پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شمارهها | 6,532 |
| تعداد مقالات | 70,501 |
| تعداد مشاهده مقاله | 124,094,222 |
| تعداد دریافت فایل اصل مقاله | 97,199,394 |
A hybrid-based clustering algorithm for targeting porphyry copper mineralization at Chahargonbad district in SE Iran | ||
| International Journal of Mining and Geo-Engineering | ||
| دوره 55، شماره 1، شهریور 2021، صفحه 19-28 اصل مقاله (1.82 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2020.295510.594837 | ||
| نویسندگان | ||
| Hossein Rahimi1؛ Maysam Abedi1؛ Abbas Bahroudi* 1؛ Mohammad Javad Rezapour2؛ Gholam-Reza Elyasi2؛ Soheila Aslani1 | ||
| 1School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| 2Geo-Exploration Targeting Lab (GET-Lab), School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| This work presents a hybrid-based clustering approach for mineral potential mapping (MPM) of porphyry-type Cu mineralization at Kerman province in the SE of Iran. Whereby a multidisciplinary geospatial data set was processed and integrated in the Chahargonbad district. Data-driven prediction-area (P-A) plots were drawn for each evidence layer derived from geological, geochemical, geophysical and satellite imagery data. The P-A plots provide insight into the weight of evidence for synthesizing all geospatial layers. Out of many knowledge-driven methods which biasing from experts' opinions, index overlay and fuzzy operators were employed to find out an optimum Cu favorability map through calculating an efficiency index representing the performance of each MPM. A concentration-area (C-A) fractal model was implemented to separate the mineral favorability map into some populations to ensure correct determining the cluster numbers. Clusters number is a prerequisite which must be defined correctly to increase the performance of clustering analysis for generating reliable results in MPM. Such an appropriate number of clusters can be incorporated in running three prevalent groups of clustering methodologies as data-driven approaches in MPM. They are self-organizing map, fuzzy c-means, and k-means algorithms. One of the reasons for this tendency to consider a hybrid-based method is that it overcomes the shortcomings of the both methods (bias of experts’ opinions and unknown clusters number) in mineral favorability mapping. The unknown number of clusters was determined through a knowledge-driven method, and then it was passed to an unsupervised data-driven method, i.e. clustering algorithm. This hybrid method produces synthesized maps in close association with known porphyry-Cu mineralization in the Chahargonbad area. | ||
| کلیدواژهها | ||
| Clustering؛ Hybrid method؛ Mineral potential mapping؛ Porphyry copper؛ Chahargonbad | ||
| مراجع | ||
|
[1] Yousefi, M., & Kamkar Rouhani, A. (2010). Principle of mineral potential modeling (Vol. 1). Amir Kabir University, Tehran.
[2] Carranza, E.J.M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). Elsevier.
[3] Carranza, E.J.M. (2017). Natural resources research publications on geochemical anomaly and mineral potential mapping, and introduction to the special issue of papers in these fields. Natural Resources Research, 26(4), 379-410.
[4] Kashani, S.B.M., Abedi, M., & Norouzi, G.H. (2016). Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets, a case study in seridune deposit, Iran. Earth Science Informatics, 9(2), 167-181.
[5] Pan, G. & Harris, D. (2000). Information synthesis for mineral exploration: Oxford Univ. Press, New York.
[6] Carranza, E.J.M. & Hale, M. (2002). Where porphyry copper deposits are spatially localized? A case study in Benguet province, Philippines. Natural Resources Research, 11(1), 45-59.
[7] Nykänen, V., & Salmirinne, H. (2007). Prospectivity analysis of gold using regional geophysical and geochemical data from the Central Lapland Greenstone Belt, Finland. Geological Survey of Finland, 44, 251-269.
[8] Agterberg, F., & Bonham-Carter. G.F. (1999). Logistic regression and weights of evidence modeling in mineral exploration. in Proceedings of the 28th International Symposium on Applications of Computer in the Mineral Industry (APCOM), Golden, Colorado.
