Email this article 
Hide
Show all
Geostatistics Application On Uranium Resources Classification: Case Study of Rabau Hulu Sector, Kalan, West Kalimantan
DOI: http://dx.doi.org/10.55981/eksplorium.2018.4960
Abstract
ABSTRACT
In resources estimation, geostatistics methods have been widely used with the benefit of additional attribute tools to classify resources category. However, inverse distance weighting (IDW) is the only method used previously for estimating the uranium resources in Indonesia. The IDW method provides no additional attribute that could be used to classify the resources category. The objective of research is to find the best practice on geostatistics application in uranium resource estimation adjusted with geological information and determination of acceptable geostatistics estimation attribute for resources categorization. Geostatistics analysis in Rabau Hulu Sector was started with correlation of the orebody between boreholes. The orebodies in Rabau Hulu Sectors are separated individual domain which further considered has the hard domain. The orebody-15 was selected for further geostatistics analysis due to its wide distribution and penetrated most by borehole. Stages in geostatistics analysis cover downhole composites, basic statistics analysis, outliers determination, variogram analysis, and calculation on the anisotropy ellipsoid. Geostatistics analysis shows the availability of the application for two resources estimation attributes, which are kriging efficiency and kriging variance. Based on technical judgment of the orebody continuity versus the borehole intensity, the kriging efficiency is considered compatible with geological information and could be used as parameter for determination of the resources category.
ABSTRAK
Pada estimasi sumber daya, metode geostatistik telah banyak digunakan dengan kelebihan adanya alat atribut tambahan untuk mengklasifikasikan kategori sumber daya. Namun demikian, pembobotan inverse distance (IDW) adalah satu-satunya metode yang sebelumnya digunakan untuk mengestimasi sumber daya uranium di Indonesia. Metode IDW tidak memberikan tambahan atribut yang dapat digunakan dalam mengklasifikasikan kategori sumber daya. Tujuan dari penelitian adalah mendapatkan praktek terbaik untuk aplikasi geostatistik pada estimasi sumber daya disesuaikan dengan informasi geologi dan penentuan atribut geostatistik yang dapat digunakan untuk kategorisasi sumber daya. Analisis geostatistik di Sektor Rabau Hulu diawali dengan korelasi tubuh bijih antara lubang bor. Tubuh-tubuh bijih di Sektor Rabau Hulu merupakan domain individual yang selanjutnya dipertimbangkan memiliki domain tegas. Tubuh bijih-15 dipilih untuk digunakan pada analisis geostatistik selanjutnya karena distribusinya yang luas dan paling banyak dipenetrasi bor. Tahapan dalam analisis geostatistik mencakup komposit downhole, analisis statistik dasar, determinasi outliers, analisis variogram, dan perhitungan ellipsoid anisotropi. Analisis geostatistik menghasilkan kemungkinan aplikasi dua atribut estimasi sumber daya, yaitu kriging efisiensi dan kriging varians. Berdasarkan penilaian teknis kemenerusan tubuh bijih terhadap intensitas lubang bor, kriging efisiensi dipertimbangkan sesuai dengan informasi geologi dan dapat digunakan sebagai parameter untuk penentuan kategori sumber daya.
Keywords
Full Text:
PDFReferences
[1] P. Goovaerts, Geostatistics for Natural Resources Evaluation. New York, 1997.
[2] H. Yan and H. Moradkhani, “Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling,” Adv. Water Resour., vol. 94, pp. 364–378, 2016.
[3] A. M. .C. Wadoux, D. J. Brus, M. A. Rico-Ramirez, and G. B. M. Heuvelink, “Sampling design optimisation for rainfall prediction using a non-stationary geostatistical model,” Adv. Water Resour., vol. 107, pp. 126–138, 2017.
[4] C. Cafaro, C. Giovani, and M. Garavaglia, “Geostatistical simulations for radon indoor with a nested model including the housing factor,” J. Environ. Radioact., vol. 151, pp. 264–274, 2016.
[5] H. Zou, S. Liu, G. Cai, T. V. Bheemasetti, and A. J. Puppala, “Mapping probability of liquefaction using geostatistics and first order reliability method based on CPTU measurements,” Eng. Geol., vol. 218, pp. 197–212, 2017.
[6] J. Chen, X. Li, H. Zhu, and Y. Rubin, “Geostatistical method for inferring RMR ahead of tunnel face excavation using dynamically exposed geological information,” Eng. Geol., vol. 228, no. December 2016, pp. 214–223, 2017.
