Skip to main content

Advertisement

SpringerLink
Log in

Best worst method-based mineral prospectivity modeling over the Central part of the Southern Kibi-Winneba Belt of Ghana

  • Research
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

The best–worst method (BWM), though a recently developed multi-criteria decision-making (MCDM) technique, has its application evolving in recent years in addressing MCDM problems. By employing the BWM technique, this study generated a mineral prospectivity map (MPM), primarily essential for the delineation of prospective regions of gold (Au) deposits over the central part of the Southern Kibi-Winneba belt. The MPM produced was generated by integrating seven geoscientific thematic layers derived from geophysical and geological datasets within the study area. These thematic layers were used as the main criteria for the generation of MPM, whose respective weights were determined based on the BWM technique by three decision-makers with in-depth knowledge of the use of geoscientific datasets for mineral exploration. The assigned weights obtained from the three decision-makers for each of the seven thematic layers were combined and normalised to obtain BWM weight for each evidential layer. These weighted evidential layers were subsequently integrated to produce the mineral prospectivity map over the study area. The mineral prospectivity map produced based on the BWM was evaluated based on known areas of reported Au occurrence and subsequently evaluated based on the frequency ratio technique to ascertain the coherence between locations of known mineral occurrence and the delineated prospectivity classes obtained. The performance of the prospectivity model produced was further assessed using the receiver operating characteristics (ROC) curve. An ROC score of 0.808 was obtained for the mineral prospectivity model (MPM) generated. The ROC score obtained indicates that the MPM produced exhibits a conscionable outcome for gold exploration over the central part of the southern Kibi-Winneba belt.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The EGM2008 is available is available at the GFZ German Research Centre for Geoscience and website. Other geophysical datasets (magnetic and radiometric) and gold occurrence data would be made available on reasonable request.

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

References

  • Abedi M, Norouzi G-H (2016) A general framework of topsis method for integration of airborne geophysics, satellite imagery, geochemical and geological data. Int J Appl Earth Obs Geoinf 46:31–44

    Google Scholar 

  • Agyei Duodu, J., Hirdes, W., Toloczyki, M., Davis, D. W., Loh, G. K., Boamah, K., & Baba, M. (2009). Geological Map of Ghana, 1: 1,000,000. Geological Survey Department, Accra, Ghana (GSD) and Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, Germany (BGR). BGR Library, (2010).

  • Airò M, Ala G, Buccheri P, Caruso M, Fascella G, Giovino A, Mammano M (2015) Effect of weak magnetic fields on the in vitro propagation of genista aetnensis (raf. ex biv.) dc. In VI International Symposium on Production and Establishment of Micropropagated Plants 1155:387–392

    Google Scholar 

  • Airo M-L, Wennerström M (2010) Application of regional aeromagnetic data in targeting detailed fracture zones. J Appl Geophys 71(2–3):62–70

    Article  Google Scholar 

  • Amponsah TY, Danuor SK, Wemegah DD, Forson ED (2022a) Groundwater potential characterisation over the Voltaian basin using geophysical, geological, hydrological and topographical datasets. J Afr Earth Sc 192:104558

    Article  Google Scholar 

  • Amponsah, P. O., Forson, E. D., Sungzie, P. S., and Loh, Y. S. A. (2022b). Groundwater prospectivity modeling over the akatsi districts in the volta region of ghana using the frequency ratio technique. Model Earth Syst Environ, pages 1–19.

  • Aryafar A, Roshanravan B (2021) BWM-SAW: A new hybrid MCDM technique for modeling of chromite potential in the Birjand district, east of Iran. J Geochem Explor 231:106876

    Article  Google Scholar 

  • Aryee BN (2001) Ghana’s mining sector: its contribution to the national economy. Resour Policy 27(2):61–75

    Article  Google Scholar 

  • Carranza E, Hale M, Faassen C (2008) Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geol Rev 33(3–4):536–558

    Article  Google Scholar 

  • Carranza EJM (2010) Improved wildcat modelling of mineral prospectivity. Resour Geol 60(2):129–149

