Application of Probabilistic Clustering Algorithms to Determine Mineralization Areas in Regional-Scale Exploration Studies | ||
| Journal of Mining and Environment | ||
| مقاله 10، دوره 11، شماره 4، دی 2020، صفحه 1059-1078 اصل مقاله (8.49 M) | ||
| نوع مقاله: Original Research Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jme.2020.9867.1910 | ||
| نویسندگان | ||
| H. Geranian* 1؛ Z. Khajeh Miry2 | ||
| 1Department of Mining Engineering, Birjand University of Technology, Birjand, Iran | ||
| 2Industry, Mine & Trade Organization of South Khorasan Province, Birjand, Iran | ||
| چکیده | ||
| In this work, we aim to identify the mineralization areas for the next exploration phases. Thus, the probabilistic clustering algorithms due to the use of appropriate measures, the possibility of working with datasets with missing values, and the lack of trapping in local optimal are used to determine the multi-element geochemical anomalies. Four probabilistic clustering algorithms, namely PHC, PCMC, PEMC, PDBSCAN, and 4138 stream sediment samplings, are used to divide the samples into the three clusters of background, possible anomaly, and probable anomaly populations. In order to determine these anomalies, ten and eight metal elements are selected as the chalcophile and siderophile elements, respectively. The results obtained show the areas of approximately 500 and 5,000 km2 as the areas of the probable and possible anomalies, respectively. The composite geochemical anomalies of the chalcophile and siderophile elements are mostly dominant in the metamorphic-acidic-intermediate rock units and the alkaline-metamorphic-intermediate rock units of the studied area, respectively. Besides, the obtained anomalies of the four clustering algorithms also cover about 65% of the mineralized areas, all mines, and almost 60% of the alteration areas. The validity criterion of the clustering methods show more than 70% validity for the obtained anomalies. The results obtained indicate that the probabilistic clustering algorithms can be an appropriate statistical tool in the regional-scale geochemical explorations. | ||
| کلیدواژهها | ||
| Probabilistic clustering algorithms؛ Composite geochemical anomaly؛ Geochemical potential mapping؛ Hydrothermal alterations؛ Deh-Salm quadrangle | ||
| مراجع | ||
|
[1] Haldar, S.K. (2013). Mineral Exploration: Principles and Applications, Elsevier, 372 p.
[2] Galuszka, A. (2007). A review of geochemical background concepts and an example using data from Poland. Environmental Geology 52(5): 861-870.
[3] Wellmer, F.W. (1998). Statistical Evaluations in Exploration for Mineral Deposits, Springer-Verlag Berlin Heidelberg, 379 p.
[4] Chork, C.Y. (1990). Unmasking multivariate anomalous observations in exploration geochemical data from sheeted-vein tin mineralization near Emmaville, N.S.W., Journal of Geochemical Exploration 37 (2): 205-223.
[5] Geranian, H., Mokhtari, A.R. and Cohen, D.R. (2013). A comparison of fractal methods and probability plots in identifying and mapping soil metal contamination near an active mining area. Iran, Science of the Total Environment 463-464: 845–854.
[6] Wang, J. and Zuo, R. (2016). An extended local gap statistic for identifying geochemical anomalies, Journal of Geochemical Exploration 164: 86–93.
[7] Ghavami-Riabia, R., Seyedrahimi-Niaraqa, M.M., Khalokakaiea, R. and Hazarehb, M.R. (2010). U-spatial statistic data modeled on a probability diagram for investigation of mineralization phases and exploration of shear zone gold deposits. Journal of Geochemical Exploration 104 (1–2): 27–33.
[8] Cheng, Q., Xu, Y. and Grunsky, E. (2000). Integrated Spatial and Spectrum Method for Geochemical Anomaly Separation. Natural Resources Research 9: 43–52.
[9] Cheng, Q., Agterberg, F.P. and Bonham-Carter, G.F. (1996). A spatial analysis method for geochemical anomaly separation. Journal of Geochemical Exploration 56 (3): 183-195.