[9] Carranza, E.J.M., & Hale, M. (2001). Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Exploration and Mining Geology, 10(3), 165- 175.
[10] Mejía-Herrera, P.,Royer, J.J., Caumon, G., & Cheilletz, A. (2015). Curvature attribute from surface-restoration as predictor variable in Kupferschiefer copper potentials. Natural Resources Research, 24(3), 275-290.
[11] Abedi, M., & Norouzi, G.H. (2012). Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. Journal of Applied Geophysics, 83, 35-45.
[12] Nykänen, V. (2008). Radial basis functional link nets used as a prospectivity mapping tool for orogenic gold deposits within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield. H. Rahimi et al. / Int. J. Min. & Geo-Eng. (IJMGE), 55-1 (2021) 19-28 27 Natural Resources Research, 17(1), 29-48.
[13] Harris, D., Zurcher, L., Stanley, M., Marlow, J., & Pan, G. (2003). A comparative analysis of favorability mappings by weights of evidence, probabilistic neural networks, discriminant analysis, and logistic regression. Natural Resources Research, 12(4), 241-255.
[14] Porwal, A., Carranza, E.J.M., & Hale, M. (2003). Artificial neural networks for mineral-potential mapping: a case study from Aravalli Province, Western India. Natural resources research, 12(3), 155-171. [15] Porwal, A., Carranza, E.J.M., & Hale, M. (2004). A hybrid neuro[1]fuzzy model for mineral potential mapping. Mathematical Geology, 36(7), 803-826.
[16] Singer, D.A., & Kouda, R. (1996). Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan. Mathematical Geology, 28(8), 1017-1023.
[17] Agterberg, F., Bonham-Carter, G.F., & Wright, D. (1990). Statistical pattern integration for mineral exploration, in Computer applications in resource estimation. Elsevier, 1-21.
[18] Bonham-Carter, G.F. (1989). Weights of evidence modeling: a new approach to mapping mineral potential. Statistical applications in the earth sciences, 171-183.
[19] Abedi, M., Norouzi, G.H., & Bahroudi, A. (2012). Support vector machine for multi-classification of mineral prospectivity areas. Computers & Geosciences, 46, 272-283.
[20] Shabankareh, M., & Hezarkhani, A. (2017). Application of support vector machines for copper potential mapping in Kerman region, Iran. Journal of African Earth Sciences, 128, 116-126.
[21] Carranza, E.J.M., & Laborte, A.G. (2016). Data-driven predictive modeling of mineral prospectivity using random forests: a case study in Catanduanes Island (Philippines). Natural Resources Research, 25(1), 35-50.
[22] Zhang, Z., Zuo, R., & Xiong, Y. (2016). A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China. Science China Earth Sciences, 59(3), 556- 572.
[23] Abedi, M., Norouzi, G.H., & Torabi, S.A. (2013). Clustering of mineral prospectivity area as an unsupervised classification approach to explore copper deposit. Arabian Journal of Geosciences, 6(10), 3601-3613.
[24] Eberle, D.G., & Paasche, H. (2012). Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites. Geophysics, 77(4), 167-176.
[25] Paasche, H., & Eberle, D.G. (2009). Rapid integration of large airborne geophysical data suites using a fuzzy partitioning cluster algorithm: a tool for geological mapping and mineral exploration targeting. Exploration Geophysics, 40(3), 277-287.
[26] Ghezelbash, R., Maghsoudi, A., & Carranza, E.J.M. (2019). Mapping of single-and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models. Journal of Geochemical Exploration, 199, 90-104.
[27] Daviran, M., Maghsoudi, A., Cohen, D., Ghezelbash, R., & Yilmaz, H. (2019). Assessment of Various Fuzzy c-Mean Clustering Validation Indices for Mapping Mineral Prospectivity: Combination of Multifractal Geochemical Model and Mineralization Processes. Natural Resources Research, 29(1), 229- 246.