    Article  Google Scholar 

  • Carranza EJM, Laborte AG (2015) Data-driven predictive mapping of gold prospectivity, baguio district, philippines: Application of random forests algorithm. Ore Geol Rev 71:777–787

    Article  Google Scholar 

  • Carvalho C, Anjos R, Veiga R, Macario K (2011) Application of radiometric analysis in the study of provenance and transport processes of brazilian coastal sediments. J Environ Radioact 102(2):185–192

    Article  Google Scholar 

  • Chen Y, Wu W (2017) Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data. Aust J Earth Sci 64(5):639–651

    Article  Google Scholar 

  • Daviran M, Maghsoudi A, Cohen DR, Ghezelbash R, Yilmaz H (2020) Assessment of various fuzzy c-mean clustering validation indices for mapping mineral prospectivity: combination of multifractal geochemical model and mineralization processes. Nat Resour Res 29:229–246

    Article  Google Scholar 

  • Daviran M, Maghsoudi A, Ghezelbash R, Pradhan B (2021) A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Comput Geosci 148:104688

    Article  Google Scholar 

  • Daviran M, Parsa M, Maghsoudi A, Ghezelbash R (2022) Quantifying uncertainties linked to the diversity of mathematical frameworks in knowledge-driven mineral prospectivity mapping. Nat Resour Res 31(5):2271–2287

    Article  Google Scholar 

  • Davis DW, Hirdes W, Schaltegger U, Nunoo EA (1994) U Pb age constraints on deposition and provenance of Birimian and gold-bearing Tarkwaian sediments in Ghana West Africa. Precambrian Res 67(1–2):89–107

    Article  Google Scholar 

  • Deng X, Li L, Tan Y (2017) Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity. ISPRS Int J Geo Inf 6(4):103

    Article  Google Scholar 

  • Dentith M, Mudge TS (2014) Geophysics for the mineral exploration geoscientist. Cambridge University Press

    Book  Google Scholar 

  • Dickson B, Scott K (1997) Interpretation of aerial gamma-ray surveys-adding the geochemical factors. J Aust Geol Geophys 17:187–200

    Google Scholar 

  • Ðord¯evic´, D., Stojic´, G., Stevic´, Ž., Pamucˇar, D., Vulevic´, A., and Mišic´, V. (2019). A new model for defining the criteria of service quality in rail transport: The full consistency method based on a rough power heronian aggregator. Symmetry, 11(8):992.

  • Dove, K. (1991). Geology of the I = 4_field sheets nos. 29 and 31, Winneba SW and NW. Archive Report 25. Ghana Geological Survey Department, Accra.

  • Dzigbodi-Adjimah K (2004) The mineralogy and petrography of the ferruginous manganese rocks at mankwadzi, ghana. J Afr Earth Sc 38:293–315

    Article  Google Scholar 

  • Eberle DG, Paasche H (2012) Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites. Geophysics 77(4):B167–B176

    Article  Google Scholar 

  • Eisenlohr, B. (1989). The structural geology of Birimian and Tarkwaian rocks of southwest Ghana: Republic of Ghana, Federal Republic of Germany, Technical Cooperation Project no. 80.2040. 6. BGR.

  • 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 Geol Rev. 137(104271).

  • 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 (c-a) fractal model and prediction-area (p-a) plot. J. Geophys, 174(103971).

  • Forson, E. D., Wemegah, D. D., Hagan, G. B., Appiah, D., Addo-Wuver, F., Adjovu, I., Otchere, F. O., Menyeh, A., and Amponsah, T. (2022a). Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets. J. African Earth Sci, 190.

  • Forson, E. D., Amponsah P. O., Hagan, G. B., and Sapah M. S. (2022b). Frequency ratio-based flood vulnerability modeling over the Greater Accra Region of Ghana. Model Earth Syst Environ, pages 1 – 20.

  • Geodita Resources Ltd (2007). Exploration summary on the gomoa mangoase licence.Unpublished internal report.

  • Geodita Resources Ltd (2013). Drill report on gomoa mangoase concession. Unpublished internal report.

  • Griffis, J. R., Barning, K., Agezo, L. F., and Akosah, K. F. (2002). Gold deposits of ghana. In: Graphic Evolution Ltd. Ontario, Canada. Minerals Commission of Ghana.