[10] Daya, A.A. (2015). Comparative study of C–A, C–P, and N–S fractal methods for separating geochemical anomalies from background: A case study of Kamoshgaran region, northwest of Iran. Journal of Geochemical Exploration 150: 52–63.
[11] Jimenez-Espinosa, R., Sousa, A.J. and Chica-Olmo, M. (1993). Identification of geochemical anomalies using principal component analysis and factorial kriging analysis. Journal of Geochemical Exploration 46: 245-256.
[12] Cao, M., and Lu, L. (2015). Application of the multivariate canonical trend surface method to the identification of geochemical combination anomalies. Journal of Geochemical Exploration 153 (1): 1–10.
[13] Meng, H.D., Song, Y.C., Son, F.Y. and Shen, H.T. (2011). Research and application of cluster and association analysis in geochemical data processing. Computational Geosciences 15: 87–98.
[14] Zaremotlagh, S., Hezarkhani, A. and Sadeghi, M. (2016). Detecting homogenous clusters using whole-rock chemical compositions and REE patterns: A graph-based geochemical approach. Journal of Geochemical Exploration 170: 94–106.
[15] Collyer, P.L. and Merriam, D.F. (1973). An application of cluster analysis in mineral exploration. Mathematical Geosciences 5 (3): 213–223.
[16] Roy, A. (1981). Application of cluster analysis in the interpretation of geochemical data from the Sargipalli lead-zinc mine area, Sundergarh district, Orissa (India). Journal of Geochemical Exploration 14: 245–264.
[17] Ellefsen, K.J. and Smith, D.B. (2016). Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model. Applied Geochemistry 75: 200–210.
[18] Morrison, J.M., Goldhaber, M.B., Ellefsen, K.J. and Mills, C.T. (2011). Cluster analysis of a regional-scale soil geochemical dataset in northern California. Applied Geochemistry 26: S105–S107.
[19] Fatehi, M. and Asadi, H.H. (2017). Application of semi-supervised fuzzy c-means method in clustering multivariate geochemical data, a case study from the Dalli Cu-Au porphyry deposit in central Iran. Ore Geology Reviews 81: 245–255.
[20] Ellefsen, K.J., Smith, D.B. and Horton, J.D. (2014). A modified procedure for mixture-model clustering of regional geochemical data. Applied Geochemistry 51: 315-326.
[21] Aggarwal, C.C. and Reddy, C.K. (2013). Data Clustering: Algorithms and Applications. CRC Press, 652 p.
[22] Han, J., Kamber, M. and Pei, J. (2011). Data mining: concepts and techniques, 3rd Edition. Morgan Kaufmann, 744 p.
[23] Brauer, S. (2014). A Probabilistic Expectation Maximization Algorithm for Multivariate Laplacian Mixtures. MS Thesis of Paderborn University, 78 p.
[24] Fan, J. (2019). OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm. Neural Computing and Applications 31: 2095-2105.
[25] Krishnapuram, R. and Keller, J.M. (1993). A Possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1 (2): 98–110.
[26] Xie, Z., Wang, S. and Chung, F.L. (2008). An enhanced possibilistic C-Means clustering algorithm EPCM. Soft Computing 12: 593–611.
[27] Salgado, P. and Igrejas, G. (2007). Probabilistic Clustering Algorithms for Fuzzy Rules Decomposition. IFAC Proceedings Volumes 40 (21): 115-120.
[28] Celeux, G. and Diebolt, J. (1985). The SEM algorithm: A probabilistic teacher algorithm derived from the EM algorithm for the mixture problem. Computational Statistics Quarterly 2: 73–82.
[29] Quost, B. and Denœux, T. (2016). Clustering and classification of fuzzy data using the fuzzy EM algorithm. Fuzzy Sets and System 286 (1): 134-156.
