Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Open Geosciences

formerly Central European Journal of Geosciences

Editor-in-Chief: Jankowski, Piotr

IMPACT FACTOR 2018: 0.788
5-year IMPACT FACTOR: 0.899

CiteScore 2018: 1.02

SCImago Journal Rank (SJR) 2018: 0.295
Source Normalized Impact per Paper (SNIP) 2018: 0.612

Open Access
See all formats and pricing
More options …

Regional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS

Suhaimizi Yusoff
  • Faculty of Engineering, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), University Putra Malaysia, 43400, Serdang, Malaysia; Minerals and Geoscience Department (JMG), 19-22th Floor, Bangunan Tabung Haji, Jalan Tun Razak, 50658, Kuala Lumpur, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Biswajeet Pradhan
  • Faculty of Engineering, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), University Putra Malaysia, 43400, Serdang, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Mohamad Abd Manap
  • Minerals and Geoscience Department (JMG), 19-22th Floor, Bangunan Tabung Haji, Jalan Tun Razak, 50658, Kuala Lumpur, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Helmi Zulhaidi Mohd Shafri
  • Faculty of Engineering, Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), University Putra Malaysia, 43400, Serdang, Malaysia
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2015-06-11 | DOI: https://doi.org/10.1515/geo-2015-0012


The aim of this study is to test and compare two probabilistic based models (frequency ratio and weightsof- evidence) with regard to regional gold potential mapping at Kelantan, Malaysia. Until now these models have not been used for the purpose of mapping gold potential areas in Malaysia. This study analyzed the spatial relationship between gold deposits and geological factors such as lithology, faults, geochemical and geophysical data in geographical information system (GIS) software. About eight (8) gold deposits and five (5) related factors are identified and quantified for their spatial relationships. Then, all factors were combined to generate a predictive gold potential map. The predictive maps were then validated by comparing them with known gold deposits using receiver operating characteristics (ROC) and “area under the curve” (AUC) graphs. The results of validation showed accuracies of 80% for the frequency ratio and 74% for the weightsof- evidence model, respectively. The results demonstrated the usefulness of frequency ratio and weights-of-evidence modeling techniques in mineral exploration work to discover unknown gold deposits in Kelantan, Malaysia.

Keywords: Gold potential mapping; Remote sensing; Frequency ratio; Weights-of-evidence; GIS; Kelantan; Malaysia


  • [1] Carranza E.J.M., Geocomputation of mineral exploration targets. Comput. Geosci., 2011, 37, 1907-1916. CrossrefGoogle Scholar

  • [2] Knox-Robinson C.M., Vectorial fuzzy logic: a novel technique for enhanced mineral prospectivity mapping, with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane, Western Australia. Aust. J. of Earth Sci., 2000, 47, 929-941. Google Scholar

  • [3] Partington G.A., Exploration targeting using GIS: more than a digital light table. Geo Computing Conference, Brisbane, Australia, 2010. Google Scholar

  • [4] Chung C.F., Agterberg F.P., Regression models for estimating mineral resources from geological map data. Math. Geol., 1980, 12, 473-488. CrossrefGoogle Scholar

  • [5] Bonham-Carter G.F., Agterberg F.P.,Wright D.F., Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetic Engineering and Remote Sensing, 1988, 54, 1585-1592. Google Scholar

  • [6] Harris J.R., Data integration for gold exploration in eastern Nova Scotia using a GIS. In: Remote Sensing for Exploration Geology, Proc. Calgary, Alberta, 1989, 233–249. Google Scholar

  • [7] Moon W.M., Chung C.F., An P., Representation and Integration of geological, geophysical and remote sensing data. Geoinformatics, 1991, 2, 177-182. Google Scholar

