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In 2017, the Metal Earth multi‐disciplinary exploration project acquired a total of 921 km of regional deep seismic reflection profiles and 184 km of high‐resolution seismic reflection profiles in the Abitibi and Wabigoon greenstone belts of the Superior province of Canada. The Abitibi belt hosts several world‐class mineral deposits, whereas the Wabigoon has sparse economic mineral deposits. Two high‐resolution surveys in the Swayze area, a poorly endowed part of the western Abitibi greenstone belt, served as pioneer surveys with which to better understand subsurface geology and design a strategy to process other surveys in the near future. Swayze seismic data were acquired with crooked survey geometries along roads. Designing an effective seismic processing flow to address these geometries and complex geology required straight common midpoint lines along which both two‐dimensional prestack dip‐moveout correction and poststack migration processing were applied. The resulting seismic sections revealed steeply dipping and subhorizontal reflections; some correlate with folded surface rocks. An interpreted fault/deformation zone imaged in Swayze north would be a target for metal endowment if it extends the Porcupine–Destor structure. Because of the crooked line geometry of the surveys, two‐dimensional /three‐dimensional prestack time migration and swath three‐dimensional processing were tested. The prestack time migration algorithm confirmed reflections at the interpreted base of the Abitibi greenstone belt. The swath three‐dimensional images provided additional spatial details about the geometries of some reflections, but also had less resolution and did not detect many reflectors observed in two dimensions. Geological contacts between felsic, mafic and ultramafic greenstone rock layers are thought the main cause of reflectivity in the Swayze area. [ABSTRACT FROM AUTHOR]

• Translating the orogenic mineral system of the Swayze greenstone belt into mappable expressions. • Support vector machines and artificial neural network were used to predict gold prospectivity. • Stratified k-fold cross validation resulted in a mean accuracy of > 87% after k =10 folds. • The best predictor layers were proximity to D 2 and D 3 high strain zones, lithological contacts, and D 2 folds. • New deposit targets lie proximal to faulting, EM conductivities, and along chemically reactive rocks. Exploration for new mineral deposits has become increasingly difficult as new discoveries are being found under progressively deeper cover. To better understand and predict orogenic gold mineralization in the Archean Swayze greenstone belt, the essential ingredients of a mineral system are considered: 1) the source of gold and transport fluid ligands, 2) fluid pathways, 3) traps, and 4) the processes responsible for gold precipitation. The aim of this study is to use a mineral systems approach to help generate exploration targeting models using a spatial statistical method - weights of evidence (WofE) and data-driven machine learning tools, namely radial basis function neural networks (RBFNN) and support vector machine (SVM). The mineral prospectivity maps generated using the RBFNN and SVM machine learning methods were trained using the K-Fold cross-validation approach whereby 10 subsets of the data were used to train and test the model performance. The mean area under receiver operator curve after 10-fold cross-validations were 91% and 94% for the RBFNN models, and the SVM models obtained accuracies of 91% and 87%. Feature importance estimations obtained from both methods indicate that D 2 and D 3 high-strain zones, lithological contacts and D 2 folds (i.e., synclines and anticlines) were found to be important predictor layers for targeting potential prospective zones of gold mineralization. The machine learning algorithms used in this study are novel and pragmatic methods that use the full potential of geoscience datasets in mapping orogenic gold prospectivity. [ABSTRACT FROM AUTHOR]

• Evaluation of gold deposit distribution at various scales in the Swayze greenstone belt. • Gold deposits are spatially correlated with ESE-WNW- and ENE-WSW-striking faults. • Ripley's K-function cluster analysis suggests gold deposit occurs within 2 to 4 km of each other. • Fractal analysis indicates that gold deposit distribution is bifractal at scales <8 km and >8 km. • Over 96% of all gold deposits are found within 3 km distances from faulting. Understanding the spatial controls on mineral deposits is crucial in mineral exploration targeting. This paper uses a combination of spatial data analysis techniques, such as the distance distribution method, Fry, nearest neighbor index, Ripley's K-function multi-distance, fractal, and kernel density analysis, to better define controls on the distribution of gold deposits in the Swayze greenstone belt, Ontario, Canada. According to results obtained with the distance distribution method, D 2 structures were critical in controlling the spatial distribution of the gold mineralization, with 97% of the gold deposits occurring within 7 km of these structures (and 65% within 4 km). Fry analysis confirmed this spatial relationship with a close agreement between the orientations of the D 2 structures and gold deposit alignment. The outcome of Ripley's K-function cluster analysis suggests that gold deposits occur within 4 km distances of other gold deposits. These findings are consistent with the results from the fractal analysis, showing that gold deposit distribution is bifractal and that different geological features occurred at local (<8 km) versus regional (>8 km) scales. We conclude that higher-order D 2 structures and lower-order faults connected to the D 2 structures acted as pathways to focus gold-bearing hydrothermal fluids, thus leading to the observed concentration and distribution of gold deposits at the regional to local scales. [ABSTRACT FROM AUTHOR]