Volume 12 Issue 1
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Thomas Busuyi Afeni, Victor Oluwatosin Akeju, Adeyemi Emman Aladejare. A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit[J]. Geoscience Frontiers, 2021, 12(1): 243-253. doi: 10.1016/j.gsf.2020.02.019
Citation: Thomas Busuyi Afeni, Victor Oluwatosin Akeju, Adeyemi Emman Aladejare. A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit[J]. Geoscience Frontiers, 2021, 12(1): 243-253. doi: 10.1016/j.gsf.2020.02.019

A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit

doi: 10.1016/j.gsf.2020.02.019
  • Received Date: 2019-06-27
  • Rev Recd Date: 2019-12-19
  • Mining projects especially relating to limestone deposits require an accurate knowledge of tonnage and grade, for both short and long-term planning. This is often difficult to establish as detailed exploration operations, which are required to get the accurate description of the deposit, are costly and time consuming. Geologists and mining engineers usually make use of geometric and geostatistical methods, for estimating the tonnage and grade of ore reserves. However, explicit assessments into the differences between these methods have not been reported in literature. To bridge this research gap, a comparative study is carried out to compare the qualitative reserve of Oyo-Iwa limestone deposit located in Nigeria, using geometric and geostatistical methods. The geometric method computes the reserve of the limestone deposit as 74,536,820 t (mean calcite, CaO grade = 52.15) and 99,674,793 t (mean calcite, CaO grade = 52.32), for the Northern and Southern zones of the deposit, respectively. On the other hand, the geostatistical method calculates the reserve as 81,626,729.65 t (mean calcite, CaO grade = 53.36) and -100,098,697.46 t (mean calcite, CaO grade = 52.96), for the two zones, respectively. The small relative difference in tonnage estimation between the two methods (i.e., 9.51% and 0.43%), proves that the geometric method is effective for tonnage estimation. In contrast, the relative difference in grade estimation between the two methods (i.e., 2.32% and -1.26%) is not negligible, and could be crucial in maintaining the profitability of the project. The geostatistical method is, therefore, more suitable, reliable and preferable for grade estimation, since it involves the use of spatial modelling and cross-validated interpolation. In addition, the geostatistical method is used to produce quality maps and three-dimensional (3-D) perspective view of the limestone deposit. The quality maps and 3-D view of the limestone deposit reveal the variability of the limestone grade within the deposit, and it is useful for operational management of the limestone raw materials. The qualitative mapping of the limestone deposit is key to effective production scheduling and accurate projection of raw materials for cement production.

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  • [1]
    Abdessattar, L., Dimitriy, N., Messaoud, M., 2019. Geostatistical modeling by the Ordinary Kriging in the estimation of mineral resources on the Kieselguhr mine, Algeria. IOP Conf. Ser. Earth Environ. Sci. 362 (1), 012051. https://doi.org/10.1088/1755-1315/362/1/012051.
    [2]
    Abzalov, M., 2016. Introduction to geostatistics. In:Abzalov, M. (Ed.), Applied Mining Geology. Modern Approaches in Solid Earth Sciences, vol 12. Springer, Cham, pp. 233-237.
    [3]
    Akeju, V.O., Afeni, B.T., 2015. Investigation of the spatial variability in Oyo-Iwa limestone deposit for quality control. J. Eng. Sci. Technol. 10 (8), 1065-1085.
    [4]
    Almedia, J., Rocha, M., Teixeira, A., 2004. Spatial characterization of limestone and marl quality in a quarry for cement manufacturing, Geostatistics Banff. In:Leuangthong, O., Deutsch, C.V. (Eds.), Quantitative Geology and Geostatistics, vol 14.Springer, Dordrecht, pp. 399-408.
    [5]
    Appleyard, G.R., 2001. An overview and outline, in mineral resource and ore reserve estimation. In:Edwards, A.C. (Ed.), The AusIMM Guide to Good Practice. The Australasian Institute of Mining and Metallurgy, Melbourne, pp. 3-12.
    [6]
    Asghari, O., Hezarkhani, A., 2006. Geostatistical modeling and reserve estimation of Choghart iron ore deposit through Ordinary Kriging method. In:Proceedings of the 5th International Scientific Conference-SGEM2005. Bulgaria, pp. 631-642.
    [7]
    ASTM, 2003. Standard test methods for chemical analysis of hydraulic cement. Annu.Book ASTM (Am. Soc. Test. Mater.) Stand. 4 (1), C114-03.
