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f Lessons Learned: Deep Learning for Mineral Exploration
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, First EAGE Conference on Machine Learning in Americas, Sep 2020, Volume 2020, p.1 - 1
Abstract
The application of commercial-scale deep learning to mineral exploration is in its infancy, with the intersection of affordable computation, large data volumes, novel applications of 3D deep learning and commercial buy-in all contributing to recent developments. The DeepMine team at IBM have spent the last three years developing and applying 3D Convolutional Neural Networks (CNNs) to the prediction of economic-grade resource in a hard-rock mining context. The iterative process has resulted in successes and shortcomings, yielding valuable insights for the resource exploration community. The approach involves representing subsurface data as point cloud information, which is then input as voxelated training examples into the model. 3D CNN models were constructed on top of PyTorch’s Deep Learning framework, developed by Facebook’s AI research group. The success of the project has been judged primarily by the ability of the model to reliably predict economic mineralization at greater distances from existing drillhole data upon each enhancement. Most recent applications have begun to incorporate non-drillhole data sources, such as Vertical Time-Domain Electro-Magnetic (VTEM) and Airborne Gravity and Magnetic data. It was found that key challenges and opportunities existed in the areas of efficient and accurate data representation, bespoke data augmentation techniques and the 3D CNN formulation itself. New improvements continue to be made to the IBMs body of work on the subject, DeepMine.