  • [8] Agterberg F.P., Bonham-Carter G.F., Cheng Q., Wright D.F., Weights of evidence modeling and weighted logistic regression for mineral potentialmapping. In: J.C. Davis&U.C. Herzfeld (eds.) Computers in Geology-25 Years of Progress. Oxford University Press, International Association of Mathematical Geology, Studies in Mathematical Geology, 1993, 5, 13-32. Google Scholar

  • [9] Bonham-Carter G.F., Geographic Information Systems for Geoscientists: Modeling with GIS. New York, Pergamon/Elsevier, 1994. Google Scholar

  • [10] Rencz A.N., Harris J.R.,Watson G.P.,Murphy B., Data integration for mineral exploration in the Antigonish Highlands, Nova Scotia. Can. Jour. Remote Sensing, 1994, 20, 258-267. Google Scholar

  • [11] Wright D.F., Bonham-Carter G.F., VHMS favourability mapping with GIS-based integration models, Chisel Lake-Anderson Lake Area. In: G.F. Bonham-Carter, A.G. Galley, G .E.M. Hall (eds.) EXTECHI: A Multidisciplinary Approach to Massive Sulphide Research in the Rusty Lake-Snow Lake Greenstone Belts,Manitoba. Geol. Survey Canada Bull., 1996, 426, 339-376. Google Scholar

  • [12] Harris J.R.,Wilkinson L., Grunsky E.C., Effective use and interpretation of lithogeochemical data in regional exploration programs. Ore Geol. Rev., 2000, 16, 107-143. CrossrefGoogle Scholar

  • [13] Raines G.L., Evaluation of weights of evidence to predict epithermal gold deposits in the GreatBasin of the western United States: Nat. Resour. Res., 1999, 8, 257-276. CrossrefGoogle Scholar

  • [14] Lee S., Probabilistic integration of integrated gold mineral potential maps using GIS. Proceeding of 7th Symposium On Stepping Stones To The Future: Strategies, Architectures, Concepts and Technologies, Daejon, Korea, 2009. Google Scholar

  • [15] Lee S., Integration of mineral potential maps from various geospatial models. Proceedings of the 2nd International Conference onComputing for Geospatial Research&Applications, 2011. Google Scholar

  • [16] Carranza E.J.M., Hale M., Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines. Nat. Resour. Res., 2000, 9, 237-253. CrossrefGoogle Scholar

  • [17] An P., Moon W.M., Rencz A.N., Application of fuzzy set theory to integrated mineral exploration, Canadian Journal of Exploration Geophysics, 1991, 27, 1-11. Google Scholar

  • [18] Eddy B.G., Bonham-Carter G.F., Jefferson C.W., Mineral resource assessment of the Parry Islands, high Arctic, Canada: A GIS-base fuzzy logic model, Proceedings of Canadian Conference on GIS, Ottawa, 1995. Google Scholar

  • [19] An P., MoonW.M., An evidential reasoning structure for integrating geophysical, geological and remote sensing data, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Tokyo, 1993, 1359-1361. Google Scholar

  • [20] Carranza E.J.M., From Predictive Mapping of Mineral Prospectivity to Quantitative Estimation of Number of Undiscovered Prospects. Resour. Geol., 2011, 61, 30-51. CrossrefGoogle Scholar

  • [21] Carranza E.J.M., Hale M., Evidential belief functions for datadriven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol. Rev., 2002, 22, 117-132. CrossrefGoogle Scholar

  • [22] Carranza E.J.M.,Woldai T., Chikambwe E.M., Application of datadriven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi District, Zambia: Nat. Resour. Res., 2005, 14, 47-63. CrossrefGoogle Scholar

  • [23] Carranza E.J.M., Sadeghi M., Predictive mapping of prospectivity and quantitative estimation of undiscovered VMS deposits in Skellefte district (Sweden). Ore Geol. Rev., 2010, 38, 219-241. CrossrefGoogle Scholar

  • [24] Carranza E.J.M., Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Handbook of Exploration and Environmental Geochemistry; Vol 11. Elsevier, 2009. Google Scholar

