Rock fracture mechanisms can be inferred from moment tensors (MT) inverted from microseismic events. However, MT can only be inverted for events whose waveforms are acquired across a network of sensors. This is limiting for underground mines where the microseismic stations often lack azimuthal coverage. Thus, there is a need for a method to invert fracture mechanisms using waveforms acquired by a sparse microseismic network. Here, we present a novel, multi-scale framework to classify whether a rock crack contracts or dilates based on a single waveform. The framework consists of a deep learning model that is initially trained on 2400000+ manually labelled field-scale seismic and microseismic waveforms acquired across 692 stations. Transfer learning is then applied to fine-tune the model on 300000+ MT-labelled lab-scale acoustic emission waveforms from 39 individual experiments instrumented with different sensor layouts, loading, and rock types in training. The optimal model achieves over 86% F-score on unseen waveforms at both the lab- and field-scale. This model outperforms existing empirical methods in classification of rock fracture mechanisms monitored by a sparse microseismic network. This facilitates rapid assessment of, and early warning against, various rock engineering hazard such as induced earthquakes and rock bursts.

中文翻译：

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稀疏微震网络多尺度裂缝类型分类的迁移学习框架

岩石破裂机制可以通过微震事件反演的力矩张量 (MT) 来推断。然而，MT 只能针对通过传感器网络获取波形的事件进行反演。这对于地下矿井来说是有限的，因为微震台通常缺乏方位角覆盖。因此，需要一种使用稀疏微震网络获取的波形来反演断裂机制的方法。在这里，我们提出了一种新颖的多尺度框架，可以根据单个波形对岩石裂纹是否收缩或扩张进行分类。该框架由一个深度学习模型组成，该模型最初是在 692 个台站采集的 2400000 多个手动标记的现场规模地震和微震波形上进行训练的。然后应用迁移学习对来自 39 个单独实验的 300000 多个带有 MT 标记的实验室规模声发射波形微调模型，这些实验在训练中使用了不同的传感器布局、负载和岩石类型。最佳模型在实验室和现场规模的未见波形上实现了超过 86% 的 F 分数。该模型在稀疏微震网络监测的岩石破裂机制分类方面优于现有的经验方法。这有助于对诱发地震和岩爆等各种岩石工程灾害进行快速评估和预警。