Document Type : Original Research Paper

Authors

1 Department of Earth Science, School of Physical and Mathematical Science, University of Ghana, Legon-Accra, Ghana.

2 Department of Physics, School of Physical and Mathematical Sciences, University of Ghana, Legon-Accra, Ghana.

Abstract

This study was set out to delineate prospective zones of gold mineralization occurrence over the Julie tenement of Northwestern Ghana using two spatial statistical techniques, namely information value (IV) and weight of evidence (WofE) models. First, 110 locations, where gold (Au) mineralization has been observed, were identified by field survey results derived from highly anomalous geo-chemical assay datasets. Of these 110 locations, 77 (representing 70% of the known locations, where gold has been observed) were randomly selected for training the aforementioned models, and the remaining 33 (analogous to 30% of the known Au occurrence) were used for validation. Secondly, eleven mineral conditioning factors (evidential layers) comprising analytic signal, reduction-to-equator (RTE), lineament density (LD), porphyry density, potassium concentration, thorium concentration, uranium concentration, potassium-thorium ratio, uranium-thorium ratio, geology, and arsenic concentration layers were sourced from geo-physical, geological, and geo-chemical datasets. Subsequently, by synthesizing these eleven evidential layers using the two spatial statistical techniques, two mineral prospectivity models were created in a geographic information system (GIS) environment. Finally, the mineral prospectivity models produced were validated using the area under the receiver operating characteristics curve (AUC). The results obtained showed that the IV model produced had a higher prediction accuracy in comparison with the mineral predictive model produced by the WofE with their AUC scores being 0.751 and 0.743, respectively.

