标题： 机器学习预测实验室地震 显示英文标题

标题： Machine Learning Predicts Laboratory Earthquakes

摘要： 地震科学中，预测断层失效是一个基本但难以实现的目标。 在这里，我们展示通过倾听实验断层发出的声学信号，机器学习可以非常准确地预测其失效前的时间。 这些预测仅基于声学信号的瞬时物理特性，而不使用其历史信息。 令人惊讶的是，机器学习识别出一种来自断层区的信号，该信号以前被认为是一种低幅度噪声，它能够在实验地震周期中实现失效预测。 我们假设将这种方法应用于连续地震数据可能会在识别目前未知的信号、提供关于断层物理的新见解以及确定断层失效时间的界限方面取得重大进展。 显示英文摘要

摘要： Forecasting fault failure is a fundamental but elusive goal in earthquake science. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal, and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low-amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We hypothesize that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.