Geostatistical Modeling using Ordinary Kriging for Estimating Nickel Resources in Sulawesi Indonesia
DOI:
https://doi.org/10.47352/jmans.2774-3047.252Keywords:
nickel, geostatistic, variogram, ordinary kriging, resource estimationAbstract
Geostatistic is a statistical tool used in the mining sector to estimate and classify mining resources at a specific location. The purpose of this study was to evaluate the distribution or model of nickel resources, as well as estimate and classify nickel resources using a geostatistical approach. This study used data from exploration drilling at one of the nickel mining concessions in Sulawesi, Indonesia. The data set included 464 drill holes with an average distance of 50–100 m. The initial stage in this study was to develop a geological model, followed by descriptive statistical analysis, with the results of the variance coefficient ranging from 0.5 to 1.5 and normal distribution, indicating that the ordinary kriging method can be used and is considered adequate to produce sound and consistent findings. The values obtained from the variogram analysis on the spherical model will be used as parameters in the ordinary and efficiency kriging processes. Based on the estimation and classification of nickel resources using ordinary and efficiency kriging, the total measured, indicated, and inferred nickel resources are 39, 1.25, and 3 million tons, respectively, with an average Ni content of 1.16%.
References
[1] A. van der Ent, A. J. M. Baker, M. M. J. van Balgooy, and A. Tjoa. (2013). "Ultramafic nickel laterites in Indonesia (Sulawesi, Halmahera): Mining, nickel hyperaccumulators and opportunities for phytomining". Journal of Geochemical Exploration. 128 : 72-79. 10.1016/j.gexplo.2013.01.009.
DOI: https://doi.org/10.1016/j.gexplo.2013.01.009[2] L. Lu, Y. Lei, Y. Yang, H. Zheng, W. Wang, Y. Meng, C. Meng, and L. Zha. (2023). "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022". Resources Policy. 82. 10.1016/j.resourpol.2023.103563.
DOI: https://doi.org/10.1016/j.resourpol.2023.103563[3] S. A. Al-Khirbash. (2020). "Mineralogical characterization of low-grade nickel laterites from the North Oman Mountains: Using mineral liberation analyses – scanning electron microscopy-based automated quantitative mineralogy". Ore Geology Reviews. 120. 10.1016/j.oregeorev.2020.103429.
DOI: https://doi.org/10.1016/j.oregeorev.2020.103429[4] F. Usman, G. M. Tinungki, and E. T. Herdiani. (2021). "Efficiency of Ni Content in Laterite Nickel Deposits through The Least Square Method Approach on Semivariogram". Journal of Physics: Conference Series. 2123 (1). 10.1088/1742-6596/2123/1/012015.
DOI: https://doi.org/10.1088/1742-6596/2123/1/012015[5] X. Yan, T. Yang, Y. Xu, L. Tosi, E. Stouthamer, H. Andreas, J. Lin, and X. Huang. (2019). "Impact, Mechanism, Monitoring of Land Subsidence in Coastal Cities (Annual Work of IGCP 663)". Acta Geologica Sinica - English Edition. 93 (S3): 158-159. 10.1111/1755-6724.14276.
DOI: https://doi.org/10.1111/1755-6724.14276[6] S. S. P, Y. Sibaroni, and M. Nur Heriawan. (2016). "Spatial Analysis 3d Geology Nickel Using Ordinary Kriging Method". Jurnal Teknologi. 78 (5). 10.11113/jt.v78.8340.
DOI: https://doi.org/10.11113/jt.v78.8340[7] M. Mälicke, A. Guadagnini, and E. Zehe. (2023). "SciKit-GStat Uncertainty: A software extension to cope with uncertain geostatistical estimates". Spatial Statistics. 54. 10.1016/j.spasta.2023.100737.
DOI: https://doi.org/10.1016/j.spasta.2023.100737[8] M. N. Heriawan and K. Koike. (2008). "Identifying spatial heterogeneity of coal resource quality in a multilayer coal deposit by multivariate geostatistics". International Journal of Coal Geology. 73 (3-4): 307-330. 10.1016/j.coal.2007.07.005.
DOI: https://doi.org/10.1016/j.coal.2007.07.005[9] W. S. Bargawa. (2022). "The Performance of Estimation Techniques for Nickel Laterite Resource Modeling". Jurnal Teknologi. 84 (4): 1-8. 10.11113/jurnalteknologi.v84.17560.
DOI: https://doi.org/10.11113/jurnalteknologi.v84.17560[10] T. Wendler and S. Gröttrup. (2021). In: "Data Mining with SPSS Modeler, ch. Chapter 3". 191-305. 10.1007/978-3-030-54338-9_3.
