Garson Mine Long Short-Term Memory Network

Digitalization of underground excavations has resulted in increasing access to large amounts of data for rock engineering professionals. Data-driven approaches, such as machine learning algorithms, present an opportunity to aid data interpretation. At Garson Mine, a 112-year-old nickel mine near Sudbury, Canada, the microseismic database is used to manually calibrate a complex mine-scale finite difference model, which is in turn used to assess seismic risk to inform mine operations and scheduling. The manual model calibration is tedious and time consuming. This research proposes a Long-Short Term Memory (LSTM) network to assist in finite difference model calibration by forecasting the stresses in the model. The LSTM is trained using the microseismic database, the geology and geomechanical parameters from the existing FLAC3D model. Two LSTM networks are developed and compared for Garson Mine: one that predicts the principal stresses and another that predicts the six-component stress tensor at each zone centroid in the FLAC3D model. Various LSTM network hyperparameters were analyzed to determine the optimal architecture for the two sets of targets, including: input encoding and pre-processing, training solver, network layer architecture, and cost function. Architectures were chosen based on three performance metrics: the corrected Akaike Information Criterion (AICc), coefficient of determination (R2), and percent capture (%C). This study found that similar LSTM network architectures are able to adequately predict both principal stresses and the complete stress tensor, however, the ensemble variance was larger when predicting the complete stress tensor. When predicting the principal stresses, AICc was -59.62, R2 was 0.996, and %C was 97%, and when predicting the six-component stress tensor AICc was -45.50, R2 was 0.997, and %C was 80%. This research represents progress towards continuous, automated calibration of complex numerical models, whereby earlier and more accurate forecasts of changes in stress conditions will allow earlier intervention and reaction to challenging stress environments, leading to increased safety of excavations and mine personnel.

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