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Identification of hidden mineralized and non-mineralized zones using spectral analysis of geochemical data | ||
| International Journal of Mining and Geo-Engineering | ||
| دوره 55، شماره 1، شهریور 2021، صفحه 81-89 اصل مقاله (1.12 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijmge.2020.273203.594776 | ||
| نویسندگان | ||
| Hossein Mahdiyanfar* 1؛ Mohammad Farzamian2 | ||
| 1Department of Mining Engineering, University of Gonabad, Gonabad, Iran | ||
| 2Centro de Geofísica, Universidade de Lisboa, Campo Grande Ed. C8, 1749-016 Lisbon, Portugal | ||
| چکیده | ||
| Detection of dispersed and blind mineral deposits is an important aim in the mineral exploration. Detailed exploratory operations such as drilled boreholes which are performed for exploration of mineral deposits in the depth caused high cost and risk. In this research, a new scenario based on spectral analysis of geochemical data has been utilized for prediction of mineralized zones in the depth without any additional cost. The variations of mineralized elements from the surface to the depth are predicted and delineated by using this approach based on surface geochemical data. This proposed approach is the state-of the-art application of two-dimensional Fourier transformation (2DFT) for geochemical image processing. This approach which is named frequency coefficient method (FCM) has been defined based on the behavior of elements in the frequency domain. In this study, the FCM shows two Pb and Zn mineralized zones at the surface and moderate depth and a non-mineralized zone at the profound depth in Chichakloo Pb–Zn mineralization. Finally, the results of FCM have been validated and confirmed by the results of drilled boreholes and geophysical surveys. | ||
| کلیدواژهها | ||
| Dispersed mineralization؛ Deep mineralization؛ Geochemical patterns؛ Geochemical frequency domain؛ Two-dimensional Fourier transformation | ||
| مراجع | ||
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[1] Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). Performance evaluation of RBF-and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of SA multifractal model and mineralization controls. Earth Science Informatics, 1-17.
[2] Parsa, M., Maghsoudi, A., Yousefi, M., & Carranza, E. J. M. (2017). Multifractal interpolation and spectrum–area fractal modeling of stream sediment geochemical data: implications for mapping exploration targets. Journal of African Earth Sciences, 128, 5-15.
[3] Cao, L., Cheng, Q. (2012). Quantification of anisotropic scale invariance of geochemical anomalies associated with Sn-Cu mineralization in Gejiu, Yunan Province, China, Geochemical Exploration 122, 47–54.
[4] Fyzollahhi, N., Torshizian, H., Afzal, P., & Jafari, M. R. (2018). Determination of lithium prospects using fractal modelling and staged factor analysis in Torud region, NE Iran. Journal of Geochemical Exploration, 189, 2-10.
[5] Zuo, R. (2011). Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal the Gangdese Belt, Tibet (China). J. Geochemical Exploration. 111, 13-22.
[6] Zuo, R. (2011). Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Applied Geochemistry 26, S271-S273.
[7] Zuo, R., Carranza, E.J.M. & Cheng, Q. (2012). Fractal/multifractal modelling of geochemical exploration data. Journal of Geochemical Exploration 122, 1-3.
[8] Zuo, R., Xia, Q. & Zhang, D. (2013). A comparison study of the C-A H. Mahdiyanfar & M. Farzamian / Int. J. Min. & Geo-Eng. (IJMGE), 55-1 (2021) 81-89 89 and S-A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry 33, 165-172.
[9] Ghezelbash, R., Maghsoudi, A., & Daviran, M. (2019). Combination of multifractal geostatistical interpolation and spectrum–area (S– A) fractal model for Cu–Au geochemical prospects in Feizabad district, NE Iran. Arabian Journal of Geosciences, 12(5), 152.
[10] Shokouh Saljoughi, B., Hezarkhani, A., & Farahbakhsh, E. (2018). A comparative study of fractal models and U-statistic method to identify geochemical anomalies; case study of Avanj porphyry system, Central Iran. Journal of Mining and Environment, 9(1), 209-227.
[11] Zuo, R., Wang, J. (2015). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration.
[12] Wang, H., Zuo, R. (2015). A comparative study of trend surface analysis and spectrum–area multifractal model to identify geochemical anomalies. Journal of Geochemical Exploration, 155, 84-90.
[13] Wang, J., Zuo, R. (2015). A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Science Informatics, 1-11.
[14] Shahi, H., Ghavami, R., Kamkar Rouhani, A. & Asadi-Haroni, H. (2014). Identification of mineralization features and deep geochemical anomalies using a new FT-PCA approach, journal of Geopersia, 4 (2), 101-110.
[15] Shahi, H., Ghavami Riabi, R., Kamkar Ruhani, A. & Asadi Haroni, H. (2015). Prediction of mineral deposit model and identification of mineralization trend in depth using frequency domain of surface geochemical data in Dalli Cu-Au porphyry deposit. Journal of Mining and Environment, 6(2), pp.225-236.
[16] Shahi, H., Ghavami, R., Kamkar Rouhani, A. & Asadi-Haroni, H. (2015). Application of Fourier and wavelet approaches for identification of geochemical anomalies, Journal of African Earth Sciences 106. 118–128.
[17] Shahi, H., Ghavami, R., & Rouhani, A. K. (2016). Detection of deep and blind mineral deposits using new proposed frequency coefficients method in frequency domain of geochemical data. Journal of Geochemical Exploration.
[18] Roberts, R.G., Sheahan, P., Cherry, M.E. (Eds.), (1988). Ore deposit models: Geoscience Canada Reprint Series 3. Geological Association of Canada, Newfoundland. 200 p.
[19] Cox, D.P., Singer, D.A. (Eds.), (1986). Mineral Deposit Models. U.S. Geological Survey Bulletin 1693. U.S. Government Printing Office, Washington. 379 p.
[20] Carranza, E.J.M, Sadeghi, M. (2012). Primary geochemical characteristics of mineral deposits - Implications for exploration, Ore Geology Reviews 45.1–4.
[21] Grigorian, S.V. (1985). Secondary Lithochemical Halos in Prospecting for Hidden Mineralization. Nedra Publishing House, Moscow.
[22] Grigorian, S.V. (1992). Mining Geochemistry. Nedra Publishing House, Moscow.
[23] Hassani, H., Daya, A., Alinia, F. (2009). Application of a fractal method relating power spectrum and area for separation of geochemical anomalies from background. Aust J Basic Appl Sci, 3(4), 3307-3320 .
[24] Hornak, J. (1997). "The Basic of NMR." http://www.cis.rit.edu/htbooks/nmr/inside.htm.
[25] Dobrin, M. B., Savit, C. H. (1988). Geophysical propecting: McGraw-Hill Book Co., New York, 867 p.
[26] Bhattacharyya, B.K. (1966). Continuous spectrum of the total[1]magnetic-field anomaly due to a rectangular prismatic body. Geophysics, 31(1), 97-121 .
[27] Cheng, Q. (2012). Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. Journal of Geochemical Exploration, 122, 55-70 .
[28] Cheng, Q. (2014). Vertical distribution of elements in regolith over mineral deposits and implications for mapping geochemical weak anomalies in covered areas. Geochemistry: Exploration, Environment, Analysis, 14(3), 277-289.
[29] Farzamian, M., Rouhani, A. K., Yarmohammadi, A., Shahi, H., Sabokbar, H. F., & Ziaiie, M. (2016). A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran. Arabian Journal of Geosciences, 9(2), 104. | ||
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