DOI: https://doi.org/10.1007/978-3-030-54338-9_3[11] R. G. Arancibia, P. Llop, and M. Lovatto. (2023). "Nonparametric prediction for univariate spatial data: Methods and applications". Papers in Regional Science. 102 (3): 635-673. 10.1111/pirs.12735.
DOI: https://doi.org/10.1111/pirs.12735[12] P. Kumar, B. Rao, A. Burman, S. Kumar, and P. Samui. (2023). "Spatial variation of permeability and consolidation behaviors of soil using ordinary kriging method". Groundwater for Sustainable Development. 20. 10.1016/j.gsd.2022.100856.
DOI: https://doi.org/10.1016/j.gsd.2022.100856[13] C. Y. S. Ip, H. Rahardjo, and A. Satyanaga. (2019). "Spatial variations of air-entry value for residual soils in Singapore". Catena. 174 : 259-268. 10.1016/j.catena.2018.11.012.
DOI: https://doi.org/10.1016/j.catena.2018.11.012[14] N. A. Amri, W. S. Bargawa, and T. A. Cahyadi. (2021). "Multi Criterion Priority On Kriging Of Gold Resources Prediction". Journal Techno. 7 (2).
[15] R. Faidatulaila, I. Marwanza, and T. T. Purwiyono. (2023). "Variography analysis on the assessment of coal deposit quality using the ordinary kriging method". AIP Conference Proceedings. 10.1063/5.0126896.
DOI: https://doi.org/10.1063/5.0126896[16] B. Anggara, I. Marwanza, M. Ahmad Azizi, W. Dahani, and Subandrio. (2021). "Fitting the variogram model of nickel laterite using root means square error in Morowali, Central Sulawesi". IOP Conference Series: Earth and Environmental Science. 882 (1). 10.1088/1755-1315/882/1/012042.
DOI: https://doi.org/10.1088/1755-1315/882/1/012042[17] M. Abzalov. (2016). In: "Applied Mining Geology, (Modern Approaches in Solid Earth Sciences, ch. Chapter 17". 233-237. 10.1007/978-3-319-39264-6_17.
DOI: https://doi.org/10.1007/978-3-319-39264-6_17[18] C. Zhou, L. Wang, and Y. Chen. (2023). "Active learning-based structural reliability evaluation Kriging model and sequential importance sampling". Structures. 56. 10.1016/j.istruc.2023.104956.
DOI: https://doi.org/10.1016/j.istruc.2023.104956[19] Y. Pang, Y. Wang, X. Lai, S. Zhang, P. Liang, and X. Song. (2023). "Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization". Computer Methods in Applied Mechanics and Engineering. 414. 10.1016/j.cma.2023.116194.
DOI: https://doi.org/10.1016/j.cma.2023.116194[20] D. Ali Akbar. (2012). "Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method". International Journal of Mining Science and Technology. 22 (4): 573-577. 10.1016/j.ijmst.2012.01.022.
DOI: https://doi.org/10.1016/j.ijmst.2012.01.022[21] D. G. Krige. (1997). In: "Geostatistics Wollongong’ 96, (Quantitative Geology and Geostatistics, ch. Chapter 14". 799-810. 10.1007/978-94-011-5726-1_14.
DOI: https://doi.org/10.1007/978-94-011-5726-1_14[22] C. Munyati and N. I. Sinthumule. (2021). "Comparative suitability of ordinary kriging and Inverse Distance Weighted interpolation for indicating intactness gradients on threatened savannah woodland and forest stands". Environmental and Sustainability Indicators. 12. 10.1016/j.indic.2021.100151.
DOI: https://doi.org/10.1016/j.indic.2021.100151[23] W. A. K. Conoras and A. A. Lamburu. (2020). "Modeling and Resource Classification Lateritic Nickel Deposits on a Heterogeneous Block in The Haul-Sagu Area using Estimation and Simulation Geostatistical Method". Journal of Physics: Conference Series. 1569 (4). 10.1088/1742-6596/1569/4/042079.
DOI: https://doi.org/10.1088/1742-6596/1569/4/042079[24] S. Al-Ali and S. Al-Khafaji. (2023). "Spatial distribution and reserve estimation of sand and gravel deposits using geostatistical methods in west Basrah, southern Iraq". Kuwait Journal of Science. 50 (2): 127-137. 10.1016/j.kjs.2023.02.017.
DOI: https://doi.org/10.1016/j.kjs.2023.02.017[25] M. L. C. M. Henckens, F. H. B. Biermann, and P. P. J. Driessen. (2019). "Mineral resources governance: A call for the establishment of an International Competence Center on Mineral Resources Management". Resources, Conservation and Recycling. 141 : 255-263. 10.1016/j.resconrec.2018.10.033.
