Ore Deposit Boundary Modification in Afghanistan Aynak Central Copper Deposit using Sequential Indicator Simulation and Indicator Kriging | ||
| Journal of Mining and Environment | ||
| مقاله 1، دوره 13، شماره 2، تیر 2022، صفحه 325-340 اصل مقاله (8.51 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2022.11557.2142 | ||
| نویسندگان | ||
| Shah H. Shafayi1، 2؛ F. Mohammad Torab* 3 | ||
| 1Technical University of Ghazni, Ghazni, Afghanistan | ||
| 2Faculty of Mining and Metallurgical Engineering, Yazd University, Yazd, Iran | ||
| 3Faculty of Mining and Metallurgical Engineering, Yazd University | ||
| چکیده | ||
| The Aynak copper deposit is the most important strata-bound copper reserve in Afghanistan. The main purpose of this work is the ore deposit boundary modification and reserve estimation of the Aynak central copper deposit using the geostatistical methods. The ordinary kiging (OK), indicator kriging (IK) and sequential indicator simulation (SIS) methods were used to modify the optimum ore deposit boundary and ore reserve estimation. Then the results, accuracy and efficiency of these three methods are compared. Before the ore reserve estimation, the pre-processing, statistical and geostatistical analysis of the sampled data are performed. For a precise estimation process, it is necessary to modify the optimum ore body boundary as an estimation space. Therefore, the IK and SIS methods are applied to revise the conventional ore deposit boundary and estimation space. At the first stage, the ore body wireframe and solid model are obtained using the conventional cross-section method. The block model is created covering the mineralization space of the ore body, and firstly constrained by the conventional model (solid model). Consequently, the ore body model is adapted and bounded using the IK and SIS geostatistical methods. Finally, the log-kriging method that is basically unbiased and guarantees the minimum estimation error is used to estimate the Cu concentration in each block, and after back-transformation, the grade-tonnage curves are plotted. The total tonnage of the deposit is calculated based on different cut-off grades. Assuming the cut-off grade of 0.2% for Cu, the tonnage of ore reserve based on the conventional OK method, IK method, and SIS constrained ore body model are estimated as 453.4, 459.1, and 467.7 million tons with an average grade of 1.077%, 1.08%, and 1.05%, respectively. The proximity of the obtained reserve estimation results using different implemented methodologies is due to the low-grade variability and genetical regularity in the Aynak staratabound copper deposit and guarantees the accuracy of the results obtained in the ore reserve evaluation. | ||
| کلیدواژهها | ||
| Geostatistics؛ Orebody Boundary Modeling؛ Indicator kriging؛ Sequential Indicator-Simulation؛ Aynak Copper Deposit | ||
| مراجع | ||
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[1]. Gholamnejad, J. Ansari, A.H. Yarahmadi, B . and Taqizadeh, M. (2010). Determination of Ore/Waste Boundary Using Indicator Kriging, Case Study: Choghart Iron Mine of Iran. Int J of Eng. 23 (3): 269-276.
[2]. Dag, A. and Mert, B.A. (2008). Evaluating thickness of bauxite deposit using indicator geostatistics and fuzzy estimation. Res geo. 58 (2): 188-195. doi: 10.1111/j.1751-3928.2008.00055.x
[3]. Afzal, P. (2018). Comparing ordinary kriging and advanced inverse distance squared methods based on estimating coal deposits; case study: East-Parvadeh deposit, central Iran. J of Min Environ. 9 (3): 753-760. doi:10.22044/jme.2018.6897.1522
[4]. Daya, A A . and Zaremotlagh, S. (2013). A comparative analysis between disjunctive kriging and ordinary kriging for estimating the reserve of a mine: A case study of Choghart iron ore deposit. J of Min Met, 49(1), 1-8.
[5]. Gholampour, O. Hezarkhani, A. Maghsoudi, A . and 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 of Min Environ. 10 (3): 575-595.
[6]. Daya, A.A. (2015). Ordinary kriging for the estimation of vein type copper deposit: A case study of the Chelkureh, Iran. J of Min & Met. 51 (1): 1-14.
[7]. Rao, V.K. Rao, C . and Narayana, A. (2014). Assessing grade domain of iron ore deposit using geostatistical modelling: A case study. J of the Geol Soci of Ind. 83 (5): 549-554.
[8]. Daya, A.A. (2012). Reserve estimation of central part of Choghart north anomaly iron ore deposit through ordinary kriging method. Int J of Min Sci Tech. 22 (4): 573-577.
[9]. Kasmaee, S. Raspa, G. de Fouquet, C. Tinti, F. Bonduà, S . and Bruno, R. (2019). Geostatistical estimation of multi-domain deposits with transitional boundaries: a sensitivity study for the Sechahun iron mine. Minerals, 9(2), 115.