[28] Abedi, M., Torabi, S.A., Norouzi, G.H., Hamzeh, M., & Elyasi, G.R. (2012). PROMETHEE II: a knowledge-driven method for copper exploration. Computers & Geosciences, 46, 255-263.
[29] Bonham-Carter, G.F., (1994). Geographic information systems for geoscientists-modeling with GIS. Computer methods in the geoscientists, 13, 398.
[30] Carranza, E.J.M., Mangaoang, J.C., & Hale, M., (1999). Application of mineral exploration models and GIS to generate mineral potential maps as input for optimum land-use planning in the Philippines. Natural Resources Research, 8(2), 165-173.
[31] Mirzaei, M., Afzal, P., Adib, A., Khalajmasoumi, M., & Zia Zarifi, A. (2014). Prospection of Iron and Manganese Using Index Overlay and Fuzzy Logic Methods in Balvard 1: 100,000 Sheet, SE Iran. Iranian Journal of Earth Sciences, 6(1), 1-11.
[32] Sadeghi, B., Khalajmasoumi, M., Afzal, P., & Moarefvand, P. (2014). Discrimination of iron high potential zones at the zaghia iron ore deposit, bafq, using index overlay GIS method. Iran J Earth Sci, 6, 91-98.
[33] Sadeghi, B., & Khalajmasoumi, M. (2015). A futuristic review for evaluation of geothermal potentials using fuzzy logic and binary index overlay in GIS environment. Renewable and Sustainable Energy Reviews, 43, 818-831.
[34] Abedi, M., Torabi, S.A., & Norouzi, G.H. (2013). Application of fuzzy AHP method to integrate geophysical data in a prospect scale, a case study: Seridune copper deposit. Bollettino di Geofisica Teorica ed Applicata, 54(2).
[35] An, P., Moon, W.M., & Rencz, A. (1991). Application of fuzzy set theory for integration of geological, geophysical and remote sensing data. Canadian Journal of Exploration Geophysics, 27(1), 1-11.
[36] Chung, C.J.F., & Moon, W.M. (1991). Combination rules of spatial geoscience data for mineral exploration. Geoinformatics, 1991. 2(2): p. 159-169.
[37] Moradi, M., Basiri, S., Kananian, A., & Kabiri, K. (2015). Fuzzy logic modeling for hydrothermal gold mineralization mapping using geochemical, geological, ASTER imageries and other geo-data, a case study in Central Alborz, Iran. Earth Science Informatics, 8(1), 197-205.
[38] Abedi, M., Norouzi, G.H., & Fathianpour, N. (2013). Fuzzy outranking approach: a knowledge-driven method for mineral prospectivity mapping. International Journal of Applied Earth Observation and Geoinformation, 21, 556-567.
[39] Abedi, M., Norouzi, G.H., & Fathianpour, N. (2015). Fuzzy ordered weighted averaging method: a knowledge-driven approach for mineral potential mapping. Geophys Prospect, 63, 461-477.
[40] Abedi, M., Torabi, S.A., Nourozi, G.H., & Hamzeh, M. (2012). ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. Journal of applied geophysics, 87, 9-18.
[41] Moon, W.M. (1990). Integration of geophysical and geological data using evidential belief function. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 711-720.
[42] Tangestani, M.H., & Moore, F. (2002). The use of Dempster–Shafer model and GIS in integration of geoscientific data for porphyry copper potential mapping, north of Shahr-e-Babak, Iran. International Journal of Applied Earth Observation and Geoinformation, 4(1), 65-74.
[43] Pazand, K., & Hezarkhani, A. (2015). Porphyry Cu potential area selection using the combine AHP-TOPSIS methods: a case study in Siahrud area (NW, Iran). Earth Science Informatics, 8(1), 207- 28 H. Rahimi et al. / Int. J. Min. & Geo-Eng. (IJMGE), 55-1 (2021) 19-28 220.
