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A novel fusion python application of data mining techniques (DMT) wasdesigned and implemented to locate, identify, and delineate the subsurfacestructural pattern (SSP) of source rocks for the features of interest underlainthe study area. The techniques of machine learning tools (MLT) helped to definemagnetic anomaly source (MAS) rock and the various depths of these subsurfacesource rock features. The principal objective is to use straightforward DMT tolocate magnetic anomaly features of interest that host mineralization. Therequired geo-referenced radiometric data, which facilitated the delineation ofSSP, were sufficiently covered by combining the application of the OasisMontaj\c{opyright} 2014 source parameter imaging functions. Relevance basicfiltering techniques of data reduction were used to improve the signal-to-noise(S/N) ratio and hence automatically determine depths to the various engrossedfeatures from gridded geo-referenced airborne magnetic datasets before the DMTapplication was performed. Geological source rock models (GSRM) (i.e., rockcontacts, dykes) served as the delineated features based on their structuralindex (SI) values. The anomalies were perpendicularly oriented, with fewinconsequential nonvertical features, and all were generally aligned in NNE-SSWand NE-SW directions. The DMT approach showed that magnetic anomaly patterns(MAP) control the SSP and the ground surface stratigraphy (GSS) on a geologicaltime-scale (GTS) by fusing the subsurface gravitational structural features(SGSF) in the area. The DMT facilitated the determination of depths to thesesubsurface geological source rock features with a maximum depth ofapproximately 1.277 km using a 3x3 window size to map the concealed features ofinterest.

A novel fusion Python application of data mining techniques to evaluate airborne magnetic datasets