[30] González, M., Minuesa, C. and Puerto, I. (2016). Maximum likelihood estimation and expectation–maximization algorithm for controlled branching processes. Computational Statistics & Data Analysis 93: 209-227.
[31] Hu, T. and Sung, S.Y. (2006). A hybrid EM approach to spatial clustering. Computational Statistics & Data Analysis 50: 1188–1205.
[32] Kriegel, H.P. and Pfeifle, M. (2005). Density-based clustering of uncertain data. In Proc. of KDD2005, New York, NY, USA, 672–677.
[33] Xu, H. and Li, G. (2008). Density-Based Probabilistic Clustering of Uncertain Data. International Conference on Computer Science and Software Engineering (CSSE 2008), Wuhan, China, 474-477.
[34] Zhang, X., Liu, H., Zhang, X. and Liu, X. (2014). Novel Density-Based Clustering Algorithms for Uncertain Data. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec, Canada, 2191- 2197.
[35] Beckmann, N., Kriegel, H.P., Schneider, R. and Seeger, B. (1990). The R*-tree: an efficient and robust access method for points and rectangles. Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, 322-331.
[36] Erdem, A. and Gűndem, T.I. (2014). M-FDBSCAN: A multicore density-based uncertain data clustering algorithm. Turkish Journal of Electrical Engineering & Computer Sciences 22: 143 – 154.
[37] Halkidi, M., Batistakis, Y. and Vazirgiannis, M. (2002). Clustering validity methods: Part I. ACM SIGMOD Record 31(2): 40-45.
[38] Rendón, E., Abundez, I., Arizmendi, A. and Quiroz, E.M. (2011). Internal versus External cluster validation indexes. International Journal of Computers and Communications 5 (1): 27-34.
[39] Halkidi, M., Batistakis, Y. and Vazirgiannis, M. (2002). Clustering validity checking methods: Part II. ACM SIGMOD Record 31 (3).
[40] Gurrutxaga, I., Albisua, I., Arbelaitz, O., Martın, J.I., Muguerza, J., Pérez, J.M. and Perona, I. (2010). SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index. Pattern Recognition 43: 3364–3373.
[41] Liu, Y., Li, Z., Xiong, H., Gao, X. and Wu, J. (2010). Understanding of Internal Clustering Validation Measures. IEEE International Conference on Data Mining, 911-916.
[42] Bröcker, M., Fotoohi Rad, G., Abbaslu, F. and Rodionov, N. (2014). Geochronology of high-grade metamorphic rocks from the Anjul area Lut block, eastern Iran. Journal of Asian Earth Sciences 82: 151–162.
[43] Mirnejad, H., Blourian, G.H., Kheirkhah, M., Akrami, M.A. and Tutti, F. (2008). Garnet bearing rhyolite from Deh-Salm area, Lut block, Eastern Iran: anatexis of deep crustal rocks. Mineral. Petrol. 94: 259–269.
[44] Asadi, S. and Kolahdani, S. (2014). Tectono-magmatic evolution of the Lut block, eastern Iran: A model for spatial localization of porphyry Cu mineralization. Journal of Novel Applied Sciences 3: 1058-1069.
[45] Mazhari, S.A. and Safari, M. (2013). High-K Calc-alkaline Plutonism in Zouzan, NE of Lut Block, Eastern Iran: An Evidence for Arc Related Magmatism in Cenozoic. Journal Geological Society of India 81: 698-708.
[46] Pang, K.N., Chung, S.L., Zarrinkoub, M.H., Mohammadi, S.S., Yang, H.M., Chu, C. H., Lee, H.Y. and Lo, C.H. (2012). Age, geochemical characteristics and petrogenesis of Late Cenozoic intraplate alkali basalts in the Lut-Sistan region, eastern Iran. Chemical Geology 306–307: 40–53.
[47] Mahmoudi, S., Masoudi, F., Corfu, F. and Mehrabi, B. (2010). Magmatic and metamorphic history of the Deh-Salm metamorphic Complex, Eastern Lut block, (Eastern Iran), from U–Pb geochronology. Int. J. Earth Sci. 99: 1153–1165.
