Predictive Geochemical Exploration: Inferential Generation of Modern Geochemical Data, Anomaly Detection and Application to Northern Manitoba

Abstract

Geochemical surveys contain an implicit data lifecycle or pipeline that consists of data generation (e.g., sampling and analysis), data management (e.g., quality assurance and control, curation, provisioning and stewardship) and data usage (e.g., mapping, modeling and hypothesis testing). The current integration of predictive analytics (e.g., artificial intelligence, machine learning, data modeling) into the geochemical survey data pipeline occurs almost entirely within the data usage stage. In this study, we predict elemental concentrations at the data generation stage and explore how predictive analytics can be integrated more thoroughly across the data lifecycle. Inferential data generation is used to modernize lake sediment geochemical data from northern Manitoba (Canada), with results and interpretations focused on elements that are included in the Canadian Critical Minerals list. The results are mapped, interpreted and used for downstream analysis through geochemical anomaly detection to locate further exploration targets. Our integration is novel because predictive modeling is integrated into the data generation and usage stages to increase the efficacy of geochemical surveys. The results further demonstrate how legacy geochemical data are a significant data asset that can be predictively modernized and used to support time-sensitive mineral exploration of critical minerals that were unanalyzed in original survey designs. In addition, this type of integration immediately creates the possibility of a new exploration framework, which we call predictive geochemical exploration. In effect, it eschews sequential, grid-based and fixed resolution sampling toward data-driven, multi-scale and more agile approaches. A key outcome is a natural categorization scheme of uncertainty associated with further survey or exploration targets, whether they are covered by existing training data in a spatial or multivariate sense or solely within the coverage of inferred secondary data. The uncertainty categorization creates an effective implementation pathway for future multi-scale exploration by focusing data generation activities to de-risk survey practices.