  • [25] Oh H.J., Lee S., Regional Probabilistic and Statistical Mineral Potential, Mapping of Gold-Silver Deposits Using GIS in the Gangreung Area, Korea. Resour. Geol., 2008, 58, 171-187. CrossrefGoogle Scholar

  • [26] Ernowo, Oktaviani P., Regional probabilistic of gold-silver potential mapping using likelihood ratio models in Flores Island. Proceeding of the 39thIAGI Annual Convention and Exhibition, Lombok, 2010. Google Scholar

  • [27] Bonham-Carter G.F., Agterberg F.P., Wright D.F., Weights of evidence modeling: A new approach to mapping mineral potential, In: Statistical Applications in the Earth Sciences, Agterberg, F.P., & Bonham-Carter, G.F., (Ed.): 171-183, Geological Survey of Canada 98, Canadian Government Publishing Centre, 1989. Google Scholar

  • [28] Carranza E.J.M., Weights of evidence modeling of mineral potential: a case study using small number of prospects, Abra, Philippines. Nat. Resour. Res., 2004, 13, 173-187. CrossrefGoogle Scholar

  • [29] Austin J.R., Blenkinsop T.G., Local to regional scale structural controls on mineralisation and the importance of a major lineament in the easternMount Isa In lier, Australia: Review and analysis with autocorrelation and weights of evidence. Ore Geol. Rev., 2009, 35, 298-316. CrossrefGoogle Scholar

  • [30] Arianne F., Craig J.R.H., Mineral potentialmapping in frontier regions: A Mongolian case study. Ore Geol. Rev., 2013, 51, 15-26. CrossrefGoogle Scholar

  • [31] Carranza E.J.M., Hale M., Faassen C., Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping. Ore Geol. Rev., 2008, 33, 536-558. CrossrefGoogle Scholar

  • [32] Singer D.A., Kouda R., Application of a feed forward neural network in the search for Kuroko deposits in the Hokuroku District, Japan. Math. Geol., 1996, 28, 1017-1023. CrossrefGoogle Scholar

  • [33] Oh H.J., Lee S., Application of Artificial Neural Network for Gold- Silver Deposits Potential Mapping: A Case Study of Korea. Nat. Resour. Res., 2011, 19, 103-124. CrossrefGoogle Scholar

  • [34] Surip N., Hamzah A.H., Zakaria M.R., Napiah A., Talib J.A., Mapping of gold in densely vegetated area using remote sensing and GIS techniques in Pahang,Malaysia. Proceeding of Asian Conference on Remote Sensing (ACRS), Kuala Lumpur, 2007. Google Scholar

  • [35] Yin E.H., Provisional draft report on the geology and mineral resources of the Gua Musang area, Sheet 45, South Kelantan. Geological Survey of Malaysia, 1965. Google Scholar

  • [36] Hutchison C.S., Ophiolite in Southeast Asia. Geological Society of America Bulletin, 1975, 86, 797-806. Google Scholar

  • [37] Chu L.H., Preliminary report on geochemistry, Anomaly 3503, Kelantan. Geological Survey Malaysia Report (unpublished), 1980 Google Scholar

  • [38] Chu L.H., Exploration in the Sok Base Metal Prospect, Kelantan. Geological Survey Malaysia Report (unpublished), 1983. Google Scholar

  • [39] Chu L.H., Muntanion H., Sidek A., Chand F., Troup A., Regional geochemistry of South Kelantan. Geological Survey Malaysia, 1983. Google Scholar

  • [40] Hock T.L., Kow L.A., Yee F.K., Gold mineralization and prospect in Kelantan. Geological Survey Malaysia, 1987. Google Scholar

  • [41] Geological Survey Malaysia, Airbone spectrometric and magnetic survey, Central Belt Project, 1982. Google Scholar

