Journal of Geosciences and Geomatics
ISSN (Print): 2373-6690 ISSN (Online): 2373-6704 Website: Editor-in-chief: Maria TSAKIRI
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Journal of Geosciences and Geomatics. 2015, 3(5), 122-127
DOI: 10.12691/jgg-3-5-2
Open AccessArticle

Competitiveness of Inverse Distance Weighting Method for the Evaluation of Gold Resources in Fluvial Sedimentary Deposits: A Case Study

Al-Hassan Sulemana1, and Adjei David2

1University of Mines and Technology, Tarkwa, Ghana

2Goldfields Ghana Limited, Tarkwa, Ghana

Pub. Date: October 22, 2015

Cite this paper:
Al-Hassan Sulemana and Adjei David. Competitiveness of Inverse Distance Weighting Method for the Evaluation of Gold Resources in Fluvial Sedimentary Deposits: A Case Study. Journal of Geosciences and Geomatics. 2015; 3(5):122-127. doi: 10.12691/jgg-3-5-2


Gold mineralisation at Pepe occurs in a sedimentary sequence known as the Banket Series formation. Due to cut-back, further exploration has been done to obtain credible resource estimates for pragmatic mine planning and design. There is therefore, the need for the application of an appropriate, accurate and cost effective estimation method. The selection of the method used for any particular deposit depends on several factors including ease–of-use, robustness, accuracy and precision. Although the mine employs Ordinary Kriging (OK), which has gained much recognition and proven to be a very good estimator, it is complex and time consuming. This calls for effective alternatives. Inverse Distance Weighting (IDW) is another reliable method of estimation as it is simple, accurate and fast, and has proven to be effective for some deposits. This study seeks to verify the propriety and applicability of IDW to the estimation of the orebody by comparing the estimates obtained from Inverse Distance Weighting (IDW) and Ordinary Kriging methods. Correlation analysis performed on ID2W and OK model grades indicated a near perfect correlation coefficient of 0.93; an indication that ID2W can be used as a good alternative to OK at Pepe.

sedimentary Ordinary Kriging correlation orebody resource regression

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[1]  Royle, A. G. (1978). “Bias and its effects in Ore-reserve estimation”, Trans. Inst. Min. Metall., (Sect. A: Min, Industry), 87, A8-A12.
[2]  Strogen, P. (1991). “The Sedimentology and Structure of the Tarkwaian, Western Region and its Relevance to Gold Exploration and Development”, in Proceedings of International Conference of the Geology with Special Emphasis on Gold, pp. 34-45.
[3]  Karpeta, W. P. (2001). The structural Evolution of the Tarkwaian basin: A Review of the Geology, Mining and Exploration of the Tarkwa Mine Area, Bastillion Limited, 40p.
[4]  Sestini, G. (1973). “Sedimentology of a Paleoplacer; The Gold Bearing of Tarkwaian of Ghana: Ores in sediments”, International Union of Geological Sciences, Series A, No. 3, pp. 269-278.
[5]  Kesse, G. O. (1985). The Mineral and Rock Resources of Ghana, A. A. Balkema Publishers, Rotterdam, pp. 89 - 200.
[6]  Bluman, A. G. (2004). Elementary Statistics,: A step by step Approach, McGraw Hill Publications, New York, Fifth Edition, 811pp.
[7]  Walpole, R. E. and Myers, R. H. (1993). Probability and Statistics for Engineers and Scientists, MacMillan Publishing Company, New York, pp. 34-45.
[8]  Dowd, P. A. (1992). “Geostatistical Ore Reserve Estimation - A case study on a Disseminated Nickel Deposit. Case Histories in Mineral Deposits Evaluation”, Geological Society Special Publication, London, (Annels, A. E. ed.), pp. 243-256.
[9]  Sinclair, A. J. and Blackwell, G. H. (2002). Applied Mineral Inventory Estimation, Cambridge, Kluwer Academic Publishers, pp. 330-337.