An Integrated Geo-Statistical Methodology for an Optimum Resource Estimation of Angouran Underground Mine | ||
| Journal of Mining and Environment | ||
| مقاله 16، دوره 14، شماره 2، تیر 2023، صفحه 635-651 اصل مقاله (4.45 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2023.12710.2308 | ||
| نویسندگان | ||
| Mohammad Rezaei* ؛ Milad Ghasemi | ||
| Department of Mining Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| چکیده | ||
| Resource estimation and determining the grade distribution is one of the most important stages in planning and designing the open-pit and underground mines. In this work, a new mythology is used for resource estimation of the Angouran underground mine based on the optimized integration of the indicator kriging (IK), simple kriging (SK), and inverse distance weighted (IDW) methods. For this purpose, waste blocks are first removed from the block model using the IK method. Then the amount of mineral resource is estimated using the SK and IDW methods. Indeed, variograms are developed to estimate the grade of zinc minerals in the three used methods. Variograms analysis in three directions prove that the studied resource is anisotropic. Also the validation results confirm that the correlation coefficients between the measured and estimated zinc values by the SK and IDW methods equal to 0.76 and 0.75, respectively. Knowing this satisfactory result, a 3D model of the resource is prepared using the IK method, in which the ore and waste sections of the Angouran underground mine are separated definitely. According to the above methodology, the calculated resource of the Angouran underground mine using the SK method is achieved 1373962.5 tons with an average grade of 30.11%, whereas the estimated amount of this resource is attained 1349325 tons with an average grade of 31.88% using the IDW approach. The verification results show that the suggested methodology based on the optimized integration of the IK, SK, and IDW methods can be successfully applied for resource modeling and grade estimating of the Angouran underground mine. | ||
| کلیدواژهها | ||
| Angouran underground mine؛ Resource estimation؛ Indicator kriging؛ Simple kriging؛ Inverse distance weighted | ||
| مراجع | ||
|
[1]. Dominy, S.C., Noppe, M.A. and Annels, A.E. (2002). Errors and uncertainty in mineral resource and ore reserve estimation: the importance of getting it right. Exploration and Mining Geology, 11 (1-4): 77–98.
[2]. Hajsadeghi, S., Asghari, O., Mirmohammadi, M., Afzal, P. and Meshkani, S.A. (2020). Uncertainty-Volume fractal model for delineating copper mineralization controllers using geo-statistical simulation in Nohkouhi volcanogenic massive sulfide deposit, Central Iran. Bulletin of the Mineral Research and Exploration, 161 (161): 1–11.
[3]. Zerzour, O., Gadri, L., Hadji, R., Mebrouk, F. and Hamed, Y. (2021). Geostatistics-based Method for Irregular Mineral Resource Estimation, in Ouenza Iron Mine, Northeastern Algeria. Geotechnical and Geological Engineering, 39(5): 3337–3346.
[4]. Kerbati, N.R., Gadri, L., Hadji, R., Hamad, R. and Boukelloul, M.L. (2020). Graphical and numerical methods for stability analysis in surrounding rock of underground excavations, example of Boukhadra Iron Mine NE Algeria. Geotechnical and Geological Engineering, 38, 2725–733.
[5]. Soltani, F., Moarefvand, P., Alinia, F. and Afzal, P. 2019. Characterizing Rare Earth Elements by coupling multivariate analysis, factor analysis and geo-statistical simulation; case-study of Gazestan deposit, central Iran. Journal of Mining and Environment, 10 (4): 929–945.
[6]. Yasrebi, A.B., Hezarkhani, A., Afzal, P., Karami, R., Eskandarnejad Tehrani, M. and Borumandnia, A. 2020. Application of an Ordinary Kriging-Artificial Neural Network for Elemental Distribution in Kahang Porphyry Deposit, Central Iran. Arabian Journal of Geosciences, 13, 748 (1–14).
[7]. Sotoudeh, F., Ataei, M., Kakaie, R. and Pourrahimian, Y. (2020). Application of Sequential Gaussian Conditional Simulation to Underground Mine Design Under Grade Uncertainty. Journal of Mining and Environment, 11 (3): 695–709.
[8]. Shafayi, S.H. and Mohammad Torab, F. (2022). Ore Deposit Boundary Modification in Afghanistan Aynak Central Copper Deposit using Sequential Indicator Simulation and Indicator Kriging. Journal of Mining and Environment, 13 (2): 325–340.
[9]. Ongarbayev, I. and Madani, N. (2022). Anisotropic Inverse Distance Weighting Method: an Innovative Technique for Resource Modeling of Vein-type Deposits. Journal of Mining and Environment, 13 (4): 957–972.
[10]. Afeni, T.B., Lawal, A.I. and Adeyemi, R.A. (2020). Re-examination of Itakpe iron ore deposit for reserve estimation using geo-statistics and artificial neural network techniques. Arabian Journal of Geosciences, 13 (657).
[11]. Afeni, T.B., Akeju, V.O. and Aladejare, A.E. (2021). A comparative study of geometric and geo-statistical methods for qualitative reserve estimation of limestone deposit. Geoscience Frontiers, 12 (1): 243–253.
[12]. Zhang, J. and Yao, N. (2008). The geo-statistical framework for spatial prediction. Geo-spatial Information Science, 11(3): 180–185.
[13] Yasrebi, J., Saffari, M., Fathi, H., Karimian, N., Moazallahi, M. and Gazni, R. (2009). Evaluation and comparison of ordinary kriging and inverse distance weighting methods for prediction of spatial variability of some soil chemical parameters. Research Journal of Biological Sciences, 4 (1): 93–102.
