Identification of anomalies using multivariate fractal modeling in the Maleksiahkuh region, SE Iran

Document Type : Research Article

Authors

1 Dept. of Mining Engineering, University of Sistan and Baluchestan, Zahedan, Iran

2 Dept. of Geology, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

Anomaly separation based on stream sediment data is an important step for mineral exploration. In this article, three methods of cluster analysis, factor analysis and fractal geometry have been used to separate the anomalous and suspected mineralization areas from the background areas. By combining these three methods, a possible mineralization was found in the Maleksiahkuh area. In addition, the relationship between the anomalies and the anomaly's host rocks was discussed. Maleksiahkuh is located 35 kilometers north of Zahedan and in the eastern part of the Flysch zone, Iran. Multivariate statistical analysis was performed. The results show a positive correlation between copper and molybdenum. The amount of chromium from the field is relatively high. Chromium is rich in the host mafic rocks. The presence of large concentrations of chromium in the region can be attributed to the presence of mafic rocks. The highest positive correlation was observed between manganese and cobalt, which is about 0.997. In addition, iron with titanium has a correlation of 0.984. Cobalt with iron has a correlation of 0.975. The cluster analysis for the region confirmed the existence of three clusters. The third cluster containing elements As, Sr, Sn, Sb, Pb, Cu, and Ag is probably related to the base-metal mineralization. Factor analysis was performed on the elemental concentrations. The sixth factor, which Cu and Ag elements have the highest weightage, was considered as another mineralization factor. The location of the most concentrated copper in the map derived from the Number-size (N-S) fractal method corresponds to the highest score in the factor rating map. There is a good match between copper anomalies and mafic rocks. Green crests have always been associated with mineralization, and studies show that there is a good relationship between mineralization and these rocks.

