Mapping structural complexity using geophysics: A new geostatistical approach applied to greenstone belts of the southern Superior Province, Canada

With the increasing size and complexity of geoscientific databases, statistical tools and machine learning algorithms are needed to highlight subtle patterns within dense data clouds and reduce human-related bias in geological mapping. This study presents a new tool that maps structural complexity using circular variance and spherical dispersion in two Archean greenstone belts near Dryden and Timmins in the Superior Province, Ontario. Structural complexity quantitatively highlights areas where structures are folded, juxtaposed, and/or parallel. Bedding measurements and autodetected aeromagnetic lineaments are used as inputs to test if structural complexity can refine or add confidence to existing geological interpretations. Linear, low variance trends frequently delineate the location and extent of deformation zones, which likely reflect parallel transposed fabrics in high strain corridors. Areas of high variance spatially correlate with fold traces. Sharp boundaries between high and low variance anomalies are interpreted as faults and/or shear zones that juxtapose structural blocks. The near continuous and representative nature of autodetected magnetic lineaments was effective in capturing structural complexity at a regional scale; however, spherical dispersion captured the highest resolution of structural complexity at local scales ( μ + 1σ). Additionally, the gold grade of large deposits (> 1 Moz) increases with proximity to high variance anomalies. When paired with expert knowledge, these techniques will increase repeatability in future exploration endeavors, making exploration a more rigorous process with increased confidence.