  • Herman MW, Koczkodaj WW (1996) A monte carlo study of parwise comparison. Inf Process Lett 57(1):25–29

    Article  Google Scholar 

  • Hirdes W, Davis DW, Eisenlohr BN (1992) Reassessment of Proterozoic granitoid ages in Ghana on the basis of U/Pb zircon and monazite dating. Precambr Res 56(1–2):89–96

    Article  Google Scholar 

  • Jenks GF (1963) Generalization in statistical mapping. Ann Assoc Am Geogr 53(1):15–26

    Article  Google Scholar 

  • Juliani C, Ellefmo SL (2019) Prospectivity mapping of mineral deposits in northern norway using radial basis function neural networks. Minerals 9(2):131

    Article  Google Scholar 

  • Kapstein, E. and Kim, R. (2011). The socio-economic impact of newmont ghana gold limited. Stratcomm Africa.

  • Klemd R, Hünken U, Olesch M (2002) Metamorphism of the country rocks hosting gold–sulfide-bearing quartz veins in the paleoproterozoic southern kibi-winneba belt (se-ghana). J Afr Earth Sc 35(2):199–211

    Article  Google Scholar 

  • Leube A, Hirdes W, Mauer R, Kesse GO (1990) The early proterozoic birimian supergroup of ghana and some aspects of its associated gold mineralization. Precambr Res 46(1–2):139–165

    Article  Google Scholar 

  • Mansouri E, Feizi F, Rad AJ, Arian M (2017) A comparative analysis of index overlay and topsis (based on ahp weight) for iron skarn mineral prospectivity mapping, a case study in sarvian area, markazi province, iran. Maden Tetkik Ve Arama Dergisi 155(155):147–160

    Google Scholar 

  • McKay G, Harris J (2016) Comparison of the data-driven random forests model and a knowledge-driven method for mineral prospectivity mapping: A case study for gold deposits around the huritz group and nueltin suite, nunavut, canada. Nat Resour Res 25(2):125–143

    Article  Google Scholar 

  • McKinley J, Deutscht C, Neufeldt C, Patton M, Cooper M, Young M (2014) Use of geostatistical bayesian updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration. J South Afr Inst Min Metall 114(8):575–575

    Google Scholar 

  • Newmont Ghana Limited (2006). Terminal report on gomoa prospecting license (pl3/66).Unpublished internal report.

  • Nyarko E, Aseidu D, Osea S, Dampare S, Zakaria N, Hanson J, Osei J, Enti-Brown S, Tulasi D (2012) Geochemical characteristics of the basin-type granitoids in the winneba area of ghana. Proc Int Acad Ecol Environ Sci 2(3):177

    Google Scholar 

  • Nykänen V, Lahti I, Niiranen T, Korhonen K (2015) Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt, Northern Finland. Ore Geol Rev 71:853–860

    Article  Google Scholar 

  • Oladele S, Ayolabi E, Dublin-Green C (2016) Structural characterization of the nigerian sector of benin basin using geopotential field attributes. J Afr Earth Sc 121:200–209

    Article  Google Scholar 

  • Parsa M, Maghsoudi A (2021) Assessing the effects of mineral systems-derived exploration targeting criteria for Random Forests-based predictive mapping of mineral prospectivity in Ahar-Arasbaran area. Iran Ore Geology Reviews 138:104399

    Article  Google Scholar 

  • Perrouty S, Aillères L, Jessell MW, Baratoux L, Bourassa Y, Crawford B (2012) Revised eburnean geodynamic evolution of the gold-rich southern ashanti belt, ghana, with new field and geophysical evidence of pre-tarkwaian deformations. Precambr Res 204:12–39

    Article  Google Scholar 

  • Porwal A, Carranza E, Hale M (2004) A hybrid neuro-fuzzy model for mineral potential mapping. Math Geol 36(7):803–826

    Article  Google Scholar 

  • Porwal A, Carranza EJM, Hale M (2006) A hybrid fuzzy weights-of-evidence model for mineral potential mapping. Nat Resour Res 15(1):1–14

