S43A:
Applications of Data Science and Machine Learning in Seismology II Posters

Submit an Abstract to this Session



Session ID#: 26176

Session Description:
We currently are experiencing a dramatic increase in the volume, variety, and velocity of data in seismology. Methods and tools from data science and machine learning enable characterization of different types of signals, noise reduction, event detection, phase identification, seismic source characterization, inference of Earth’s seismic structure, and novel visualizations. With all the data available to researchers and practitioners in the field of seismology and advances in computational systems, there are great opportunities for data science and machine learning algorithms to extract meaningful insights for reducing earthquake hazards, understanding plate tectonics, earthquakes and earth structures, and improving the detection and limiting the proliferation of nuclear weapons. We encourage contributions on the methods and results of combining techniques and tools from data science and machine learning with seismology.
Primary Convener:  Timothy Draelos, Sandia National Laboratories, Albuquerque, NM, United States
Convener:  Qingkai Kong, University of California Berkeley, Berkeley, CA, United States; Lawrence Livermore National Laboratory, Livermore, CA, United States
Chairs:  Timothy Draelos, Sandia National Laboratories, Albuquerque, NM, United States and Qingkai Kong, Lawrence Livermore National Laboratory, Livermore, CA, United States
OSPA Liaison:  Qingkai Kong, Lawrence Livermore National Laboratory, Livermore, CA, United States
Index Terms:

1914 Data mining [INFORMATICS]
1932 High-performance computing [INFORMATICS]
1942 Machine learning [INFORMATICS]
7290 Computational seismology [SEISMOLOGY]

Abstracts Submitted to this Session:

Classifying seismic noise and sources from OBS data using unsupervised machine learning (221276)
Stephen Glenn Mosher, University of Ottawa, Ottawa, ON, Canada and Pascal Audet, University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada
Source discrimination between Mining blasts and Earthquakes in Tianshan orogenic belt，NW China (222817)
Lanlan Tang1, Miao Zhang2 and Lianxing Wen1,3, (1)University of Science and Technology of China, Hefei, China, (2)Los Alamos National Laboratory, Los Alamos, NM, United States, (3)Stony Brook University, Stony Brook, NY, United States
Application of constrained k-means clustering in ground motion simulation validation (227213)
Naeem Khoshnevis and Ricardo Taborda, Center for Earthquake Research and Information, Memphis, TN, United States
Separating non-diffuse component from ambient seismic noise cross-correlation in southern California­­ (233619)
Xin Liu, Stanford Earth Sciences, Stanford, United States, Gregory C Beroza, California Geological Survey Menlo Park, Menlo Park, CA, United States and Nori Nakata, Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States
Seismic waveform classification using deep learning (237463)
Qingkai Kong and Richard M Allen, University of California Berkeley, Berkeley, CA, United States
Detecting Seismic Events Using a Supervised Hidden Markov Model (244301)
Lynne Burks1, Robert Forrest2, Jaideep Ray1 and Chris Young1, (1)Sandia National Laboratories, Albuquerque, NM, United States, (2)Sandia National Laboratories, Livermore, CA, United States
Automatic Earthquake Detection by Active Learning (246329)
Karianne J. Bergen, Stanford University, Institute for Computational and Mathematical Engineering, Stanford, United States and Gregory C Beroza, Stanford University, Department of Geophysics, Stanford, CA, United States
Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure (247949)
Chengping Chai, University of Tennessee, Department of Physics and Astronomy, Knoxville, TN, United States, Carene S Larmat, Los Alamos National Laboratory, Los Alamos, NM, United States, Monica Maceira, Oak Ridge National Laboratory, Oak Ridge, TN, United States, Prof. Charles J Ammon, PhD, Pennsylvania State University Main Campus, Department of Geosciences, University Park, PA, United States, Rizheng He, Chinese Academy of Geological Sciences, Institute of Geology, Beijing, China and Haijiang Zhang, University of Science and Technology of China, School of Earth and Space Sciences, Hefei, China
Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California (248080)
Benjamin K Holtzman1, Arthur Paté2, John Paisley3, Felix Waldhauser4, Douglas Repetto2 and Lapo Boschi5, (1)Lamont-Doherty Earth Observatory, Palisades, NY, United States, (2)Lamont Doherty Earth Observatory, Columbia Univ., Palisades, NY, United States, (3)Columbia University of New York, Data Science Institute, Dept. of Electrical Engineering, New York, NY, United States, (4)Columbia University, Lamont-Doherty Earth Observatory, Palisades, United States, (5)CNRS, Institut des Sciences de la Terre Paris (iSTeP), Paris, France
Microseismic Event Relocation and Focal Mechanism Estimation Based on PageRank Linkage (249429)
Ana C Aguiar, Lawrence Livermore National Laboratory, Livermore, United States and Stephen C Myers, Lawrence Livermore National Laboratory, Livermore, CA, United States
Explosion Monitoring with Machine Learning: A LSTM Approach to Seismic Event Discrimination (256658)
Steven Alan Magana-Zook and Stanley Dean Ruppert, Lawrence Livermore National Laboratory, Livermore, CA, United States
Dimensional Representation and Gradient Boosting for Seismic Event Classification (273617)
Ryan David Kappedal, Air Force Institute of Technology, Bellbrook, OH, United States, Frank Charles Semmelmayer, Air Force Institute of Technology, Dayton, OH, United States and Steven Alan Magana-Zook, Lawrence Livermore National Laboratory, Livermore, CA, United States
Potential Solution of a Hardware-Software System V-Cluster for Big Data Analysis (276653)
Henry Tufo, University of Colorado at Boulder, Boulder, CO, United States, David A Yuen, Department of Earth Sciences, Minnesota Supercomputing Institute, University of Minnesota Twin Cities, Earth Sciences, Minneapolis, MN, United States, Gabriele Morra, McData Consulting Co., Ltd., Wuhan, China, Jed Brown, Argonne National Laboratory, Argonne, United States and Song Zihao, Ultimate Vision, Beijing, China
Using discrete wavelet transform features to discriminate between noise and phases in seismic waveforms (280721)
Robert Forrest, Jaideep Ray and Clifford W Hansen, Sandia National Laboratories, Albuquerque, NM, United States

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