Keywords

Main Subjects

[1]. Bridge, G. (2004). Mapping the bonanza: geographies of mining investment in an era of neoliberal reform. The Professional Geographer, 56(3), 406–421.
[2]. Hilson, G. and Banchirigah, S. M. (2009).  Are alternative livelihood projects alleviating poverty in mining communities? experiences from Ghana. The Journal of Development Studies, 45(2), 172–196.
[3]. Forson, E. D., Menyeh, A., Wemegah, D. D., Danuor, S. K., Adjovu, I., and Appiah, I. (2020). Mesothermal gold prospectivity mapping of the southern Kibi-Winneba belt of Ghana based on fuzzy analytical hierarchy process, concentration-area (ca) fractal model and prediction-area (pa) plot. Journal of Applied Geophysics, 174, 103971.
[4]. Azumah Resources Limited (2018). The Julie Mineral Resource Estimate. Unpublished internal report.
[5]. Sun, T., Chen, F., Zhong, L., Liu, W., and Wang, Y. (2019). Gis-based mineral prospectivity mapping using machine learning methods: A case study from tongling ore district, eastern china. Ore Geology Reviews,109, 26–49.
[6]. Ma, Y., Zhao, J., Sui, Y., Liao, S., and Zhang, Z. (2020). Application of knowledge-driven methods for mineral prospectivity mapping of polymetallic sulfide deposits in the southwest indian ridge between 46 and 52 E. Minerals, 10(11), 970.
[7]. Riahi, S., Bahroudi, A., Abedi, M., Aslani, S., and Elyasi, G.-R. (2021). Integration of airborne geophysics and satellite imagery data for exploration targeting in porphyry Cu systems: Chahargonbad district, Iran. Geophysical Prospecting, 69(5), 1116–1137.
[8]. Forson, E. D. and Menyeh, A. (2023). Best worst method-based mineral prospectivity modeling over the central part of the southern Kibi-Winneba belt of Ghana. Earth Science Informatics, 16(2), 1657–1676.
[9]. Abedi, M., Torabi, S. A., Norouzi, G.-H., and Hamzeh, M. (2012). Electre iii: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of applied geophysics, 87, 9–18.
[10]. Daviran, M., Parsa, M., Maghsoudi, A., and Ghezelbash, R. (2022). Quantifying uncertainties linked to the diversity of mathematical frameworks in knowledge-driven mineral prospectivity mapping. Natural Resources Research, 31(5), 2271–2287.
[11]. Li, H., Li, X., Yuan, F., Jowitt, S. M., Dou, F., Zhang, M., Li, X., Li, Y., Lan, X., Lu, S. (2022). Knowledge-driven based three-dimensional prospectivity modeling of Fe–Cu skarn deposits; a case study of the fanchang volcanic basin, anhui province, eastern china. Ore Geology Reviews, page 105065.
[12]. Sabbaghi, H. and Tabatabaei, S. H. (2022). Application of the most competent knowledge-driven integration method for deposit-scale studies. Arabian Journal of Geosciences, 15(11), 1–10.
[13]. Carranza, E., Hale, M., and Faassen, C. (2008). Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore geology reviews, 33(3-4), 536–558.
[14]. Bai, H., Cao, Y., Zhang, H., Wang, W., Jiang, C., and Yang, Y. (2022). Applying data-driven-based logistic function and prediction-area plot to map mineral prospectivity in the qinling orogenic belt, central china. Minerals, 12(10), 1287.
[15]. Forson, E. D., Wemegah, D. D., Hagan, G. B., Appiah, D., Addo-Wuver, F., Adjovu, I., Otchere, F. O., Mateso, S., Menyeh, A., and Amponsah, T. (2022). Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets. Journal of African Earth Sciences, 190, 104504.
[16]. Zhang, S., Carranza, E. J. M., Xiao, K., Wei, H., Yang, F., Chen, Z., Li, N., and Xiang, J. (2022). Mineral prospectivity mapping based on isolation forest and random forest: Implication for the existence of spatial signature of mineralization in outliers. Natural Resources Research, 31(4), 1981–1999.
[17]. Zhang, Z., Wang, G., Carranza, E. J. M., Fan, J., Liu, X., Zhang, X., Dong, Y., Chang, X., and Sha, D. (2022). An integrated framework for data-driven mineral prospectivity mapping using bagging-based positive-unlabeled learning and Bayesian cost-sensitive logistic regression. Natural Resources Research, pages 1–20.
[18]. Amponsah, P. O. and Forson, E. D. (2023). Geospatial modelling of mineral potential zones using data-driven based weighting factor and statistical index techniques. Journal of African Earth Sciences, 206, 105020.
[19]. Harris, J., Grunsky, E., Behnia, P., and Corrigan, D. (2015). Data-and knowledge-driven mineral prospectivity maps for canada’s north. Ore Geology Reviews, 71, 788–803.
