Detection of mining-induced microseismicity through a deep convolutional neural network

The underground extraction of mineral resources is often closely linked to induced microseismic events. The use of a seismic network to continuously monitor mining-induced seismicity to reduce risks and improve operational safety is common. For this monitoring to be effective, a comprehensive catalog of microseismic events, containing low-to high-magnitude events, is essential to evaluate the response of the rock mass to mining activities. However, detecting low-magnitude events based on manual picking or automated conventional approaches has been challenging in mining environments owing to the inherent noise level. Recent advancements in deep learning and data-driven methods, particularly Convolutional Neural Networks (CNNs) trained on extensive seismic datasets, have shown improved capabilities in automated event detection and arrival phase picking on seismic data recorded by regional seismic networks. In this study, we assessed the performance of PhaseNet, a deep learning arrival-time picking method, in detecting the P- and S-wave arrivals of mining-induced microseismic events at different noise levels. As access to high-quality, labeled microseismic datasets for such mining applications is rare, a realistic three-component synthetic dataset was generated using full-waveform modeling. This simulation accounted for the geological conditions and network geometry specific to a mine in Ontario, Canada. The mine, which integrates copper and nickel operations, experiences considerable mining-induced earthquakes annually, posing risks to miners and infrastructure. The simulation includes a variety of source mechanisms with different magnitudes and offers more than 270,000 labeled seismograms. The results from the PhaseNet-trained model, which utilized the simulated dataset, demonstrated its effectiveness in managing noisy waveforms. This capability allows the detection of low-magnitude events within the mine environment, which may be overlooked by traditional methods. Furthermore, the model shows high accuracy in picking both the P- and S-wave arrival times, achieving precision rates exceeding 0.9. Tests on real data were performed in three different scenarios. The first scenario involves training the model exclusively using real data. The second scenario combines synthetic and real data to retrain the model previously trained with synthetic data only. Finally, the third scenario focuses on retraining the pre-trained model using only synthetic data. All these trained models were used to evaluate the performance on the real test dataset. The results indicate that the model retrained with synthetic and real seismograms yielded the best arrival time predictions for the mine dataset.

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