  • [42] Heng G.S., Hoe T.G., Hassan W.F.W., Gold mineralization and zonation in the State of Kelantan. Geological Society of Malaysia Bulletin, 2006, 52, 129-135. Google Scholar

  • [43] Hassan W.F.W., Khersonese emas geologi emas Semenanjung Malaysia. Universiti Kebangsaan Malaysia., 2007, 30-36. Google Scholar

  • [44] Carranza E.J.M., Objective selection of suitable unit cell size in data-driven modeling of mineral prospectivity. Comput. Geosci., 2009, 35, 2032-2046. CrossrefGoogle Scholar

  • [45] Pradhan B., Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J. Indian Soc. Remote Sens., 2010, 38, 301-320. Google Scholar

  • [46] Lee S., Pradhan B., Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J. Earth Syst. Sci., 2006, 6, 1-12 Google Scholar

  • [47] Lee S, Pradhan B., Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression model. Landslides, 2007, 4, 33-41. CrossrefGoogle Scholar

  • [48] Pradhan B., Lee S., Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ. Modell Softw., 2010, 25, 747-759. CrossrefGoogle Scholar

  • [49] Pradhan B., Lee S., Buchroithner M.F., Remote sensing and GIS based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogramm Fernerkun, 2010, 1, 17-32. Google Scholar

  • [50] Pradhan B.,Mansor S., Pirasteh S., Buchroithner M., Landslidehazard and risk analyses at a landslide prone catchment area usingstatistical based geospatial model. Int. J. Remote Sens., 2011, 32, 4075-4087. CrossrefGoogle Scholar

  • [51] Pradhan B., Chaudhari A., Adinarayana J., Buchroithner M.F., Soil erosion assessment and its correlationwith landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environ. Monit. Assess, 2012, 184, 715-727. CrossrefGoogle Scholar

  • [52] Manap M.A., Nampak H., Pradhan B., Lee S., Sulaiman W.N.A., Ramli M.F., (2012) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J. Geosci., 2012. Google Scholar

  • [53] Franca-Rocha W., Bonham-Carter G., Misi A., GIS modeling for mineral potential mapping of carbonate-hosted pb-zn deposits. Revista Brasileira de Geociências, 2003, 33, 191-196. Google Scholar

  • [54] Chung C.F., Fabbri A.G., Validation of spatial prediction models for landslide hazard mapping. Nat. Hazards, 2003, 30, 451-472. CrossrefGoogle Scholar

  • [55] Pradhan B., A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci., 2013, 51, 350-365. CrossrefGoogle Scholar

  • [56] Pradhan B., Lee S., Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ. Earth Sci., 2010, 60, 1037-1054. CrossrefGoogle Scholar

  • [57] Lee S., Dan N.T., Probabilistic landslide susceptibility mapping on the Lai Chau province of Vietnam: focus on the relationship between tectonic fractures and landslides. Environ. Geol., 2005, 48, 778-787. CrossrefGoogle Scholar

About the article

Received: 2014-04-03

Accepted: 2014-06-16

Published Online: 2015-06-11

Citation Information: Open Geosciences, Volume 7, Issue 1, ISSN (Online) 2391-5447, DOI: https://doi.org/10.1515/geo-2015-0012.

Export Citation

©2015 Suhaimizi Yusoff et al.. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Mathew Gregory Tagwai, Onimisi A. Jimoh, Kamar Shah Ariffin, and Mohd Firdaus Abdul Razak
Journal of Spatial Science, 2019, Page 1
Mahyat Shafapour Tehrany, Lalit Kumar, Mustafa Neamah Jebur, and Farzin Shabani
Geomatics, Natural Hazards and Risk, 2019, Volume 10, Number 1, Page 79
Jabir Haruna Abdulkareem, Biswajeet Pradhan, Wan Nor Azmin Sulaiman, and Nor Rohaizah Jamil
Environment Systems and Decisions, 2018

Comments (0)

Please log in or register to comment.
Log in