[7] B. Sadeghi, N. Madani, and E. J. M. Carranza, “Combination of geostatistical simulation and fractal modeling for mineral resource classification,” J. Geochemical Explor., vol. 149, pp. 59–73, 2015.
[8] F. Atalay and A. E. Tercan, “Coal resource estimation using Gaussian copula,” Int. J. Coal Geol., vol. 175, no. January, pp. 1–9, 2017.
[9] M. E. Hohn and J. Q. Britton, “A geostatistical case study in West Virginia: All coals are not the same,” Int. J. Coal Geol., vol. 112, pp. 125–133, 2013.
[10] M. N. Heriawan and K. Koike, “Identifying spatial heterogeneity of coal resource quality in a multilayer coal deposit by multivariate geostatistics,” Int. J. Coal Geol., vol. 73, pp. 307–330, 2008.
[11] M. G. Deng, W. C. Li, B. Li, L. H. Li, S. De Jiang, G. X. Xiong, X. S. Zhang, and H. J. Yu, “Application of Log Kriging on Estimated Reserves of the 10-9 Ore Body of Lutangba in the Gejiu Tin Deposits,” J. China Univ. Min. Technol., vol. 17, no. 2, pp. 286–289, 2007.
[12] Suharji, “Re-evaluasi Sumber Daya Uranium di Sektor Semut, Kalan, Kalimantan Barat,” in Prosiding Seminar Nasional Geologi Nuklir dan Sumber Daya Tambang Tahun 2014, pp. 35–50, 2014.
[13] H. Syaeful, Suharji, and A. Sumaryanto, “Pemodelan Geologi dan Estimasi Sumber Daya Uranium di Sektor Lemajung, Kalan, Kalimantan Barat,” in Prosiding Seminar Nasional Teknologi Energi Nuklir Tahun 2014, pp. 329–342, 2014.
[14] A. G. Muhammad and B. Soetopo, “Pemodelan dan Estimasi Sumber Daya Uranium di Sektor Lembah Hitam, Kalan, Kalimantan Barat,” Eksplorium, vol. 37, no. 1, pp. 1–12, 2016.
[15] X. Emery, J. M. Ortiz, and J. J. Rodríguez, “Quantifying uncertainty in mineral resources by use of classification schemes and conditional simulations,” Math. Geol., vol. 38, no. 4, pp. 445–464, 2006.
[16] I. M. Glacken and D. V. Snowden, “Mineral Resource Estimation,” in Mineral Resources and Ore Reserve Estimation - The AusIMM Guide to Good Practice, A. C. Edwards, Ed. Melbourne: The Australasian Institute of Mining and Metallurgy, pp. 189–198, 2001.
[17] G. Blackwell, “Relative kriging errors - A basis for mineral resource classification,” Explor. Min. Geol., vol. 7, no. 1, pp. 99–105, 1999.
[18] S. Tjokrokardono, B. Sutopo, L. Subiantoro, and K. Setiawan, “Geologi dan Mineralisasi Uranium Kalan, Kalimantan Barat,” in Kumpulan Laporan Hasil Penelitian Tahun 2005, pp. 27–52, 2005.
[19] B. Soetopo, R. Witjahyati, and Y. Wusana, “Synthesis on Geology and Uranium Mineralization of Rabau Hulu Sector, Kalan, West Kalimantan,” in Seminar Geologi Nuklir dan Sumberdaya Tambang Tahun 2004, pp. 84–99, 2004.
[20] N. Mery, X. Emery, A. Cáceres, D. Ribeiro, and E. Cunha, “Geostatistical modeling of the geological uncertainty in an iron ore deposit,” Ore Geol. Rev., vol. 88, pp. 336–351, 2017.
[21] V. Snowden, “Practical interpretation of resource classification guidelines,” AusIMM 1996 Annu. Conf., no. 09, pp. 1–16, 2000.
[22] C. J. Mwenifumbo and A. L. Mwenifumbo, Geophysical logging methods for uranium geology and exploration. Geological Survey of Canada, Technical Note 4, 2013.
[23] D. R. Young, “The effect of ignoring the sample support on the global and local mean grade estimates, mineral resource classification and project valuation of variable width Merensky and UG2 Reef orebodies,” Third Int. Platin. Conf., no. 2006, pp. 63–76, 2008.
Refbacks
- There are currently no refbacks.
Copyright EKSPLORIUM: Buletin Pusat Pengembangan Bahan Galian Nuklir (e-ISSN 2503-426x p-ISSN 0854-1418)
National Research and Innovation Agency (BRIN), KA. B.J. Habibie, Jl. M.H. Thamrin No.8, Jakarta, 10340, Indonesia.
