Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier

Abstract

Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used Random Forests (RF), a supervised machine learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical sampling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data sampling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

“…Machine learning algorithms (MLA) have emerged as powerful tools to deal with massive datasets and recurring tasks in recent years. Several works have used MLA to solve different geoscienti c problems, such as geological mapping (e.g., Costa et al, 2019;Cracknell et al, 2014;Kuhn et al, 2020Kuhn et al, , 2018Radford et al, 2018), data-driven mineral prospectivity mapping (e.g., Brandmeier et al, 2020;Laborte, 2016, 2015;Prado et al, 2020;Rodriguez-Galiano et al, 2015;Zhang et al, 2021), anomaly detection, among many others (see Dramsch, 2020, and references therein). Speci cally, in mineralogy, MLA have been used for mineral identi cation and classi cation from rock thin sections images (e.g., Borges and Aguiar, 2019;Rubo et al, 2019a) or from drill cores (e.g., Koch et al, 2019), and for the calculation of mineral formulas, e.g., for amphiboles (Li et al, 2020).…”

Porosity Prediction With Uncertainty Quantification From Multiple Seismic Attributes Using Random Forest

“…The uncertainty of that estimate is either unavailable or comes at an extra cost. In terms of classification problems, for machine learning models that can inherently or indirectly acquire class membership probabilities, uncertainty quantification is relatively convenient (Cracknell & Reading, 2013;Kuhn et al, 2018Kuhn et al, , 2020Roodposhti et al, 2019;Wong et al, 2002). For example, Cracknell and Reading (2013) used the variance calculated from class membership probabilities to estimate the uncertainty of RF and the support vector machine for identification of lithology.…”