Knowledge-driven stochastic modeling of geological geometry features conditioned on drillholes and outcrop contacts

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Geometry features in geosciences are crucial for understanding both near-surface and deeper Earth’s interior structures. Geometry features have traditionally been delineated through diverse data sources such as surface mapping, geophysics, and core samples. However, the quantitative integration of geological knowledge and insights with the geoscientific data remains insufficiently addressed in the model construction. We have formulated a novel framework that employs stochastic level set simulation to model subsurface geometric features. The uniqueness of our framework involves geological knowledge, represented by two-dimensional (2D) geological diagrams, in the numerical modeling using Procrustes analysis. We account for the geological diagrams’ intrinsic variability and uncertainty through transformations such as rotation, scaling, and translation. By using the designed loss functions, the resulting models align with the established geological knowledge and are also conditioned on the lithology from drillhole and surface observations (i.e., outcrop contacts). We apply the methodology to a field study of a Cu-Ni-PGE (copper-nickel-platinum group element) prospect hosted in a mafic intrusion, the Crystal Lake Gabbro (CLG), in northwest Ontario. Our study focuses on a single segment of the y-shaped CLG. The numerical outcomes of this application demonstrate that the incorporation of expert knowledge, drillholes, and surface data yields models with reliable geological geometry features, particularly the distribution of bottom boundary for intrusion models which is highly associated with economic mineralization.