Laboratory earthquake forecasting: A machine learning competition

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 5
• Main Authors: Johnson, Paul A, Rouet-Leduc, Bertrand, Pyrak-Nolte, Laura J, Beroza, Gregory C, Marone, Chris J, Hulbert, Claudia, Howard, Addison, Singer, Philipp, Gordeev, Dmitry, Karaflos, Dimosthenis, Levinson, Corey J, Pfeiffer, Pascal, Puk, Kin Ming, Reade, Walter
• Format: Journal Article
• Language: English
• Published: United States Proceedings of the National Academy of Sciences 02.02.2021; National Academy of Sciences
• Subjects: artificial intelligence; Earth Sciences; earthquake prediction; forecasting; GEOSCIENCES; laboratory earthquakes; machine learning; machine learning competition; Physical Sciences; physics of faulting; Seismicity; earthquake prediction; physics of faulting; laboratory earthquakes; machine learning competition
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2011362118

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.