An assessment of multi-criteria predictive model for alluvial sapphire exploration in Belel and environs, Adamawa Yadé-domain, northern Cameroon

Fru, Mary Immaculate Neh; Suh, Cheo Emmanuel; Tende, Andongma W.; Embui, Fonabe Victor; Carrino, Thais Andressa

doi:10.1007/s43217-022-00122-6

Cited by 1 publication

(1 citation statement)

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“…In most cases, a combination of existing geoscientific data such as geological, geophysical, geochemical, structural, and satellite data are indispensable requirements for an effective and thorough investigation of mineral resource occurrences 1 , 2 . Remote sensing (RS) and GIS-based techniques are a reliable platform for the integration of all these pools of geodata 3 , 4 . Sabins 5 described RS as the science of acquiring, processing, and interpreting images and related data, acquired from aircraft and satellites that record the interaction between matter and electromagnetic energy.…”

Section: Introductionmentioning

confidence: 99%

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

Gajere,

Adekeye,

Tende

et al. 2024

Sci Rep

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Spatial predictive mapping using geographic information system (GIS) is considered an invaluable tool for reconnaissance-scale exploration of mineral resources. In this study, geospatial data on geophysics, remote sensing, and structural and lithological attributes were systematically integrated to prospect barite potential zones within the Mid-Nigerian Benue Trough (MBT). Correlation attribute evaluation was used to establish the relationship between mineral deposit occurrences and geospatial data, while data integration was implemented using the Multi-Objective Optimization by Ratio Analysis (MOORA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Additive Ratio Assessment (ARAS) multi-criteria models. Here we show that the correlation attribute evaluation suggests that barite occurrences displayed a strong correlation with spatial data on lineament density, ferric iron alteration, and potassium to thorium (K/Th) ratio, whereas a weak correlation was observed with spatial data on the first vertical derivative (FVD), proximity to the host rock, and ferrous iron alteration. Here we report that the quantitative estimation of predictive models indicated that very high predictive zones for barite occurrences accounted for 19% of all the models. The accuracy assessment using Receiver Operating Characteristic (ROC)/Area Under the Curve (AUC) showed prediction levels above 78% for all models. The effectiveness of the spatial application of multi-criteria decision models makes them a reliable tool for barite exploration within the Mid-Nigerian Benue Trough (MBT) and other geologically similar environments.

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