We use seismic waves that pass through the hypocentral region of the 2016 M6.5 Norcia earthquake together with Deep Learning (DL) to distinguish between foreshocks, aftershocks and time-to-failure (TTF). Binary and N-class models defined by TTF correctly identify seismograms in test with > 90% accuracy. We use raw seismic records as input to a 7 layer CNN model to perform the classification. Here we show that DL models successfully distinguish seismic waves pre/post mainshock in accord with lab and theoretical expectations of progressive changes in crack density prior to abrupt change at failure and gradual postseismic recovery. Performance is lower for band-pass filtered seismograms (below 10 Hz) suggesting that DL models learn from the evolution of subtle changes in elastic wave attenuation. Tests to verify that our results indeed provide a proxy for fault properties included DL models trained with the wrong mainshock time and those using seismic waves far from the Norcia mainshock; both show degraded performance. Our results demonstrate that DL models have the potential to track the evolution of fault zone properties during the seismic cycle. If this result is generalizable it could improve earthquake early warning and seismic hazard analysis.

我们使用穿过 2016 年 M6.5 Norcia 地震震中区域的地震波以及深度学习 （DL） 来区分前震、余震和失效时间 （TTF）。由 TTF 定义的二进制和 N 级模型可以正确识别测试中的地震图，准确率为 > 90%。我们使用原始地震记录作为 7 层 CNN 模型的输入来执行分类。在这里，我们表明 DL 模型成功地区分了主震前后的地震波，这与实验室和理论预期一致，即在失效时突然变化和震后逐渐恢复之前裂纹密度的逐渐变化。带通滤波地震图（低于 10 Hz）的性能较低，这表明 DL 模型从弹性波衰减的细微变化中学习。验证我们的结果确实为断层特性提供了代理的测试包括：使用错误的主震时间训练的 DL 模型以及使用远离 Norcia 主震的地震波的 DL 模型;两者都显示性能下降。我们的结果表明，DL 模型有可能跟踪地震周期中断层带特性的演变。如果这个结果是可推广的，它可以改进地震预警和地震灾害分析。