[44] Yousefi, M., & Carranza, E.J.M. (2016) Data-driven index overlay and Boolean logic mineral prospectivity modeling in greenfields exploration. Natural Resources Research, 25(1), 3-18.
[45] Yousefi, M., & Carranza, E.J.M. (2015). Prediction–area (P–A) plot and C–A fractal analysis to classify and evaluate evidential maps for mineral prospectivity modeling. Computers & Geosciences, 79, 69-81.
[46] Theodoridis, S., & Koutroumbas, K. (2009). Pattern recognition. Elsevier Inc.
[47] Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(2), 411-423.
[48] Saha, S., & Bandyopadhyay, S. (2012). Some connectivity based cluster validity indices. Applied Soft Computing, 12(5), 1555-1565.
[49] Ghezelbash, R., Maghsoudi, A., & Carranza, E.J.M. (2020). Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm. Computers & Geosciences, 134, 104335.
[50] Rezapour, M.J., Abedi, M., Bahroudi, A., & Rahimi, H. (2019). A clustering approach for mineral potential mapping: A deposit-scale porphyry copper exploration targeting. Geopersia, 10(1).
[51] Cheng, Q., Agterberg, F., & Ballantyne, S. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51(2),109-130.
[52] Alavi, M. (1994). Tectonics of the Zagros orogenic belt of Iran: new data and interpretations. Tectonophysics, 229(3-4), 211-238.
[53] Forster, H. (1976). Continental drift in Iran in relation to the Afar structure. Afar between continental and oceanic rifting (VII), 182- 190.
[54] Elyasi, G.R. (2009). Mineral potential mapping in detailed stage using GIS in one of exploration prospects of Kerman Province. Master of Science Thesis, University of Tehran (published in Persian).
[55] Khan Nazer, N., Emami, M., & Ghaforie, M. (1995). Geological map of Chahargonbad. Geological survey of Iran.
[56] Yazdi, Z., Jafari Rad, A.R., & Kheyrollahi, H. (2015). Recognition of geological features and alteration zones related to porphyry copper mineralization using airborne geophysical data a case study: Chahargonbad1: 100000 geological map, Kerman province, central Iran.
[57] Elyasi, G.R., Bahroudi, A., Abedi, M., & Rahimi, H. (2020). Weighted Photolineaments Factor (WPF): An Enhanced Method to Generate a Predictive Structural Evidential Map with Low Uncertainty, a Case Study in Chahargonbad Area, Iran. Natural Resources Research, 1-33.
[58] Nabighian, M.N. (1974). Additional comments on the analytic signal of two-dimensional magnetic bodies with polygonal cross[1]section. Geophysics, 39(1), 85-92.
[59] Ford, K., Keating, P., & Thomas, M. (2007). Overview of geophysical signatures associated with Canadian ore deposits. Geological Association of Canada. Mineral Deposits Division, Special Publication, (5), 939-970.
[60] Dunn, J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters (Vol. 3). Journal of Cybernetics.
[61] Bezdek, J. (1981). Pattern recognition with fuzzy objective function algorithms-NY. Plenum Press.
[62] Barbakh, W.A., Wu, Y., & C. Fyfe, C. (2009). Non-standard parameter adaptation for exploratory data analysis (Vol. 249). Springer.
[63] Jain, A.K., Murty, M.N., & Flynn, P.J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323.
[64] Kohonen, T. (2012). Self-organization and associative memory (Vol. 8). Springer Science & Business Media.
[65] Kohonen, T., & Somervuo, P. (1998). Self-organizing maps of symbol strings. Neurocomputing, 21(1-3), 19-30.
[66] Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on neural networks, 11(3), 586- 600.
[67] Abedi, M., Mohammadi, R., Nourozi, G.H., & Mir Mohammadi, M.S. (2016). A comprehensive VIKOR method for integration of various exploratory data in mineral potential mapping. Arabian Journal of Geosciences, 9(6), 482 | ||
|
آمار تعداد مشاهده مقاله: 520 تعداد دریافت فایل اصل مقاله: 544 |
||