Transfer learning framework for multi-scale crack type classification with sparse microseismic networks

Abstract

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.