Keywords

Main Subjects

References

[1]       Hassanipak AA, Sharafodin M (2011)  Exploratory data analysis. Tehran University Press, Third edition. (In Persian)
[2]       Templ M, Filzmoser P, Reimann C (2008) Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 23 (8), 2198-2213
[3]       Akbarpour A, Gholami N, Azizi H, Torab FM (2012) Cluster and R-mode factor analyses on soil geochemical data of Masjed-Daghi exploration area, northwestern Iran. Arabian Journal of Geosciences, 6 (9), 3397-3408
[4]       Morrison  JM, Goldhaber MB, Ellefsen KJ, Mills CT (2011) Cluster analysis of a regional scale soil geochemical dataset in northern California. Applied Geochemistry, 26S105-S107
[5]       Praveena S, Ahmed A, Radojevic M, Abdullah MH, Aris A (2007) Factor-cluster analysis and enrichment study of mangrove sediments-an example from Mengkabong, Sabah. Malaysian J.Anal.Sci, 11 (2), 421-430
[6]       Reimann C, Filzmoser P, Garrett RG (2002) Factor analysis applied to regional geochemical data: problems and possibilities. Applied Geochemistry, 17 (3), 185-206
[7]       Cheng QM, Agterberg FP, Ballantyne SB (1994) The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration 51, 109–130
[8]       Cheng Q, Agterberg FP, Bonham-Carter GF (1996) A spatial analysis method for geochemical anomaly separation, Journal of Geochemical Exploration, 56, 183-195
[9]       Cheng Q, Xu Y, Grunsky E (1999) Integrated spatial and spectral analysis for geochemical anomaly separation. In: Lippard S. J., Naess A., Sinding-Larsen R., (Eds.): Proc. of the Fifth Annual Conference of the International Association for Mathematical Geology, Trondheim, Norway, 6-11th August 1999, Vol. 1, 87–92
[10]    Cheng Q, Xu Y, Grunsky E (2000) Multifractal power spectrum-area method for geochemical anomaly separation, Natural Resources Research, 9, 43-51
[11]    Afzal P, Khakzad A, Moarefvand P, RashidnejadOmran N, Esfandiari B, FadakarAlghalandis Y (2010) Geochemical anomaly separation by multifractal modeling in Kahang (GorGor) porphyry system, Central Iran. J. Geochem. Explor. 104, 34–46
[12]    Afzal P, FadakarAlghalandis Y, Khakzad A, Moarefvand P, RashidnejadOmran N (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J. Geochem. Explor. 108, 220–232
[13]    Chen G, & Cheng Q (2018) Fractal-based wavelet filter for separating geophysical or geochemical anomalies from background. Mathematical Geosciences, 50(3), 249–272
[14]    Sim BL, Agterberg FP, Beaudry C (1999). Determining the cutoff between background and relative base metal contamination levels using multifractal methods. Comput. Geosci. 25, 1023–1041
[15]    Goncalves MA, Mateus A, Oliveira V(2001) Geochemical anomaly separation by multifractal modeling. Journal of Geochemical Exploration. 72, 91–114
[16]    Zhou S, Zhou K, Wang J et al (2018) Application of cluster analysis to geochemical compositional data for identifying ore-related geochemical anomalies. Front. Earth Sci. 12, 491–505 (2018). https://doi.org/10.1007/s11707-017-0682-8
[17]    Heidari SM, Ghaderi M, Afzal P (2013)  Delineating mineralized phases based on lithogeochemical data using multifractal model in Touzlar epithermal Au-Ag (Cu) deposit, NW Iran. Appl. Geochem., 31, 119–132
[18]    Daya AA (2015a) 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
[19]    Ghorbani M (2013) The economic Geology of Iran Mineral Deposits and Natural Resources, Springer, 569 pp
[20]    Daya AA (2015b) Application of concentration–area method for separating geochemical anomalies from background: a case study of Shorabhaji region, northwest of Iran. Arabian journal of geosciences. 8:3905–3913
[21]    Daya AA, Afzal P (2015) Comparative study of concentration-area (C-A) and spectrum-area (S-A) fractal models for separating geochemical anomalies in Shorabhaji region, NW Iran. Arabian journal of geosciences 8:8263–8275
[22]    Daya AA, Boomeri M, Mazraee, N (2017) Identification of Geochemical Anomalies by Using of Concentration-Area (C-A) Fractal Model in Nakhilab Region, SE Iran. Journal of mining and mineral engineering. 8, 70-81
[23]    Tirrul R, Bell IR, Griffis RJ and Camp VE (1983) The sistan Suture zone of eastern Iran. Geological society .of American . Bulliten. V .94, 134 -150
[24]    Eftekharnejad J (1981) Tectonic division of Iran with respect to sedimentary basins. J Iranian Petrol Soc 82: 19–28 (in Persian)
[25]    Camp VE, Griffis RJ (1982) Character, genesis and tectonic setting of igneous rocks in the Sistan Suture Zone, eastern Iran. Lithos 15, 221-239
[26]    Boomeri M, Moradi R, Bagheri S, Stein H (2019) Geology, Re-Os age, 34S and 18O isotopic composition of the Lar Cu-Mo porphyry deposit, southeast Iran. Ore Geology Reviews 104, 477-494
[27]    Gou B, Wang C, Yu T et al (2020) Fuzzy logic and grey clustering analysis hybrid intelligence model applied to candidate-well selection for hydraulic fracturing in hydrocarbon reservoir. Arab J Geosci 13, 975. https://doi.org/10.1007/s12517-020-05970-y
[28]    Karimpour MH, Saadat S (2002) Applied Economic Geology, Ferdowsi University, Mashhad, 535 pp
[29]    Madani N, Sadeghi B (2019) Capturing Hidden Geochemical Anomalies in Scarce Data by Fractal Analysis and Stochastic Modelling’. Natural Resources Research, 28(3), 833–847
[30]    Mandelbrot, BB (1983) The Fractal Geometry of Nature. Freeman, San Francisco. 468 pp
[31]    S Hassanpour, P Afzal (2013) Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arabian Journal of Geosciences 6, 957-970
[32]    P Afzal, YF Alghalandis, P Moarefvand, NR Omran, HA Haroni (2012) application of power-spectrum–volume fractal method for detecting hypogene, supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran.
Journal of Geochemical Exploration 112, 131-138
[33]    S Paravarzar, Z Mokhtari, P Afzal, F Aliyari (2023) Application of an approximate geostatistical simulation algorithm to delineate the gold mineralized zones characterized by fractal methodology. Journal of African Earth Sciences 200, 104865
[34]    B Behbahani, H Harati, P Afzal, M Lotfi (2023) Determination of alteration zones applying fractal modeling and Spectral Feature Fitting (SFF) method in Saryazd porphyry copper system, central Iran. Bulletin of the Mineral Research and Exploration, 1-1
[35]    P Afzal, H Gholami, N Madani, AB Yasrebi, B Sadeghi (2023) Mineral Resource Classification Using Geostatistical and Fractal Simulation in the Masjed Daghi Cu–Mo Porphyry Deposit, NW Iran. Minerals 13 (3), 370.

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History

  • Receive Date: 24 June 2023
  • Revise Date: 07 January 2024
  • Accept Date: 02 March 2024

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