The development of predictive maps for geothermal resources is fundamental for its exploration across Nigeria. In this study, spatial exploration data consisting of geology, geophysics and remote sensing was initially analysed using the Shannon entropy method to ascertain a correlation to known geothermal manifestation. The application of statistical index, frequency ratio and weight of evidence modelling was then used for integrating every predictive data for the generation of geothermal favourability maps. The receiver operating/area under curve (ROC/AUC) analysis was then employed to ascertain the prediction accuracy for all models. Basically, all spatial data displayed a significant statistical correlation with geothermal occurrence. The integration of these data suggests a high probability for geothermal manifestation within the central part of the study location. Accuracy assessment for all models using the ROC/AUC analysis suggests a high prediction capability (above 75%) for all models. Highest prediction accuracy was obtained from the frequency ratio (83.3%) followed by the statistical index model (81.3%) then the weight of evidence model (79.6%). Evidence from spatial and predictive analysis suggests geological data integration is highly efficient for geothermal exploration across the middle Benue trough.

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This study evaluates the geological attributes of rocks within the Abu Ghalaga area using spatial, geochemical, and petrographic approaches. ASTER and Landsat imagery processed using band ratio and principal component analysis were used to map hydrothermal alterations, while a regional tectonic evaluation was based on automated extraction of lineaments from a digital elevation model. Geochemical and petrographic analyses were then employed for discrete scale evaluation of alteration patterns of rocks across the study location. Based on satellite image processing, alteration patterns across the study area are widespread, while evidence from lineament analysis suggests a dominant NW–SE tectonic trend accompanied by a less dominant ENE–WSW direction. The different rock units exposed in the studied district are arranged chronologically from oldest to youngest as arc metavolcanic group (basalt and rhyolite), arc metagabbro–diorite, gneissose granite (granodiorite and tonalite), and dykes (aplite and felsite). Various types of igneous and metamorphic rocks have propylitic, phyllic, and argillic zones. Geochemical data indicate that the studied rocks are classified into granite, granodiorite, gabbroic diorite, and gabbro. Geochemically, the rocks have a sub-alkaline magma type. The granodiorite–tonalite is derived from the calc–alkaline magma nature, while gabbro and diorite samples exhibit tholeiitic to calc–alkaline affinity. The tectonic setting of the studied rocks trends toward volcanic arc granite (VAG). Based on petrographic, geochemical, and remote analyses, sericitization, chloritization, epidotization, kaolinitization, carbonatization, and silicification are the main alteration types present in the study area. As a result of lineaments analysis, the existing fractures and structural planes form valid flow paths for mineral-bearing hydrothermal solutions.

Geological data integration and spatial analysis for structural elucidation are more assertive approaches for reconnaissance scale mineral exploration. In this study, several methods involving Fry analysis, distance correlation analysis, prediction area plots as well as knowledge driven predictive models including TOPSIS, ARAS and MOORA were systematically employed for unravelling the spatial geological attributes related to gold mineralisation. Additionally, statistical validation of knowledge driven predictive models were implemented using the Receiver Operating Characteristic/Area Under Curve analysis (ROC/AUC). The evidence from Fry and distance correlation analysis suggests that gold occurrence within parts of the Malumfashi schist belt of Nigeria is defined by a strong spatial association with the ENE-WSW as well as the NNE-SSW trending structures. The prediction area plot also revealed a robust spatial correlation between mineral occurrence and spatial data related to geological structures. The application of knowledge driven predictive models suggest a high favourability for gold occurrence within the southern, central, and north-eastern parts of the study location, while statistical validation using the ROC/AUC curves suggest a high prediction accuracy greater than 70% for all models. The geospatial analysis for mineral exploration within the Malumfashi area has unveiled an invaluable geological criterion for gold targeting with a considerable level of certainty.

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