A spatial reconnaissance survey for gold exploration in a schist belt

Abstract

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.

GIS-based multi-criteria decision models for barite exploration in Nigeria’s Benue Trough

“…Also, some multi-criteria decision models may attempt to present additional parameters which may affect the performance level of the resultant model. The high reliability of the TOPSIS model in comparison to the ARAS and MOORA models has been previously validated by Tende et al 20 in the predictive mapping of gold deposits in northern Nigeria. Unlike the MOORA and ARAS models, a high-performance level for the TOPSIS model is attributed to its ability to generate weights for various alternatives by presenting a scalar parameter that integrates the best and worst alternative measures in deducing the relative performance for every alternative.…”

“…In the predictive mapping of mineral resources, the use of multi-criteria decision models has been credited with a substantial degree of accuracy and has been recommended as a viable exploration tool 20,[70][71][72] . In this study, high accuracy levels in the prediction of barite occurrence were validated using the ARAS, MOORA, and TOP-SIS models.…”

“…A false color composite imagery generated from the first three principal components enhanced lithological variations across the study area. Lithological units hosting barite mineralization were extracted and subjected to buffering as a means of generating rasterized distances to existing host rock 20 . Regional structural data designated as lineaments were extracted from a high-resolution digital elevation model (DEM) using manual digitisation 48 .…”

“…Apart from the limitations related to technological advancement, other characteristic inhibitions include inaccessible terrains and the excessive cost of exploration exercises. The application of RS and GIS has been proven to be an effective remedy as it has been adjudged as the most proficient option and is ascribed with enormous achievements concerning mineral exploration 12 , 20 .…”

A historical review and analysis on MOORA and its fuzzy extensions for different applications

“…The FUCOM provides criteria weights, and FUCOM-MOOSRA outperformed FUCOM-MOORA in exploring skarn iron deposits in Iran. Tende et al [ 191 ] unravel important attributes aiding gold mineralization by combining distance correlation analysis, prediction area plots, and MCDM methods involving TOPSIS, MOORA, and ARAS in the schist belt of Nigeria. Bakhtavar et al [ 192 ] proposed an integrated methodology integrating a fuzzy cognitive map with fuzzy MOORA for prioritizing the shaft production location in underground mines, considering economic and technical criteria, which is validated by the fuzzy TOPSIS method.…”