    [8]
    Chatterjee, S., Bhattacherjee, A., Samanta, B., Pal, S.K., 2006. Ore grade estimation of a limestone deposit in India using an artificial neural network. Appl. GIS 2 (1), pp. 2.1-2.20. https://doi.org/10.2104/ag060003.
    [9]
    Clark, I., 1986. The art of cross validation in geostatistical application. In:Ramani, R.V.(Ed.), Proc. 19th International Symposium on the Application of Computers and Operations Research in the Mineral Industry. Society of Mining Engineers, Inc., Littleton, Colorado, pp. 211-220.
    [10]
    Dada, S.S., Briqueu, L., Birck, J.L., 1998. Primordial crustal growth in northern Nigeria:preliminary Rb-Sr and Sm-Nd constraints from Kaduna migmatite-gneiss complex.J. Min. Geol. 34 (1), 1-6.
    [11]
    Daya, A.A., 2019. Nonlinear disjunctive kriging for the estimating and modeling of a vein copper deposit. Iran. J. Earth Sci. 11 (3), 226-236.
    [12]
    Dominy, S.C., Noppé, M.A., Annels, A.E., 2002. Errors and uncertainty in mineral resource and ore reserve estimation:the importance of getting it right. Explor. Min. Geol. 11(1-4), 77-98.
    [13]
    Dunlop, J.S.F., 1979. Geostatistical modelling of an Australian iron ore body, case studies in geostatistical ore reserve estimation. In:16th Applications of Computers and Operations Research in the Mineral Industry, vol 21. SME Publications, pp. 226-239.
    [14]
    Elium, E., Grammer, G.M., Pranter, M., 2017. Combining sequence stratigraphy with artificial neural networks to enhance regional correlation and determination of reservoir quality in the "Mississippian limestone" of the mid-continent, USA. AAPG Annual Convention and Exhibition, Houston, Texas. April 2-5, 2017.
    [15]
    Elueze, A.A., 2000. Compositional appraisal and petrotectonic significance of the Imelu banded ferruginous rock in the Ilesha schist belt, southwestern Nigeria. J. Min. Geol. 36 (1), 9-18.
    [16]
    Elueze, A.A., Jimoh, A.O., Aromolaran, O.K., 2015. Compositional characteristics and functional applications of Obajana marble deposit in the Precambrian basement complex of central Nigeria. IFE J. Sci. 17 (3), 591-603.
    [17]
    Erarslan, K., 2012. Computer aided ore body modelling and mine valuation. In:Ahmad, Dar (Ed.), Earth Sciences. InTech, UK, pp. 345-372.
    [18]
    Fytas, K., Chaoval, N., Lavigne, M., 1990. Gold deposit estimation using indicator kriging.Cim. Bull. 934, 77-83.
    [19]
    Gholampour, O., Hezarkhani, A., Maghsoudi, A., Mousavi, M., 2019. Delineation of alteration zones based on kriging, artificial neural networks, and concentration-volume fractal modelings in hypogene zone of Miduk porphyry copper deposit, SE Iran. J. Min. Environ. 10 (3), 575-595.
    [20]
    Gusman, M., Muchtar, B., Syah, N., Akbar, M.D., Deni, A.V., 2019. Estimations of limestone resources using three dimension block kriging method, a case study:limestone sediment at PT Semen Padang. IOP Conf. Ser. Earth Environ. Sci. 314 (1), 012069. IOP Publishing.
    [21]
    Hockey, R.D., Sachi, R.L., Graff, W.P.F., Muotoh, E.O.G., 1986. The Geology of Lokoja -Auchi Area. Ministry of Mines, Power and Steel, Federal Republic of Nigeria, p. 71.
    [22]
    Isaaks, E.H., Srivastava, R.M., 1989. Applied Geostatistics. Oxford University Press, New York.
    [23]
    Jacob, J., Prins, C., Oelofsen, A., 2014. Determination of sampling configuration for nearshore diamondiferous gravel occurrence using geostatistical methods. J. S. Afr. Inst.Min. Metall 114, 31-38.
    [24]
    Jafrasteh, B., Fathianpour, N., 2017. A hybrid simultaneous perturbation artificial bee colony and back-propagation algorithm for training a local linear radial basis neural network on ore grade estimation. Neurocomputing 235, 217-227.
    [25]
    Jafrasteh, B., Fathianpour, N., Suárez, A., 2018. Comparison of machine learning methods for copper ore grade estimation. Comput. Geosci. 22 (5), 1371-1388.