[48] Malekzadeh Shafaroudi, A. and Karimpour, M.H. (2015). Mineralogic, fluid inclusion, and sulfur isotope evidence for the genesis of Sechangi lead–zinc (–copper) deposit, Eastern Iran. Journal of African Earth Sciences 107: 1–14.
[49] Arjmandzadeh, R., Karimpour, M.H., Mazaheri, S.A., Santos, J.F., Medina, J.M. and Homan, S.M. (2011). Two-sided asymmetric subduction; implications for ectonomagmatic and metallogenic evolution of the Lut Block, eastern Iran. Journal of Economic Geology 3 (1): 1-14.
[50] Wilmsen, M., Fürsich, F.T. and Majidifard, M.R. (2013). The Shah Kuh Formation, a latest Barremian e Early Aptian carbonate platform of Central Iran (Khur area, Yazd Block). Cretaceous Research 39: 183-194.
[51] Arjmandzadeh, R. and Santos, J.F. (2014). Sr-Nd isotope geochemistry and tectonomagmatic setting of the Dehsalm Cu-Mo porphyry mineralizing intrusive from Lut Block, estern Iran. Int J Earth Sci (Geo Rundsch) 103: 123-140.
[52] Arjmandzadeh, R., Karimpour, M.H., Mazaheri, S.A., Santos, J.F., Medina, J.M. and Homam, S.M. (2011b). Sr-Nd isotope geochemistry and petrogenesis of Chah-Shaljami granitoids (Lut Block, Eastern Iran). Journal of Asian Earth Science 41: 283-296.
[53] Eshraghi, H., Rastad, E. and Motevali, K. (2010). Auriferous sulfides from Hired gold mineralization, South Birjand, Lut Block, Iran. J Miner Petrol Sci 105: 167-174.
[54] Ghorban, M. (2013). The economic geology of Iran: Mineral Deposits and Natural Resources, Springer Publication, Netherlands, 569p.
[55] Pirajno, F. (2009). Hydrothermal Processes and Mineral Systems, Springer Publication, Australia, 1273 p.
[56] White, W.M. (2013). Geochemistry, Wiley-Blackwell Publications, 668 p.
[57] Santoa, A.P., Jacobsenb, S.B. and Baker, J. (2004). Evolution and genesis of calc-alkaline magmas at Filicudi Volcano, Aeolian Arc (Southern Tyrrhenian Sea, Italy). Lithos 72: 73– 96.
[58] Hawkes, H.E. and Webb, J.S. (1962). Geochemistry in Mineral Exploration. New York: Harper & Row, 415p.
[59] Clark, R.N., Swayze, G.A., Gallagher, A.J., King, T.V.V. and Calvin, W.M. (1993). The U. S. Geological Survey, Digital Spectral Library Version 1: 0.2 to 3.0 μm. U.S. Geological Survey, Open File Report 93-592.
[60] Kruse, F., Lefkoff, A., Boardman, J., Heidebrecht, K., Shapiro, A., Barloon, P. and Goetz, A. (1993). The spectralimage processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment,44: 145-163.
[61] Nabavi, M.H. (1976). An introduction to geology of Iran. Geological Survey of Iran Publication, Tehran, Iran, 110 p. (in Persian).
[62] Stӧcklin, J. (1968). Structural history and tectonics of Iran; a review. The American Association of Petroleum Geologists, Bulletin 52 (7): 1229-1258.
[63] Thompson, M. and Howarth, R.J. (1976). Duplicate analysis in geochemical practice. Part 1: Theoretical approach and estimation of analytical reproducibility. Analyst 101: 690–698.
[64] Zhou, S., Zhou, K., Wang, J., Yang, G. and Wang, S. (2017). Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies. Frontiers of Earth Science 12 (3): 491–505. | ||
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