, (3)Stanford University, Geophysics, Stanford, United States, (4)Stanford University, Computer Science, Stanford, CA, United States, (5)Stanford University, Department of Geophysics, Stanford, CA, United States
Visualizing Aeromagnetic Texture Domains using Multichannel Filtering and Unsupervised Clustering (403338)
David Alexander Nathan, University of Western Australia, Centre for Exploration Targeting, Crawley, WA, Australia, Alan Aitken, The University of Western Australia, School of Earth Sciences, Perth, Australia, Eun-Jung Holden, The University of Western Australia, Centre for Exploration Targeting, Crawley, Australia and Jason Wong, University of Western Australia, Centre for Exploration Targeting, Crawley, Australia
Lithological Classification of Snake River Basin by Deep Learning Algorithm (404879)
Worapop Thongsame1, Waruntorn Kanitpanyacharoen1 and Ekapol Chuangsuwanich2, (1)Chulalongkorn University, Geology, Basin Analysis and Structural Evolution STAR, Bangkok, Thailand, (2)Chulalongkorn University, Bangkok, Thailand
InversionNet: Accurate and Efficient Seismic Waveform Inversion with Convolutional Neural Networks (435777)
Youzuo Lin, Los Alamos National Laboratory, Los Alamos, United States and Yue Wu, Los Alamos National Laboratory, Geophysics, Los Alamos, United States
Multinomial logistic regression applied to identify tectonic tremor in Northern South America by using the Colombian National Seismic Network (382257)
Mariana Gomez Ospina, Universidad Nacional de Colombia, Medellin, Medellin, Colombia, Amanda Thomas, University of Oregon, Department of Earth Sciences, Eugene, United States and Gaspar Monsalve, Universidad Nacional de Colombia, Facultad de Minas, Medellín, Colombia
Pairwise Association of Common Source Seismic Arrivals Applying a Convolutional Neural Network (399501)
Ian W. McBrearty1, Andrew Delorey2 and Paul A Johnson2, (1)Stanford University, Department of Geophysics, Stanford, CA, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States
Preliminary study on local earthquakes detection in the southwestern Longmenshan in time-frequency domain using the convolutional neural (422945)
Chaoliang Wang, Chengdu University of Technology, Chengdu, China and Chuntao Liang, Chengdu University of Technology, Geophysics, Chengdu, China
Probabilistic Machine Learning Forecasting of Short Term Volcanic Inflation and Deflation Processes (452604)
Marian Anghel and Omar E Marcillo, Los Alamos National Laboratory, Los Alamos, NM, United States
Rapid Characterization of Large Earthquakes with GNSS and Machine Learning (440167)
Jiun-Ting Lin1, Diego Melgar2 and Amanda Thomas2, (1)University of Oregon, Department of Earth Sciences, Eugene, OR, United States, (2)University of Oregon, Department of Earth Sciences, Eugene, United States
Siamese networks for triggered earthquakes detection (365969)
Vivian Tang, Northwestern University, Department of Earth and Planetary Sciences, Evanston, IL, United States, Prem Seetharaman, Northwestern University, Earth and Planetary Sciences, Evanston, IL, United States, Kevin Chao, Northwestern University, Northwestern Institute on Complex Systems, Evanston, IL, United States, Bryan A Pardo, Northwestern University, Electrical Engineering and Computer Science, Evanston, IL, United States and Suzan van der Lee, Northwestern University, Earth and Planetary Sciences, Evanston, United States
Single-station classification of tectonic tremors (384687)
Kate Huihsuan Chen1, Ting-Chen Yeh2, Yaochieh Chen2 and Yi-Hung Liu3, (1)National Taiwan Normal University, Department of Earth Sciences, Taipei, Taiwan, (2)Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan, (3)National Taipei University of Technology, Department of Mechanical Engineering, Taipei, Taiwan
Study on an automated quality control of seismic data using Convolutional Neural Network (432540)
Keun Joo Seo1, Jeongseon Oh1 and Dong-Hoon Sheen2, (1)Chonnam National University, Gwangju, Korea, Republic of (South), (2)Chonnam National University, Dept. of Geological Environment, Gwangju, Korea, Republic of (South)
Transfer Learning for Seismic Phase Picking on Different Geographic Regions (425653)
Chenyu Li1, Lijun Zhu2, Dongdong Yao1, Xiaofeng Meng3, Zhigang Peng4, James H. McClellan2 and Jake I. Walter5, (1)Georgia Institute of Technology Main Campus, Atlanta, GA, United States, (2)Georgia Institute of Technology Main Campus, School of Electrical and Computer Engineering, Atlanta, GA, United States, (3)University of Southern California, Los Angeles, United States, (4)Georgia Institute of Technology, School of Earth and Atmospheric Sciences, Atlanta, United States, (5)Institute for Geophysics, Austin, United States

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