[20]. Zhang, N. and Zhou, K. (2015). Mineral prospectivity mapping with weights of evidence and fuzzy logic methods. Journal of Intelligent & Fuzzy Systems, 29(6), 2639–2651.
[21]. Chudasama, B., Torppa, J., Nykänen, V., Kinnunen, J., Lerssi, J., and Salmirinne, H. (2022). Target-scale prospectivity modeling for gold mineralization within the rajapalot au-co project area in northern Fennoscandian shield, finland. part 1: application of knowledge-driven-and machine learning-based-hybrid-expert systems for exploration targeting and addressing model-based uncertainties. Ore Geology Reviews, page 104937.
[22]. Salvi, S., Amponsah, P. O., Siebenaller, L., Béziat, D., Baratoux, L., and Jessell, M. (2016). Shear-related gold mineralization in northwest ghana: The julie deposit. Ore Geology Reviews, 78, 712–717.
[23]. Bourenane, H., Guettouche, M. S., Bouhadad, Y., and Braham, M. (2016). Landslide hazardmapping in the Constantine city, northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods. Arabian Journal of Geosciences, 9(2), 1–24.
[24]. Dickson, K. and Benneh, G. (1988). A new geography of Ghana: Longman group UK limited.
[25]. Feybesse, J.-L., Billa, M., Guerrot, C., Duguey, E., Lescuyer, J.-L., Milesi, J.-P., and Bouchot, V. (2006). The Paleoproterozoic Ghanaian province: geodynamic model and ore controls, including regional stress modeling. Precambrian Research, 149(3-4), 149–196.
[26]. Jessell, M. W., Amponsah, P. O., Baratoux, L., Asiedu, D. K., Loh, G. K., and Ganne, J. (2012). Crustal-scale transcurrent shearing in the Paleoproterozoic Sefwi-Sunyani-Comoe region, West Africa. Precambrian Research, 212, 155–168.
[27]. Amponsah, P. O., Kwayisi, D., Awunyo, E. K., Sapah, M. S., Sakyi, P. A., Su, B.-X., Lu, Y., and Nude, P. M (2023b). New evidence for crustal reworking and juvenile arc-magmatism during the Palaeoproterozoic Eburnean events in the Suhum basin, South-East Ghana. Geological Journal.
[28]. Block, S., Jessell, M., Aillères, L., Baratoux, L., Bruguier, O., Zeh, A., Bosch, D., Caby, R., and Mensah, E. (2016). Lower crust exhumation during paleoproterozoic (eburnean) orogeny, NW Ghana, West African craton: interplay of coeval contractional deformation and extensional gravitational collapse. Precambrian Research, 274, 82–109.
[29]. Nunoo, S., Hofmann, A., and Kramers, J. (2022). Geology, zircon U–Pb dating and εHf data for the Julie greenstone belt and associated rocks in NW Ghana: Implications for Birimian-to-Tarkwaian correlation and crustal evolution. Journal of African Earth Sciences, 186, 104444.
[30]. Agyei-Duodu, J. (2009). Geological Map of Ghana 1: 1 000 000. Geological Survey Department.
[31]. Feng, X., Wang, E., Ganne, J., Amponsah, P., and Martin, R. (2018). Role of volcano-sedimentary basins in the formation of greenstone-granitoid belts in the west african craton: a numerical model. Minerals, 8(2), 73.
[32]. Feng, X., Wang, E., Amponsah, P. O., Ganne, J., Martin, R., and Jessell, M. W. (2019). Effect of pre-existing faults on the distribution of lower crust exhumation under extension: numerical modelling and implications for NW Ghana. Geosciences Journal, 23(6), 961–975.
[33]. Sapah, M. S., Agbetsoamedo, J. E., Amponsah, P. O., Dampare, S. B., and Asiedu, D. K. (2021). Neodymium isotope composition of palaeoproterozoic Birimian shales from the Wa-Lawra belt, north-west Ghana: Constraint on provenance. Geological Journal, 56(4), 2072–2081.
[34]. Baratoux, L., Metelka, V., Naba, S., Jessell, M. W., Grégoire, M., and Ganne, J. (2011). Juvenile Paleoproterozoic crust evolution during the eburnean orogeny (2.2–2.0 ga), western burkina faso. Precambrian Research, 191(1-2), 18–45.
[35]. Amponsah, P. O., Salvi, S., Béziat, D., Siebenaller, L., Baratoux, L., and Jessell, M. W. (2015). Geology and geochemistry of the shear-hosted julie gold deposit, nw ghana. Journal of African Earth Sciences, 112, 505–523.
[36]. De Kock, G., Armstrong, R., Siegfried, H., and Thomas, E. (2011). Geochronology of the Birim supergroup of the West African craton in the Wa-Bolé region of west-central Ghana: Implications for the stratigraphic framework. Journal of African Earth Sciences, 59(1), 1–40.
[37]. Amponsah, P. O., Salvi, S., Didier, B., Baratoux, L., Siebenaller, L., Jessell, M., Nude, P. M., and Gyawu, E.A (2016). Multistage gold mineralization in the Wa-Lawra greenstone belt, NW Ghana: The Bepkong deposit. Journal of African Earth Sciences, 120, 220–237.