[14]. Badel, M., Angorani, S. and Panahi, M.S. (2011). The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit. Computers & Geosciences, 37 (4): 530–540.
[15]. Kis, I.M. (2016). Comparison of Ordinary and Universal Kriging interpolation techniques on a depth variable (a case of linear spatial trend): case study of the Sandrovac Field. Rudarsko Geolosko Naftni Zbornik, 31 (2): 41–58.
[16]. Tercan, A.E. and Karayigit, A.I. (2001). Estimation of lignite reserve in the Kalburcayiri field, Kangal basin, Sivas, Turkey. International Journal of Coal Geology, 47 (2): 91–100.
[17]. Misra, D., Samanta, B., Dutta, S. and Bandopadhyay, S. (2007). Evaluation of artificial neural networks and kriging for the prediction of arsenic in Alaskan bedrock-derived stream sediments using gold concentration data. International Journal of Mining, Reclamation and Environment, 21 (4): 282–294.
[18]. Heriawan, M. N. and Koike, K. (2008). Identifying spatial heterogeneity of coal resource quality in a multilayer coal deposit by multivariate geo-statistics. International Journal of Coal Geology, 73 (3–4): 307–330.
[19]. Heriawan, M.N. and Koike, K. (2008). Uncertainty assessment of coal tonnage by spatial modeling of seam distribution and coal quality. International Journal of Coal Geology, 76 (3): 217–226.
[20]. Tahmasebi, P. and Hezarkhani, A. (2010). Comparison of optimized neural network with fuzzy logic for ore grade estimation. Australian Journal of Basic and Applied Sciences, 4 (5): 764–772.
[21]. Olea, R.A., Luppens, J.A. and Tewalt, S.J. (2011). Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit. International Journal of Coal Geology, 85 (1): 78–90.
[22]. Shahbeik, S., Afzal, P., Moarefvand, P. and Qumarsy, M. (2014). Comparison between ordinary kriging (OK) and inverse distance weighted (IDW) based on estimation error. case study: Dardevey iron ore deposit, NE Iran. Arabian Journal of Geosciences, 7 (9): 3693–3704.
[23]. Thakur, M., Samanta, B., and Chakravarty, D. (2014). Support and Information Effect Modeling for Recoverable Reserve Estimation of a Beach Sand Deposit in India. Natural Resources Research, 23 (2): 231–245.
[24]. Daya, A.A. and Bejari, H. (2015). 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.
[25]. Daya, A.A. (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.
[26]. Thakur, M., Samanta, B. and Chakravarty, D. (2016). A non-stationary spatial approach to disjunctive kriging in reserve estimation. Spatial Statistics, 17, 131–160.
[27]. Jafrasteh, B., Fathianpour, N. and Suárez, A. (2018). Comparison of machine learning methods for copper ore grade estimation. Computational Geosciences, 22 (5): 1371–1388.
[28]. Rahimi, H., Asghari, O. and Afshar, A. (2018). A geo-statistical investigation of 3D magnetic inversion results using multi-Gaussian kriging and sequential Gaussian co-simulation. Journal of Applied Geophysics, 154, 136–149.
[29]. Rezaei, A., Hassani, H., Moarefvand, P. and Golmohammadi, A. (2019). Grade 3D Block Modeling and Reserve Estimation of the C-North Iron Skarn Ore Deposit, Sangan, NE Iran. Global Journal of Earth Science and Engineering, 6, 23–37.
[30]. Arinze, I.J., Emedo, C.O. and nad Ugbor, C.C. (2019). A scalar-geometric approach for the probable estimation of the reserve of some Pb-Zn deposits in Ameri, southeastern Nigeria. Journal of Sustainable Mining, 18 (4): 208–225.
[31]. Zerzour, O., Gadri, L., Hadji, R., Mebrouk, F. and Hamed, Y. (2020). Semi-variograms and kriging techniques in iron ore reserve categorization: application at Jebel Wenza deposit. Arabian Journal of Geosciences, 13 (16): 1–10.
[32]. Uyan, M. and Dursun, A.E. (2021). Determination and modeling of lignite reserve using geostatistical analysis and GIS. Arabian Journal of Geosciences, 14 (312).
[33]. Dinda, K. and Samanta, B. (2021). Non-Gaussian Copula Simulation for Estimation of Recoverable Reserve in an Indian Copper Deposit. Natural Resources Research, 30 (1): 57–76.
[34]. Madani, N., Maleki, M. and Soltani-Mohammadi, S. (2022). Geo-statistical modeling of heterogeneous geo-clusters in a copper deposit integrated with multinomial logistic regression: An exercise on resource estimation. Ore Geology Reviews, 150 (3–4): 105132.
[35]. Pebesma, E.J. (2004). Multivariable geo-statistics in S: the gstat package. Computers & Geosciences, 30 (7): 683–691.
[36]. Seyed Mousavi, S.Z., Tavakoli, H., Moarefvand, P. and Rezaei, M. (2020). Micro-structural, petro-graphical and mechanical studies of schist rocks under the freezing-thawing cycles. Cold Regions Science and Technology, 174, 103039.
[37]. Seyed Mousavi, S.Z. and Rezaei, M. (2022). Correlation assessment between degradation ratios of UCS and non-destructive properties of rock under freezing-thawing cycles. Geoderma, 428, 116209. | ||
|
آمار تعداد مشاهده مقاله: 602 تعداد دریافت فایل اصل مقاله: 560 |
||