[10]. Taghvaeenezhad, M. Shayestehfar, M. Moarefvand, P . and Rezaei, A. (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. Geosys Eng, 23(4), 216-225.
[11]. Liu, L. Li, J. Zhou, R . and Sun, T. (2016). 3D modeling of the porphyry-related Dawangding gold deposit in south China: Implications for ore genesis and resources evaluation. J of Geoche Exp, 164, 164-185.
[12]. Pishbin, M . and Fathianpour, N. (2014). Assessing the performance of statistical-structural and geostatistical methods in estimating the 3D distribution of the uniaxial compressive strength parameter in the Sarcheshmeh porphyry copper deposit. Intl J of Min and Geo-Eng. 48 (1): 11-30.
[13]. Mousavi, A. Sayadi, A.R . and Fathianpour, N. (2016). A comparative study of kriging and simulation-based methods in classifying ore and waste blocks. Arab J of Geo sci. 9 (17): 1-9.
[14]. Jang, C-S. Chen, S-K . and Cheng, Y-T. (2016). Spatial estimation of the thickness of low permeability topsoil materials by using a combined ordinary-indicator kriging approach with multiple thresholds. Eng Geo, 207, 56-65.
[15]. Kasmaee, S . and Torab, F.M. (2014). Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging. J of Cent south Uni. 21 (5): 2011-2017. doi:10.1007/s11771-014-2150-x
[16]. Khakestar, M.S. Madani, H. Hassani, H . and Moarefvand, P. (2013). Determining the best search neighbourhood in reserve estimation, using geostatistical method: A case study anomaly No 12A iron deposit in central Iran. J of the Geol Soci of Ind. 81 (4): 581-585.
[17]. Rezaee, H. Asghari, O . and Yamamoto, J.K. (2011). On the reduction of the ordinary kriging smoothing effect. J of Min Environ. 2 (2): 102-117.
[18]. Rahimi, H. Asghari, O. Hajizadeh, F . and Meysami, F. (2018). Investigation of linear and non-linear estimation methods in highly-skewed gold distribution. J of Min Environ. 9 (4): 967-979. doi:10.22044/jme.2018.7023.1544
[19]. Masoumi, I. Kamali, G.R . and Asghari, O. (2019). Assessment of an ore body internal dilution based on multivariate geostatistical simulation using exploratory drill hole data. J of Min and Environ. 10 (1): 271-286.
[20]. Hajsadeghi, S. Asghari, O. Mirmohammadi, M. Afzal, P . and Meshkani, S.A. (2020). Uncertainty-Volume fractal model for delineating copper mineralization controllers using geostatistical simulation in Nohkouhi volcanogenic massive sulfide deposit, Central Iran. Bulletin of the Mineral Research and Exploration. 161 (161): 1-11.
[21]. Azizi, M. Saibi, H . and Cooper, G.R.J. (2015). Mineral and structural mapping of the Aynak-Logar Valley (eastern Afghanistan) from hyperspectral remote sensing data and aeromagnetic data. Arab J of Geo sci. 8 (12): 10911-10918.
[22]. Waizy, H. Moles, N.R. Smith, M.P. and Boyce, A.J. (2020). Formation of the giant Aynak copper deposit, Afghanistan: evidence from mineralogy, lithogeochemistry and sulfur isotopes. Int Geol Rev. doi:10.1080/00206814.2020.1824129
[23]. A.G.S . and B.G.S. (2005). Aynak information package, part I Introduction. Afghanistan: Geological Survey and British Geological Survey.
[24]. Taylor, C.D. Peters, S.G. and Sutphin, D.M. (2011). Summary of the Aynak Copper, Cobalt and Chromium Area of Interest. Afghanistan Geological Survey and U.S Geological Survey.
[25]. AGS . and B.G.S. (2005). Aynak information package, Part II geological setting of Aynak and summary of exploration. Afghanistan Geological Survey and British Geological Survey.
[26]. AGS. and BGS. (2005). Aynak information Package, Part III Preliminary report on results of geological exploration on the Aynak copper deposit from 1974 to 1976. Afghanistan Geological Survey and British Geological Survey.
[27]. Al-Hassan, S, Boamah, E (2015). Comparison of Ordinary Kriging and Multiple Indicator Kriging Estimates of Asuadai Deposit at Adansi Gold Ghana Limited. Ghana Min J. 15 (2): 42-49.
[28]. Taghvaeenejad, M. Shayestefar, M R . and Moarefvand, P. (2021). Applying Analytical and Quantitative Criteria to Estimate Block Model Uncertainty and Mineral Reserve Classification: A Case Study: Khoshumi Uranium Deposit in Yazd. J of Min Environ. 12 (2): 425-441.