[38]. Milési, J., Feybesse, J., Pinna, P., Deschamps, Y., Kampunzu, H., Muhongo, S., Lescuyer, J., Le Goff, E., Delor, C., Billa, M. et al. (2004). Geological map of africa 1: 10,000,000, sigafrique project. In 20th conference of African geology, BRGM, Orléans, France, pages 2–7.
[39]. Holden, E.-J., Wong, J. C., Kovesi, P., Wedge, D., Dentith, M., and Bagas, L. (2012). Identifying structural complexity in aeromagnetic data: An image analysis approach to greenfields gold exploration. Ore Geology Reviews, 46, 47–59.
[40]. Forson, E. D., Menyeh, A., and Wemegah, D. D. (2021). Mapping lithological units, structural lineaments and alteration zones in the southern Kibi-Winneba belt of Ghana using integrated geophysical and remote sensing datasets. Ore Geology Reviews, 137, 104271.
[41]. Mohamed, A., Abdelrady, M., Alshehri, F., Mohammed, M. A., and Abdelrady, A. (2022). Detion of mineralization zones using aeromagnetic data. Applied Sciences, 12(18), 9078.
[42]. Yilmaz, I. (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from kat landslides (Tokat Turkey). Computers & Geosciences, 35(6),1125–1138.
[43]. Amponsah, T. Y., Wemegah, D. D., Danuor, S. K., and Forson, E. D. (2023c). Depth-based correlation analysis between density of lineaments in the crystalline basement’s weathered zones and groundwater occurrences within the Voltaian basin, Ghana. Geophysical Prospecting.
[44]. Bonham-Carter, G. F. (1994). Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists, 13:398.
[45]. Fu, C., Chen, K., Yang, Q., Chen, J., Wang, J., Liu, J., Xiang, Y., Li, Y., and Rajesh, H. (2021). Mapping gold mineral prospectivity based on weights of evidence method in southeast Asmara, Eritrea. Journal of African Earth Sciences, 176:104143.
[46]. Ozdemir, A. (2011). GIS-based groundwater spring potential mapping in the sultan mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. Journal of hydrology, 411(3-4), 290–308.
[47]. Van Westen, C. J. (1993). Application of geographic information systems to landslide hazard zonation.
[48]. Chen, Y. and Sui, Y. (2022). Dictionary learning for integration of evidential layers for mineral prospectivity modeling. Ore Geology Reviews, 141:104649.
[49]. Yin, B., Zuo, R., and Xiong, Y. (2022). Mineral prospectivity mapping via gated recurrent unit model. Natural Resources Research, 31(4), 2065–2079.
[50]. Forson, E. D., Amponsah, P. O., Hagan, G. B., and Sapah, M. S. (2023). Frequency ratio-based flood vulnerability modeling over the Greater Accra Region of Ghana. Modeling Earth Systems and Environment, 9(2), 2081–2100.
[51]. Eldosouky, A. M., Abdelkareem, M., and Elkhateeb, S. O. (2017). Integration of remote sensing and aeromagnetic data for mapping structural features and hydrothermal alteration zones in wadi Allaqi area, south eastern desert of Egypt. Journal of African Earth Sciences, 130:28–37.
[52]. Elkhateeb, S. O., Eldosouky, A. M., Khalifa, M. O., and Aboalhassan, M. (2021).  Probability of mineral occurrence in the southeast of aswan area, egypt, from the analysis of aeromagnetic data. Arabian Journal of Geosciences, 14(15), 1–12.
[53]. Wemegah, D. D., Preko, K., Noye, R. M., Boadi, B., Menyeh, A., Danuor, S. K., Amenyoh, T. et al. (2015). Geophysical interpretation of possible gold mineralization zones in kyerano, south-western Ghana using aeromagnetic and radiometric datasets. Journal of Geoscience and Environment Protection, 3(04), 67.
[54]. Forson, E. D. and Amponsah, P. O. (2023). Mineral prospectivity mapping over the Gomoa Area of Ghana's southern Kibi-Winneba belt using support vector machine and naive bayes. Journal of African Earth Sciences, 105024.
[55]. Craw, D. and Campbell, J. (2004). Tectonic and structural setting for active mesothermal gold vein systems, southern alps, new zealand. Journal of Structural Geology, 26(6-7), 995–1005.
[56]. Blake, F., Grant, K., MacKenzie, D., Scott, J., and Craw, D. (2019).  Surficial arsenic redistribution above gold-mineralised zones in east otago, new zealand. New Zealand Journal of Geology and Geophysics, 62(4), 573–587.
[57]. Elkhateeb, S. O. and Abdellatif, M. A. G. (2018). Delineation potential gold mineralization zones in a part of central eastern desert, egypt using airborne magnetic and radiometric data. NRIAG Journal of Astronomy and Geophysics, 7(2), 361–376.