气候变化领域集成服务门户(CCPortal): Laboratory earthquake forecasting: A machine learning competition

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DOI	10.1073/pnas.2011362118
	Laboratory earthquake forecasting: A machine learning competition
	Johnson P.A.; Rouet-Leduc B.; Pyrak-Nolte L.J.; Beroza G.C.; Marone C.J.; Hulbert C.; Howard A.; Singer P.; Gordeev D.; Karaflos D.; Levinson C.J.; Pfeiffer P.; Puk K.M.; Reade W.
发表日期	2021
ISSN	00278424
卷号	118 期号:5
英文摘要	Earthquake prediction, the long-sought holy grail of earthquake science, continues to confound Earth scientists. Could we make advances by crowdsourcing, drawing from the vast knowledge and creativity of the machine learning (ML) community? We used Google’s ML competition platform, Kaggle, to engage the worldwide ML community with a competition to develop and improve data analysis approaches on a forecasting problem that uses laboratory earthquake data. The competitors were tasked with predicting the time remaining before the next earthquake of successive laboratory quake events, based on only a small portion of the laboratory seismic data. The more than 4,500 participating teams created and shared more than 400 computer programs in openly accessible notebooks. Complementing the now well-known features of seismic data that map to fault criticality in the laboratory, the winning teams employed unexpected strategies based on rescaling failure times as a fraction of the seismic cycle and comparing input distribution of training and testing data. In addition to yielding scientific insights into fault processes in the laboratory and their relation with the evolution of the statistical properties of the associated seismic data, the competition serves as a pedagogical tool for teaching ML in geophysics. The approach may provide a model for other competitions in geosciences or other domains of study to help engage the ML community on problems of significance. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Earthquake prediction; Laboratory earthquakes; Machine learning competition; Physics of faulting
语种	英语
scopus关键词	competition; data analysis; earthquake; forecasting; human; human experiment; machine learning; physics; prediction; review; software; teaching; article
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180830
作者单位	Geophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, United States; Department of Physics and Astronomy, Purdue University, West Lafayette, IN 47907, United States; Department of Earth, Atmospheric and Planetary Sciences, Purdue University, West Lafayette, IN 47907, United States; Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, United States; Department of Geophysics, Stanford University, Stanford, CA 94305, United States; Department of Earth Science, La Sapienza Università di Roma, Rome, 00413, Italy; Department of Earth Science, Pennsylvania State University, University Park, PA 16802, United States; Laboratoire de Géologie, Département de Géosciences, École Normale Supérieure, PSL University, CNRS UMR, Paris, 8538, France; Kaggle, Google, LLC, Denver, CO 80301, United States; H2O.ai, Vienna, 1010, Austria; Athens, 11364, Greece; Jacksonville, FL 32207, United States; Department of Electrical Engineering, Rheinisch-Westfälische Technische Hochsc...
推荐引用方式 GB/T 7714	Johnson P.A.,Rouet-Leduc B.,Pyrak-Nolte L.J.,et al. Laboratory earthquake forecasting: A machine learning competition[J],2021,118(5).
APA	Johnson P.A..,Rouet-Leduc B..,Pyrak-Nolte L.J..,Beroza G.C..,Marone C.J..,...&Reade W..(2021).Laboratory earthquake forecasting: A machine learning competition.Proceedings of the National Academy of Sciences of the United States of America,118(5).
MLA	Johnson P.A.,et al."Laboratory earthquake forecasting: A machine learning competition".Proceedings of the National Academy of Sciences of the United States of America 118.5(2021).