Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modeling and Automation

• Published in: Mathematical geosciences Vol. 54; no. 3; pp. 533 - 572
• Main Authors: Leung, Raymond, Balamurali, Mehala, Lowe, Alexander
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022; Springer Nature B.V
• Subjects: Apexes; Automation; Bayesian analysis; Bayesian theory; Boundaries; Chemistry and Earth Sciences; Complex systems; Computation; Computer Science; Data acquisition; Domains; Drilling; Earth and Environmental Science; Earth Sciences; Gaussian process; Geochemistry; Geology; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Learning algorithms; Machine learning; Mineral exploration; Mineralization; Neural networks; Performance evaluation; Physics; Probability theory; Special Issue; Statistics for Engineering; Surface mining; Warping; Multi-class probability estimators; Mesh geometry; Geochemistry-based Bayesian deformable surface (GC-BDS); Geological boundaries; Bayesian computation; Ensemble classifiers
• ISSN: 1874-8961; 1874-8953
• DOI: 10.1007/s11004-021-09967-5

In surface mining, assay measurements taken from production drilling often provide useful information that enables initially inaccurate surfaces (for example, mineralization boundaries) created using sparse exploration data to be revised and subsequently improved. Recently, a Bayesian warping technique was proposed to reshape modeled surfaces using geochemical observations and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning (ML) into this warping framework to make the likelihood computation generalizable. The technique works by adjusting the position of vertices on the surface to maximize the integrity of modeled geological boundaries with respect to sparse geochemical observations. Its foundation is laid by a Bayesian derivation in which the geological domain likelihood given the chemistry, p ( g ∣ c ) , plays a role similar to p ( y ( c ) ∣ g ) for certain categorical mappings y : R K → Z . This observation allows a manually calibrated process centered on the latter to be automated, since ML techniques may be used to estimate the former in a data-driven way. Machine learning performance is evaluated for gradient boosting, neural network, random forest, and other classifiers in a binary and multi-class context using precision and recall rates. Once ML likelihood estimators are integrated into the surface warping framework, surface shaping performance is evaluated using unseen data by examining the categorical distribution of test samples located above and below the warped surface. Large-scale validation experiments are performed to assess the overall efficacy of ML-assisted surface warping as a fully integrated component within an ore grade estimation system, where the posterior mean is obtained via Gaussian process (GP) inference with a Matérn 3/2 kernel. This article illustrates an application of machine learning within a complex system where grade estimation is accomplished by integrating boundary warping with ML and other components. Graphic Abstract