Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets

Depth‐based correlation analysis between the density of lineaments in the crystalline basement's weathered zones and groundwater occurrences within the Voltaian basin, Ghana

“…The area is dominated by eastern trending structures with a population of 1493 (50.61%). Dense regions of structural lineaments are essential to groundwater delineation because they depict low‐pressure zones where fluids such as groundwater can settle (Forson et al., 2020; Forson, Wemegah, et al., 2022). The occurrence of structures within the Voltaian basin as established in the literature is a good indicator of potential groundwater zones since groundwater movement and storage are largely dependent on the structures in the area (Yidana et al., 2019).…”

The geology and geochemistry of the Rhyacian Josephine gold deposit, Northwest Ghana

“…The Birimian straddles countries like Mali, Ivory Coast, Ghana, Niger, Senegal and Burkina Faso. Architecturally, the Birimian in Ghana is made up of five northeast-southwest trending greenstone belts (Amponsah 2012;Perrouty et al 2012;Nunoo et al 2016;Forson et al 2022Agra et al 2023) as well as a north-south oriented greenstone belt (Amponsah et al 2015;Amponsah et al 2016aAmponsah et al , 2016bAsiedu et al 2019;Sapah et al 2020;Nunoo et al 2022aNunoo et al , 2022b, with intervening basins (Amponsah 2016a;Davis et al 1994). The Wa-Lawra belt in Ghana, which runs in a northsouth direction, forms a segment of the broader Boromo belt that stretches northward into Burkina Faso (Baratoux et al 2011;Block et al 2016;Feng et al 2018Feng et al , 2019.…”

Random forest-based mineral prospectivity modelling over the Southern Kibi–Winneba belt of Ghana using geophysical and remote sensing techniques

“…26 Efforts to understand the mineral potential of the SKWB have resulted in the use of both knowledge-driven and bivariate data-driven methods to generate an MPM. 11,27 To highlight the mineral prospects as well as target zones that can guide future exploration programmes over the SKWB, this study determines which category of geospatial datasets whose thematic layers can produce the best accuracy when employed in delineating prospective zones of mineral occurrences. Also, by taking into cognisance the superiority of ML algorithms over traditional data-driven methods, and in order to contribute to narrowing down to more favourable zones for the delineation of gold mineral deposits, this study employs the RF algorithm to develop MPM over the SKWB based on three scenarios: thematic layers (a) derived from geophysical datasets comprising magnetic, radiometric and gravity, (b) derived from remote sensing data (Landsat 8 imagery) and (c) derived from geophysical and remote sensing datasets.…”

“…9,10 However, one major disadvantage of using knowledge-driven mathematical frameworks is the extreme subjectiveness involved in assigning weights to the thematic layers that would be synthesised to produce the MPM. 11 For the data-driven frameworks (which could be bivariate or multivariate), regions of known mineral deposit occurrences are used in the production of an MPM. 11,12,13 It is noteworthy that several multivariate-based statistical approaches such as machine learning (ML) algorithms are included in the mathematical frameworks used in data-driven-based MPM.…”

“…11 For the data-driven frameworks (which could be bivariate or multivariate), regions of known mineral deposit occurrences are used in the production of an MPM. 11,12,13 It is noteworthy that several multivariate-based statistical approaches such as machine learning (ML) algorithms are included in the mathematical frameworks used in data-driven-based MPM. 14 Several studies have demonstrated the efficacy of ML algorithms (multivariate data-driven methods) in MPM and have subsequently lauded their superiority over the bivariate data-driven methods in a variety of geologic environments.…”