DOI: https://doi.org/10.1016/j.resconrec.2018.10.033[26] C. Tang, M. Irfan, A. Razzaq, and V. Dagar. (2022). "Natural resources and financial development: Role of business regulations in testing the resource-curse hypothesis in ASEAN countries". Resources Policy. 76. 10.1016/j.resourpol.2022.102612.
DOI: https://doi.org/10.1016/j.resourpol.2022.102612[27] F. Isatelle and J. Rivoirard. (2019). "Mineral Resources classification of a nickel laterite deposit: Comparison between conditional simulations and specific areas". Journal of the Southern African Institute of Mining and Metallurgy. 119 (10). 10.17159/2411-9717/660/2019.
DOI: https://doi.org/10.17159/2411-9717/660/2019[28] P. C. F. Crowson. (2011). "Mineral reserves and future minerals availability". Mineral Economics. 24 (1): 1-6. 10.1007/s13563-011-0002-9.
DOI: https://doi.org/10.1007/s13563-011-0002-9[29] O. Zerzour, L. Gadri, R. Hadji, F. Mebrouk, and Y. Hamed. (2021). "Geostatistics-Based Method for Irregular Mineral Resource Estimation, in Ouenza Iron Mine, Northeastern Algeria". Geotechnical and Geological Engineering. 39 (5): 3337-3346. 10.1007/s10706-021-01695-1.
DOI: https://doi.org/10.1007/s10706-021-01695-1[30] S. M. Jowitt and B. A. McNulty. (2021). "Geology and Mining: Mineral Resources and Reserves: Their Estimation, Use, and Abuse". SEG Discovery. 125 : 27-36. 10.5382/Geo-and-Mining-11.
DOI: https://doi.org/10.5382/Geo-and-Mining-11[31] M. M. Zaki, S. Chen, J. Zhang, F. Feng, A. A. Khoreshok, M. A. Mahdy, and K. M. Salim. (2022). "A Novel Approach for Resource Estimation of Highly Skewed Gold Using Machine Learning Algorithms". Minerals. 12 (7). 10.3390/min12070900.
DOI: https://doi.org/10.3390/min12070900[32] M. Uyan and A. E. Dursun. (2021). "Determination and modeling of lignite reserve using geostatistical analysis and GIS". Arabian Journal of Geosciences. 14 (4). 10.1007/s12517-021-06633-2.
DOI: https://doi.org/10.1007/s12517-021-06633-2[33] A. A. Daya and H. Bejari. (2014). "A comparative study between simple kriging and ordinary kriging for estimating and modeling the Cu concentration in Chehlkureh deposit, SE Iran". Arabian Journal of Geosciences. 8 (8): 6003-6020. 10.1007/s12517-014-1618-1.
DOI: https://doi.org/10.1007/s12517-014-1618-1[34] M. J. Mihalasky. (2016). "Mineral Resource Estimation (M.E. Rossi and C.V. Deutsch)". Economic Geology. 111 (1): 272-274. 10.2113/econgeo.111.1.272.
DOI: https://doi.org/10.2113/econgeo.111.1.272[35] A. Daya. (2015). "Ordinary kriging for the estimation of vein type copper deposit: A case study of the Chelkureh, Iran". Journal of Mining and Metallurgy A: Mining. 51 (1): 1-14. 10.5937/jmma1501001d.
DOI: https://doi.org/10.5937/JMMA1501001D[36] T. B. Afeni, V. O. Akeju, and A. E. Aladejare. (2021). "A comparative study of geometric and geostatistical methods for qualitative reserve estimation of limestone deposit". Geoscience Frontiers. 12 (1): 243-253. 10.1016/j.gsf.2020.02.019.
DOI: https://doi.org/10.1016/j.gsf.2020.02.019[37] Y. Sun, J. Zuo, M. Karakus, L. Liu, H. Zhou, and M. Yu. (2021). "A New Theoretical Method to Predict Strata Movement and Surface Subsidence due to Inclined Coal Seam Mining". Rock Mechanics and Rock Engineering. 54 (6): 2723-2740. 10.1007/s00603-021-02424-z.
DOI: https://doi.org/10.1007/s00603-021-02424-z[38] M. Taghvaeenezhad, M. Shayestehfar, P. Moarefvand, and A. Rezaei. (2020). "Quantifying the criteria for classification of mineral resources and reserves through the estimation of block model uncertainty using geostatistical methods: a case study of Khoshoumi Uranium deposit in Yazd, Iran". Geosystem Engineering. 23 (4): 216-225. 10.1080/12269328.2020.1748524.
DOI: https://doi.org/10.1080/12269328.2020.1748524
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