Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning
Characterizing Acoustic Signals and Searching for Precursors during the Laboratory Seismic Cycle Using Unsupervised Machine Learning
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DOI：
10.1785/0220180367
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发表时间：
2019-05-01
影响因子：
3.3
通讯作者：
Johnson, Paul A.
中科院分区：
文献类型：
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作者：
Bolton, David C.;Shokouhi, Parisa;Johnson, Paul A.
最近的研究表明，机器学习 (ML) 可以利用断层带发出的声学信号来预测实验室地震的故障时间和其他方面。这些方法使用监督机器学习来构建声学信号特征和故障属性（例如瞬时摩擦状态和故障时间）之间的映射。我们在此工作的基础上，研究了无监督机器学习在实验室地震周期和实验室地震前兆期间识别声学信号模式的潜力。我们使用来自摩擦实验的数据，该实验显示在恒定法向应力 (2.0 MPa) 和恒定剪切速度 (10 μm/s) 下进行的重复粘滑破坏（相当于地震的实验室）。在整个实验过程中，使用宽带压电陶瓷传感器以 4 MHz 频率连续记录声发射信号。声学信号的统计特征与无监督 ML 聚类算法结合使用来识别数据中的模式（聚类）。我们发现整个地震周期中 ML 簇的一致趋势和系统转变，包括实验室地震前兆的一些证据。需要进一步的工作将机器学习聚类模式与故障的物理机制和故障时间的估计联系起来。
Recent work shows that machine learning (ML) can predict failure time and other aspects of laboratory earthquakes using the acoustic signal emanating from the fault zone. These approaches use supervised ML to construct a mapping between features of the acoustic signal and fault properties such as the instantaneous frictional state and time to failure. We build on this work by investigating the potential for unsupervised ML to identify patterns in the acoustic signal during the laboratory seismic cycle and precursors to labquakes. We use data from friction experiments showing repetitive stick-slip failure (the lab equivalent of earthquakes) conducted at constant normal stress (2.0 MPa) and constant shearing velocity (10 mu m/s). Acoustic emission signals are recorded continuously throughout the experiment at 4 MHz using broadband piezoceramic sensors. Statistical features of the acoustic signal are used with unsupervised ML clustering algorithms to identify patterns (clusters) within the data. We find consistent trends and systematic transitions in the ML clusters throughout the seismic cycle, including some evidence for precursors to labquakes. Further work is needed to connect the ML clustering patterns to physical mechanisms of failure and estimates of the time to failure.