    [26]
    Jalloh, A.B., Kyuro, S., Jalloh, Y., Barrie, A.K., 2016. Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:a case study. Int. J. Min. Sci. Technol. 26 (4), 581-585.
    [27]
    Kapageridis, K.I., Denby, B., 1998. Neural Network Modelling of Ore Grade Spatial Variability:AIMS Research Unit. In:Niklasson, L., Bodén, M., Ziemke, T. (Eds.), ICANN 98. Perspectives in Neural Computing. Springer, London, pp. 209-214.
    [28]
    Kim, K.H., Lee, K., Lee, H.S., Rhee, C.W., Shin, H.D., 2018. Lithofacies modeling by multipoint statistics and economic evaluation by NPV volume for the early Cretaceous Wabiskaw Member in Athabasca oilsands area, Canada. Geosci. Front. 9(2), 441-451.
    [29]
    Krige, D.G., 1984. Geostatistics and the definition of uncertainty. Trans. Inst. Min. Metall. 93, A41-A47.
    [30]
    Leuangthong, O., Schnetzler, E., Deutsch, C.V., 2004. Geostatistical modeling of McMurray oil sands deposits. In:CIM Conference and Exhibition, 390. Edmonton, Canada, pp. 1-10.
    [31]
    Lilford, E., Jones, O., Chan, F., 2018. The Business of Mining:Mineral Project Valuation.CRC Press/Balkema.
    [32]
    Liu, C., Kubo, T., Lu, L., Koike, K., Zhu, W., 2019. Spatial simulation and characterization of three-dimensional fractures in Gejiu tin district, southwest China, using GEOFRAC.Nat. Resour. Res. 28 (1), 99-108.
    [33]
    Madani, N., Ortiz, J., 2017. Geostatistical simulation of cross-correlated variables:a case study through Cerro Matoso Nickel-Laterite deposit. In:26th International Symposium on Mine Planning and Equipment Selection. Nazarbayev University School of Mining and Geosciences.
    [34]
    Maghsoudi, M.F., van der Meijde, M., Hewson, R.D., van Ruitenbeek, F.J.A., Asadi Haroni, H., 2018. Data mining of remotely sensed datasets for ore grade estimation.In:29th Annual Conference of the Geological Remote Sensing Group 2018. London, United Kingdom.
    [35]
    Marcotte, D., Naraghi, K., Bellehumeur, C., Gloaguen, E., 2005. An application of multivariate simulation in the cement industry. Math. Geol. 37 (55), 493-512.
    [36]
    Martin, K.G., Totten, M.W., Raef, A., 2017. Characterization of a reservoir ooid shoal complex and Artificial Neural Networks application in lithofacies prediction:Mississippian St. Louis formation, Lakin fields, western Kansas. J. Petrol. Sci. Eng. 150, 1-12.
    [37]
    Matheron, G., 1962. Traité de géostatistique appliquée. Mémoires du Bureau de Recherches Géologiques et Miniéres 14, 55 (in French).
    [38]
    Matheron, G., 1971. The Theory of Regionalized Variables and its Applications, vol 5.National Supérieure des Mines, Paris, p. 211.
    [39]
    Mery, N., Emery, X., Cáceres, A., Ribeiro, D., Cunha, E., 2017. Geostatistical modeling of the geological uncertainty in an iron ore deposit. Ore Geol. Rev. 88, 336-351.
    [40]
    Moore, C., 1982. Chemical control of Portland cement clinker. Ceram. Bull. 61 (4), 511-515.
    [41]
    Okunlola, O.A., 2001. Geological and Compositional Investigation of Precambrian Marble Bodies and Associated Rocks in Burum and Jakura Area, Nigeria. Unpublished Ph.D.thesis, University of Ibadan, p. 256.
    [42]
    Olea, R.A., 2018. A practical primer on geostatistics. U.S. Geological Survey Open-File Report 2009-1103. https://pubs.usgs.gov/of/2009/1103/ofr20091103.pdf.
    [43]
    Onur, A.H., Konak, G., Karakuo, D., 2008. Limestone quarry quality optimization for a cement factory in Turkey. J. S. Afr. Inst. Min. Metall 108, 751-757.
    [44]
    Osterholt, V., Dimitrakopoulos, R., 2018. Simulation of orebody geology with multiplepoint geostatistics-application at Yandi channel iron ore deposit, WA, and implications for resource uncertainty. In:Dimitrakopoulos, R. (Ed.), Advances in Applied Strategic Mine Planning. Springer, Cham, pp. 335-352.