    Article  Google Scholar 

  • Ramadass G, SubhashBabu A, Laxmi GU (2015) Structural analysis of airborne radiometric data for identification of kimberlites in parts of eastern dharwar craton. Int J Sci Res 4(4):2375–2380

    Google Scholar 

  • Ramezanali AK, Feizi F, Jafarirad A, Lotfi M (2020) Application of best-worst method and additive ratio assessment in mineral prospectivity mapping: A case study of vein-type copper mineralization in the kuhsiah-e-urmak area, iran. Ore Geol Rev 117:103268

    Article  Google Scholar 

  • Rezaei, J. (2015).Best-worst multi-criteria decision-making method. Omega, 53:49–57. Saaty, T. (1980a). The analytic hierarchy process (ahp) for decision making. In Kobe, Japan.

  • Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resources allocation. McGraw, New York, p 281

    Google Scholar 

  • Tetteh G, Effisah-Otoo E (2017) Petrography and geochemistry of some granitoids associated with gold mineralisation at mpohor area in southeastern ashanti belt of the birimian, ghana. Ghana Min J 17(1):31–42

    Article  Google Scholar 

  • Trillas E, Eciolaza L (2015) Fuzzy Logic. Springer Int Pub 10:978–983

    Google Scholar 

  • Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66

    Google Scholar 

  • Xiong Y, Zuo R, Carranza EJM (2018) Mapping mineral prospectivity through big data analytics and a deep learning algorithm. Ore Geol Rev 102:811–817

    Article  Google Scholar 

  • Xtra-Gold Resource Corporation (2021). Kibi Gold Project.Exploration report.

  • Yousefi M, Carranza EJM (2016) Data-driven index overlay and boolean logic mineral prospectivity modeling in greenfields exploration. Nat Resour Res 25(1):3–18

    Article  Google Scholar 

  • Zhang N, Zhou K (2015) Mineral prospectivity mapping with weights of evidence and fuzzy logic methods. J Intell Fuzzy Syst 29(6):2639–2651

    Article  Google Scholar 

  • Zhang N, Zhou K, Li D (2018) Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the hatu region, xinjiang, china. Earth Sci Inf 11(4):553–566

    Article  Google Scholar 

  • Zhang Y-X, Lan H-X, Li L-P, Wu Y-M, Chen J-H, Tian N-M (2020) Optimizing the frequency ratio method for landslide susceptibility assessment: A case study of the caiyuan basin in the southeast mountainous area of china. J Mt Sci 17(2):340–357

    Article  Google Scholar 

  • Zuo R, Carranza EJM (2011) Support vector machine: a tool for mapping mineral prospectivity. Comput Geosci 37(12):1967–1975

    Article  Google Scholar 

Download references

Acknowledgements

Authors wish to thank Carnegie Corporation of New York through Building a New Generation in Africa (BANGA-Africa) for their support through their Write-shop organized in May, 2022. The authors also acknowledge Ghana Geological Survey Authority, GFZ German Research Centre for Geoscience and Geodita Resources Limited for making data available for use. Many thanks also goes to Mr. Isaac Ebiesa Kwofie (Trinity Presbyterian School, Adweso, Koforidua-Ghana) for his immense support during the early stages of the research.

Author information

Authors and Affiliations

  1. Department of Physics, School of Physical and Mathematical Sciences, College of Basic and Applied Sciences, University of Ghana, Accra, Ghana

    Eric Dominic Forson

  2. Geophysics SectionDepartment of PhysicsCollege of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Aboagye Menyeh

Authors
  1. Eric Dominic Forson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aboagye Menyeh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Both authors contributed to the conception and design of the study. Data acquisition, data processing, analysis and interpretation of results were also carried out by both authors. The first draft of the manuscript was written by Eric Dominic Forson. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Eric Dominic Forson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

Authors declare that there is no conflict of interest.

Additional information

Communicated by: H. Babaie

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Forson, E.D., Menyeh, A. Best worst method-based mineral prospectivity modeling over the Central part of the Southern Kibi-Winneba Belt of Ghana. Earth Sci Inform 16, 1657–1676 (2023). https://doi.org/10.1007/s12145-023-00999-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-023-00999-5

Keywords

Search

Navigation