Shreedharan Srisharan, Bolton David Chas, Rivière Jacques, Marone Chris
Department of Geosciences Pennsylvania State University University Park USA.
Now at The University of Texas Institute for Geophysics Austin USA.
J Geophys Res Solid Earth. 2021 Jul;126(7):e2020JB021588. doi: 10.1029/2020JB021588. Epub 2021 Jul 19.
Machine learning (ML) techniques have become increasingly important in seismology and earthquake science. Lab-based studies have used acoustic emission data to predict time-to-failure and stress state, and in a few cases, the same approach has been used for field data. However, the underlying physical mechanisms that allow lab earthquake prediction and seismic forecasting remain poorly resolved. Here, we address this knowledge gap by coupling active-source seismic data, which probe asperity-scale processes, with ML methods. We show that elastic waves passing through the lab fault zone contain information that can predict the full spectrum of labquakes from slow slip instabilities to highly aperiodic events. The ML methods utilize systematic changes in P-wave amplitude and velocity to accurately predict the timing and shear stress during labquakes. The ML predictions improve in accuracy closer to fault failure, demonstrating that the predictive power of the ultrasonic signals improves as the fault approaches failure. Our results demonstrate that the relationship between the ultrasonic parameters and fault slip rate, and in turn, the systematically evolving real area of contact and asperity stiffness allow the gradient boosting algorithm to "learn" about the state of the fault and its proximity to failure. Broadly, our results demonstrate the utility of physics-informed ML in forecasting the imminence of fault slip at the laboratory scale, which may have important implications for earthquake mechanics in nature.
机器学习（ML）技术在地震学和地震科学中变得越来越重要。基于实验室的研究已经使用声发射数据来预测失效时间和应力状态，并且在少数情况下，相同的方法也被用于现场数据。然而，允许实验室地震预测和地震预报的潜在物理机制仍然没有得到很好的解决。在这里，我们通过将探测粗糙尺度过程的有源地震数据与机器学习方法相结合来解决这一知识差距。我们表明，穿过实验室断层带的弹性波包含可以预测从慢滑失稳到高度非周期性事件的全谱实验室地震的信息。机器学习方法利用P波振幅和速度的系统变化来准确预测实验室地震期间的时间和剪应力。机器学习预测在接近断层失效时精度提高，表明超声信号的预测能力随着断层接近失效而提高。我们的结果表明，超声参数与断层滑动速率之间的关系，以及进而系统演变的实际接触面积和粗糙刚度，使得梯度提升算法能够“了解”断层的状态及其接近失效的程度。广泛地说，我们的结果证明了物理信息机器学习在预测实验室尺度断层滑动紧迫性方面的效用，这可能对自然界中的地震力学具有重要意义。
