| Summary: | 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.
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