THE PERFORMANCE OF ESTIMATION TECHNIQUES FOR NICKEL LATERITE RESOURCE MODELING
DOI:
https://doi.org/10.11113/jurnalteknologi.v84.17560Keywords:
Estimation, NNP, IDW, OK, nickel lateriteAbstract
The choice of estimation technique according to geological conditions and mineralization character is the main problem in estimating block grade of nickel laterite. CV (coefficient of variance) and variogram determine the choice of estimation technique for nickel laterite resource classification. This study aims to evaluate various techniques for estimating block grades and to select the appropriate method for the classification of nickel laterite resources. The basic statistical analysis is to find out the description of the data, while the variography is to find out the spatial correlation between the data. Nickel grade estimation results are based on Near Neighbor Polygon (NNP), Inverse Distance Weighting (IDW), and Ordinary Kriging (OK) techniques to determine the classification of nickel resources. Accuracy levels are based on cross-sectional visualization comparisons, plan views, probability plots and linear regression analysis. The OK technique were not superior in grade estimation, especially in nickel laterite deposits. The results showed that the IDW technique was suitable to be applied to the limonite zone, while the NNP technique was suitable to be applied to the saprolite zone. Based on the performance of the estimation technique, the weighted average method can be applied for the classification of inferred, indicated, and measurable resources. The grade-tonnage curve shows the nickel laterite resource potential in the study area.
References
Mongelli, G., Taghipour, B., Sinisi, R., and Khadivar, S. 2019. Mineralization and Element Redistribution in the Chah-Gheib Ni-laterite Ore Zone, Bavanat, Zagros Belt, Iran. Ore Geology Reviews. 111: 102990.
Fua, W., Zhang, Y., Pang, C., Zeng, X., Huang, X., Yang, M., Shao, Y., and Lin, H. 2018. Garnierite Mineralization from a Serpentinite-derived Lateritic Regolith, Sulawesi Island, Indonesia: Mineralogy, Geochemistry and Link to Hydrologic Flow Regime. Journal of Geochemical Exploration. 188: 240-256.
Duée, C., Orberger, B., Maubec, N., Laperche, V., Capar, L., Bourguignon, A., Bourrat, X., Mendili, Y. E., Chateigner, D., Gascoin, S., Guen, M. L., Rodriguez, C., Trotet, F., Kadar, M., Devaux, K., Ollier, M., Pillière, H., Lefèvre, T., and Koert, P. 2019. Impact of Heterogeneities and Surfaceroughness on pXRF, pIR, XRD and Raman Analyses: Challenges for On-line, Real-time Combined Mineralogical and Chemical Analyses on Drillcores and Implication for“High Speed”Ni-Laterite Exploration. Journal of Geochemical Exploration. 198: 1-17.
Martin, R., and Boisvert, J. 2020. Performance of Clustering for the Decision of Stationarity; A Case Study with a Nickel Laterite Deposit. Computers & Geosciences. 144: 104565.
Bargawa, W. S., Hariyanto, R., Lusantono, O. W., Melgis, R. F. B., and Nugroho, S. P. 2021. International Journal of GEOMATE. 20(77): 189-196.
Jo, H., & Pyrcz, M. J. 2021. Automatic Semivariogram Modeling by Convolutional Neural Network. Mathematical Geosciences. 177-205.
Katsuaki Koike, Takuya Kiriyama, Lei Lu, Taiki Kubo, Mohamad Nur Heriawan, and Ryoichi Yamada. 2022. Incorporation of Geological Constraints and Semivariogram Scaling Law into Geostatistical Modeling of Metal Contents in Hydrothermal Deposits for Improved Accuracy. Journal of Geochemical Exploration. 233.
Bargawa, W. S., Nugroho, S. P., Hariyanto, R. Lusantono O. W., and Melgis, R. F. B. 2020. Geostatistical Modeling of Ore Grade in a Laterite Nickel Deposit, LPPM UPN Veteran Yogyakarta Conference Series. Proceeding on Engineering and Science Series (ESS). 1(1): 301-310.
Zhang, X., Lian, L., and Zhu, F. 2021. Parameter Fitting of Variogram based on Hybrid Algorithm of Particle Swarm and Artificial Fish Swarm Future Generation Computer Systems. Future Generation Computer Systems. 116: 265-274.
Bargawa, W. S. 2016. Mineral Resource Estimation Using Weighted Jackknife Kriging. AIP Conf. Proc. 1755: 120001-1-120001-6.
Bargawa, W. S., and Tobing, R. F. 2020. Iron Ore Resource Modeling and Estimation Using Geostatistics. AIP Conference Proceedings. 2245: 070016.
Bargawa, W. S., Rauf, A., and Amri, N. A. 2016. Gold Resource Modeling Using Pod Indicator Kriging. Progress in Applied Mathematics in Science and Engineering Proceedings. 1705: 020025-1-120025-8.
Osterholt, V., and Dimitrakopoulos, R. 2018. Simulation of Orebody Geology with Multiple-Point Geostatistics-Application at Yandi Channel Iron Ore Deposit WA and Implications for Resource Uncertainty. Advances in Applied Strategic Mine Planning. 335-352.
Zulkarnain, I., and Bargawa, W. S. 2018. Classification of Coal Resources Using DrillHole Spacing Analysis (DHSA). Journal of Geological Resource and Engineering. 6: 151-159.
Zakeri, F., and Mariethoz, G. 2021. A Review of Geostatistical Simulation Models Applied to Satellite Remote Sensing: Methods and Applications. Remote Sensing of Environment. 259: 112381.
Adeli, A., and Emery, X. 2021. Geostatistical Simulation of Rock Physical and Geochemical Properties with Spatial Filtering and Its Application to Predictive Geological Mapping. Journal of Geochemical Exploration. 220: 10666.
Afeni, T. B., Akeju, V. O., & Aladejare, A. E. 2020. A Comparative Study of Geometric and Geostatistical Methods for Qualitative Reserve Estimation of Limestone Deposit. Geoscience Frontiers. 243-253.
Isatelle, F., and Rivoirard, J. 2019. Mineral Resources Classification of a Nickel Laterite Deposit: Comparison between Conditional Simulations and Specific Areas. The Journal of the Southern African Institute of Mining and Metallurgy.119: 871-882.
Reis, C., Arroyo, C., Curi, A., & Zangrandi, M. Impact of bulk density estimation in mine planning. Mining Technology.2021, 130 (1), 60–65.
Birch, C. 2019. Optimisation of Mining Block Size for Narrow Tabular Gold Deposits. Proceedings of the 27th International Symposium on Mine Planning and Equipment Selection – MPES. 14pp.
Tabesh, M., & Askari-Nasab, H. 2021. Clustering Mining Blocks in Presence of Geological Uncertainty. Mining Technology. 1-15.
Bustillo, Revuelta, M. 2018. Mineral Resources. Springer Textbooks in Earth Sciences, Geography and Environment. 223-309.
Journel, A. G. 2021. Roadblocks to the Evaluation of Ore Reserves—The Simulation Overpass and Putting More Geology into Numerical Models of Deposits. Advances in Applied Strategic Mine Planning. 47-55.
Maleika, W. 2020. Inverse Distance Weighting Method Optimization in the Process of Digital Terrain Model Creation based on Data Collected from a Multibeam Echosounder. Applied Geomatics. 12: 397-407.
Bargawa, W. S., and Amri, N. A. 2016. Mineral Resources Estimation based on Block Modeling. AIP Conference Proceedings. 1705(1): 020001.
Clark, I. 2021. Quantification of Mineral Resources, Encyclopedia of Geology. Second Edition. 633-642.
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