Hydrodynamic Links between Shallow and Deep Mineralization Systems and Implications for Deep Mineral Exploration

Deep mineral exploration is increasingly important for finding new mineral resources but there are many uncertainties.Understanding the factors controlling the localization of mineralization at depth can reduce the risk in deep mineral exploration.One of the relatively poorly constrained but important factors is the hydrodynamics of mineralization.This paper reviews the principles of hydrodynamics of mineralization,especially the nature of relationships between mineralization and structures,and their applications to various types of mineralization systems in the context of hydrodynamic linkage between shallow and deep parts of the systems.Three categories of mineralization systems were examined,i.e.,magmatic-hydrothermal systems,structurally controlled hydrothermal systems with uncertain fluid sources,and hydrothermal systems associated with sedimentary basins.The implications for deep mineral exploration,including potentials for new mineral resources at depth,favorable locations for mineralization,as well as uncertainties,are discussed.

Development and application of feature engineered geological layers for ranking magmatic,volcanogenic,and orogenic system components in Archean greenstone belts

Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning.Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques,which can lead to ambiguity,geological oversimplification,and/or compounding subjective bias.Workflows that utilize minimally processed input variables attempt to overcome these issues,but often lead to convoluted and uninterpretable results.To address these challenges,new and enhanced feature engineering methods were developed by combining geological knowledge,understanding of data limitations,and a variety of data science techniques.These include non-Euclidean fluid pre-deformation path distance,rheological and chemical contrast,geologically constrained interpolation of characteristic host rock geochemistry,interpolation of mobile element gain/loss,assemblages,magnetic intensity,structural complexity,host rock physical properties.These methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the Superior Province near Timmins and Dryden,Ontario,respectively.Resulting feature maps represent conceptually significant components in magmatic,volcanogenic,and orogenic mineral systems.A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components,with a few exceptions attributed to biased training data or limited constraint data.The study also highlights the shared importance of several highly ranked features for the three mineral systems,indicating that spatially related mineral systems exploit the same features when available.Comparing feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment being related to fluid source.The study demonstrates that integrative studies leveraging multidisciplinary data and methodology have the potential to advance geological understanding,maximize data utility,and generate robust exploration targets.

Fuzzy Modeling of Surficial Uranium Prospectivity in British Columbia(Canada)with a Weighted Fuzzy Algebraic Sum Operator

This paper demonstrates knowledge-guided fuzzy logic modeling of regional-scale surficial uranium(U)prospectivity in British Columbia(Canada).The deposits/occurrences of surficial U in this region vary from those in Western Australia and Namibia;thus,requiring innovative and carefully-thought techniques of spatial evidence generation and integration.As novelty,this papers introduces a new weighted fuzzy algebraic sum operator to combine certain spatial evidence layers.The analysis trialed several layers of spatial evidence based on conceptual mineral system model of surficial U in British Columbia(Canada)as well as tested various models of evidence integration.Non-linear weighted functions of(a)spatial closeness to U-enriched felsic igneous rocks was employed as U-source spatial evidence,(b)spatial closeness to paleochannels as fluid pathways spatial evidence,and(c)surface water U content as chemical trap spatial evidence.The best models of prospectivity created by integrating the layers of spatial evidence for U-source,pathways and traps predicted at least 85%of the known surficial U deposits/occurrences in>10%of the study region with the highest prospectivity fuzzy scores.The results of analyses demonstrate that,employing the known deposits/occurrences of surficial U for scrutinizing the spatial evidence layers and the final models of prospectivity can pinpoint the most suitable critical processes and models of data integration to reduce bias in the analysis of mineral prospectivity.