Deep learning improves prediction of underground mining cavity shapes
Deep Learning Volumetric Segmentation for Prediction of Cavity Geometry in Underground Jet Boring Mining
Computational Engineering, Finance, and Science
Summary
Predicting the exact shape of cavities created by high-pressure water jets during uranium mining is challenging. The authors developed a deep learning model that predicts these cavity shapes more accurately using 3D data from the mining site. Their method improves on the existing simple cylindrical model, helping mining teams plan extraction more effectively. They also used explainable AI to understand which input factors most influence the model's predictions.
What this means in practice
- •For underground mining engineers: Use the model to better predict cavity shapes created during jet boring for improved mine planning and safety.
- •For geological survey teams: Incorporate 3D geological data and drilling logs with machine learning to enhance resource extraction predictions.
Authors
Giovanni Zingaro, Yuan Wang, Deepak Meena, Peter Cheng, Russell Buchanan, John Quilty, Robert Gracie
Abstract
The Jet Boring System (JBS) is a proprietary technology developed by Cameco Corporation to extract high-grade uranium ore at the Cigar Lake Mine in Northern Saskatchewan, Canada. The JBS remotely excavates ore using a high-pressure water jet that cuts cavities within an artificially frozen ore body. This study outlines a Deep Learning (DL)-based workflow that formulates the cavity prediction problem as a volumetric binary semantic segmentation task. A preprocessing pipeline assembles drilling and jetting records, cavity scans, and a mine-wide geology block model onto a common voxel grid, where each voxel in the prediction is labelled as consumed or non-consumed during jetting. A three-dimensional U-Net with a backfill residual branch is trained on historical field cavities. The model is benchmarked against a baseline consisting of a cylindrical cavity centered along the pilot hole, with a diameter defined by domain experts. The proposed workflow achieves an IoU of 0.601 $\pm$ 0.088 and an F$_1$-Score of 0.747 $\pm$ 0.077 on a held-out test set, improving on the cylindrical baseline currently used in cavity planning by 22.4% and 14.7%, respectively. An eXplainable Artificial Intelligence (XAI) feature attribution approach is applied to identify the most influential input driving the predicted cavity geometry. The accuracy of the proposed model demonstrates its potential to support mine planning.