In this paper we find a pattern of aperiodic seismic signals that precede earthquakes at any time in a laboratory earthquake’s cycle using a small window of time. We use a data set that comes from a classic laboratory experiment having several stick-slip displacements (earthquakes), a type of experiment which has been studied as a simulation of seismologic faults for decades. This data exhibits similar behavior to natural earthquakes, so the same approach may work in predicting the timing of them. Here we show that by applying random forest machine learning technique to the acoustic signal emitted by a laboratory fault, we can predict the time remaining before failure with 1.61 seconds mean absolute error at any moment of earthquake’s cycle. These predictions are based solely on the acoustical signal's statistical features derived from the local, moving 0.3 second time windows and do not make use of its history. Essential improvements in providing new understanding of fault physics may be brought by applying this technique to acoustic seismic data.
Tanyuk, Olha; Davieau, Daniel; South, Charles; and Engels, Daniel W.
"Machine Learning Predicts Aperiodic Laboratory Earthquakes,"
SMU Data Science Review: Vol. 2
, Article 11.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss2/11
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