    [45]
    Ovinnikov, A.E., Kobzev, A.G., Pereverzeva, S.A., Berdichevskaya, T.A., Vaskova, N.A., 2018. Use of methods of geostatistics and numerical modeling in the study of fractured limestones. In:Conference Proceedings of European. Association of Geoscientists & Engineers, Saint Petersburg, Russia, pp. 1-6.
    [46]
    Patel, A.K., Chatterjee, S., 2016. Computer vision-based limestone rock-type classification using probabilistic neural network. Geosci. Front. 7 (1), 53-60.
    [47]
    Rahaman, M.A., 1988. Recent advances in the study of the basement complex of Nigeria.In:Oluyide, P.O., Mbonu, W.C., Ogezi, A.E., Egbuniwe, I.G., Ajibade, A.C., Umeji, A.C. (Eds.), Precambrian Geology of Nigeria. Geological Survey of Nigeria Special Publication, Kaduna South, pp. 1-23.
    [48]
    Salman, A., Ibrahim, K.M., Saffarini, G., Al-Qinna, M., 2009. Geostatistical calculation for clay reserve in Azraq Basin in Jordan. J. Geogr. Reg. Plann. 2 (5), 144-153.
    [49]
    Shurygin, D.N., Vlasenko, S.V., Shutkova, V.V., 2019. Estimation of the error in the calculation of mineral reserves taking into account the heterogeneity of the geological space. IOP Conf. Ser. Earth Environ. Sci. 272 (2), 022139.
    [50]
    Silva, D., Almeida, J., 2017. Geostatistical methodology to characterize volcanogenic massive and stockwork ore deposits. Minerals 7 (12), 238.
    [51]
    Świtoń, J.M., 2015. Geostatistical analysis of variability of silica dioxide content within limestone deposit. Min. Sci. 22, 181-193.
    [52]
    Truong, X.L., Truong, X.Q., Nguyen, T.A., Raghavan, V., Nguyen, C.C., 2019.Development of HUMGEOSTAT:a new geological tool for geostatistical analysis of mineral deposit:a case study at sin quyen mine (northern vietnam). J. Geol. Soc.India 93 (5), 574-582.
    [53]
    Van Breemen, O., Pidgeon, R.T., Bowden, P., 1977. Age and isotopic studies of some PanAfrican granites from North-central Nigeria. Precambrian Res. 4 (4), 307-319.
    [54]
    Vizi, L., 2008. A case study in uniform conditioning of local recoverable reserves estimation for jelšava magnesite deposit-level 220. GeoSci. Eng. 54 (1), 41-53.
    [55]
    Wang, W., 2019. Three-dimensional Geological Modelling of the Lithofacies of Caddo Limestone in Stephens County, North-Central Texas. Ph.D thesis. University of Texas, Austin.
    [56]
    Wang, G., Huang, L., 2012.3D geological modeling for mineral resource assessment of the Tongshan Cu deposit, Heilongjiang Province, China. Geosci. Front. 3 (4), 483-491.
    [57]
    Wang, Y., Akeju, O.V., Zhao, T., 2017. Interpolation of spatially varying but sparsely measured geo-data:a comparative study. Eng. Geol. 231, 200-217.
    [58]
    Webster, R., Oliver, M.A., 2007. Geostatistics for Environmental Scientists. John Wiley and Sons, West Sussex, England.
    [59]
    Wellmer, F.-W., 1998. Statistical Evaluations in Exploration for Mineral Deposits.Springer, New York.
    [60]
    Wellmer, F.W., Dalheimer, M., Wagner, M., 2007. Economic Evaluations in Exploration.Springer Science & Business Media.
    [61]
    Xu, S., Sirieix, C., Marache, A., Riss, J., Malaurent, P., 2016.3D geostatistical modeling of Lascaux hill from ERT data. Eng. Geol. 213, 169-178.
    [62]
    Yasrebi, A.B., Hezarkhani, A., 2019. Resources classification using fractal modelling in Eastern Kahang Cu-Mo porphyry deposit, Central Iran. Iran. J. Earth Sci. 11 (1), 56-67.
    [63]
    Yünsel, T.Y., 2012. A practical application of geostatistical methods to quality and mineral reserve modelling of cement raw materials. J. S. Afr. Inst. Min. Metall 112, 239-249.
    [64]
    Yünsel, T.Y., 2018. Simulation of cement raw material deposits using plurigaussian technique. Open Geosci. 10 (1), 889-901.
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