[29]. Trong, V.D. Bao, T.D . and Fomin, S. (2017). Ordinary kriging comparison and inverse distance weighting for quality assessment of Vietnam cement limestone deposits. Proceedings of the International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, Albena, Bulgaria, 29.
[30]. Daya, A.A. (2019). Nonlinear disjunctive kriging for the estimating and modeling of a vein copper deposit. Ir J of Earth Sci. 11 (3): 226-236.
[31]. Amirpoursaeid, F . and Asghari, O. (2016). Application of truncated gaussian simulation to ore-waste boundary modeling of Golgohar iron deposit. Int J of Min and Geo Eng. 50 (2): 175-181.
[32]. Bargawa, W.S. Rauf, A . and Amri, N.A. (2016). Gold resource modeling using pod indicator kriging. Paper presented at the AIP Conference Proceedings.
[33]. Hasani, S. Asghari, O. Ardejani, F.D. and Yousefi, S. (2017). Spatial modelling of hazardous elements at waste dumps using geostatistical approach: a case study Sarcheshmeh copper mine, Iran. Environ Earth Sci. 76 (15): 1-13.
[34]. Antunes, I M H R . and Albuquerque, M T D. (2013). Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal). Sci of the Total Environ, 442, 545-552.
[35]. Liu, C.W. Jang, C.S. and Liao, C.M. (2004). Evaluation of arsenic contamination potential using indicator kriging in the Yun-Lin aquifer (Taiwan). Sci of the Total Environ. 321 (1-3): 173-188.
[36]. Babaei, M. Abedi, M. Norouzi, G.H . and A.S, K. (2020). Geostatistical modeling of electrical resistivity tomography for imaging porphyry Cu mineralization in Takht-e-Gonbad deposit, Iran. J of Min Environ. 11 (1): 143-159.
[37]. Rao, V.K. Rao, C.R. and Narayana, A.C. (2014). Assessing grade domain of iron ore deposit using geostatistical modelling: A case study. J of the Geol Soci of Ind. 83 (5): 549-554.
[38]. Rahimi, H. Asghari, O. and Hajizadeh, F. (2018). Selection of optimal thresholds for estimation and simulation based on indicator values of highly skewed distributions of ore data. Natural Resources Research. 27 (4): 437-453. doi:org/10.1007/s11053-017-9366-z
[39]. Azmi, H. Moarefvand, P . and Maghsoudi, A. (2019). Geostatistical estimation to delineate oxide and sulfide zones using geophysical data; a case study of Chahar Bakhshi vein-type gold deposit, NE Iran. J of Min Environ. 10 (3): 679-694. doi:10.22044/jme.2019.7941.1661
[40]. Badel, M. Angorani, S. and Panahi, M.S. (2010). The application of median indicator kriging and neural network in modeling mixed population in an iron ore deposit. Comp & Geo sci. 37 (4): 530-540. doi:10.1016/j.cageo.2010.07.009
[41]. Azmi, H. Moarefvand, P . and Maghsoudi, A. (2020). Resource estimation of the Damanghor gold deposit (Brdaskan, Northeast Iran) based on geological and grade continuity. Geopersia. 10 (2). 381-394. doi:10.22059/GEOPE.2020.295345.648521
[42]. Salarian, S. Asghari, O. Abedi, M. and Alilou, S.K. (2019). Geostatistical and multi-fractal modeling of geological and geophysical characteristics in Ghalandar Skarn-Porphyry Cu Deposit, Iran. J of Min Environ. 10 (4): 1061-1081. doi:10.22044/jme.2019.8687.1752
[43]. Azmi, H. Moarefvand, P . and Maghsoudi, A. Resource estimation of the Damanghor gold deposit (Brdaskan, Northeast Iran) based on geological and grade continuity.
[44]. Rao, V.K. and Narayana, A.C. (2015). Application of nonlinear geostatistical indicator kriging in lithological categorization of an iron ore deposit. Current Sci. 108 (3): 413-421.
[45]. Juang, K-W. Chen, Y-S . and Lee, D-Y. (2004). Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils. Environ Pollu. 127 (2): 229-238.
[46]. Kasmaee, S. Gholamnejad, J. Yarahmadi, A. and Mojtahedzadeh, H. (2010). Reserve estimation of the high phosphorous stockpile at the Choghart iron mine of Iran using geostatistical modeling. Min Sci and Tech (China). 20 (6): 855-860.
[47]. Yun, B. PengFei, Z. Jing, Z. WeiHao, K. XiaoCui, L. Ke, C, LinYing, L. (2020). Application of geo-statistics in calculation of a uranium deposit. Paper presented at the E3S Web of Conferences. | ||
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