Magnetic and IP/Res data inversion for investigation of the spatial relation between the geophysical models and mineralization in the southern Dalli Cu-Au porphyry deposit

Document Type : Research Article

Authors

1 M.Sc. Student, Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

2 Assistant Professor, Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran

Abstract

Because of declining high-grade ore deposits and increasing demands for metal resources, exploration of low-grade metal deposits, such as porphyries, have become feasible. Besides, humankind has spent most of the shallow metal ore deposits, and new prospecting projects focus on deeper deposits. Therefore, geophysical methods have gained more attention due to their ability to determine buried ore bodies' physical properties. Hence, most countries, including Iran, make significant investments in the geophysical exploration of deep porphyry deposits. According to widely accepted Lowell and Guilbert's model for porphyry copper deposits, the ore-bearing zones mainly concentrate at the edge of the potassic alteration zone. Pyrite, a highly conductive and chargeable metallic mineral, is a significant attribute in the potassic alteration. The model also states that the high susceptible magnetite-bearing rocks mainly occur at the bottom of the pyrite shell and the ore body. Due to the occurrence and presence of susceptible and conductive metallic minerals such as magnetite and pyrite in the potassic zone near to the ore body in the copper and gold porphyry deposits, the use of magnetometry, resistivity, and inducing polarization methods give reliable information about the location, depth, and shape of the deposits. For instance, in this research, we focus on the magnetic and IP/Res data in the southern Dalli porphyry deposit, with promising Cu-Au indices, which is located at Euromieh-Dokhtar ore-bearing zone Markazi Province. First, we applied standard processing techniques to remove the aliasing and regional effect in the magnetic data. Then, using the analytic signal technique, we showed the concentration of the magnetic sources over the study area. We also applied the power spectrum and Euler deconvolution techniques to the magnetic data and estimated the magnetic sources' depths. The estimated depth from the power spectrum is between the estimated depth from Euler deconvolution for possible sources with step and pillar shapes. Next, we used the average estimated depth from each of the depth estimation techniques in a three-dimensional magnetic data inversion as the depth of the sources in depth weighting. Also, we studied the inversion results via combining the cross-section of the magnetic susceptibility model along the boreholes and the lithology and geochemical information from core samples analysis. The results indicate that the higher grades for gold and copper occur at the edge of the magnetic sources and possible magnetite mineralization zones. The inversion results using the depth weighting with the depth extracted from the power spectrum show the best correlation and spatial relation with the geochemical data. Besides the magnetic data inversion, applying Oldenburg and Li algorithms for two-dimensional inverse modeling, we extracted the underground bodies' resistivity and chargeability model along with a IP/Res profile in the study area. The resulting chargeability models show a significant relationship with the presence of gold and copper mineralization. We also compared the resulting two-dimensional resistivity and changeability models with their corresponding magnetic susceptibility at the cross-sections along with the IP/Res. The comparison shows that the possible mineralization zones coincide with larger magnetic susceptibility values, high chargeability and low resistivity. The results show good accordance with Lowell and Guilbert's model. Also, highly susceptible rock in the shallower depth indicates that the erosion process has destroyed most possible orebody.

Keywords

Main Subjects


اسدی هارونی، ه. و سن سلیمانی، ع.، 1390، مطالعات مرحله پی جویی کانسار مس-طلا پورفیری دالی در استان مرکزی، فصلنامه زمین و منابع، 4(2)، 16-9.
آزاد، م.، 1394، کاربرد فیلتر گسترش رو به بالا در تفسیر داده‎های میدان مغناطیس به‌همراه تعیین ارتفاع بهینه در منطقه منصورآباد یزد، ایران، مجله فیزیک زمین و فضا، 41(2)، 238-229.
فاتحی، م. و اسدی هارونی، ه.، 1397، ویژگی‌‎های ژئوفیزیکی کانسارهای مس پورفیری غنی از طلا: مطالعه موردی در کانسار مس-طلای پورفیری دالی، استان مرکزی، فصلنامه زمین‎شناسی اقتصادی، 10(2)، 675-639.
قاسمیان‌نیا، ر. و اسکویی، ب.، 1396، تخمین عمق، مکان و هندسه بی‎هنجاری‎های مغناطیسی به روش عدد موج محلی بهبودیافته، مجله فیزیک زمین و فضا، 43(1)، 131-115.
Asadi, H., Porwal, A., Fatehi, M., Kianpouryan, S. and Lu, Y., 2014, Exploration feature selection applied to hybrid data integration modeling : Targeting copper-gold potential in central Iran Exploration feature selection applied to hybrid data integration modelin : Targeting copper-gold potential in central Iran, Ore Geology Reviews, 71, 819–838.
Ahmadi, R. and Rezapour, M. R., 2020, Proposing the optimum locations for drilling in Saveh North-Narbaghi‎ porphyry copper deposit on the basis of geophysical data modeling, Scientific Quarterly Journal of Iranian Association of Engineering Geology, 12(4), 95-121.
Abedi, M., Babaei, M., Norouzih, G. and Kazem Alilou, S., 2021, 3D inverse modeling of electrical resistivity and induced polarization data versus geostatistical-based modeling. Geopersia.
Byrne, K., Lesage, G., Morris, W.A., Enkin, R.J., Gleeson, S.A. and Lee, R.G., 2019, Variability of outcrop magnetic susceptibility and its relationship to the porphyry Cu centers in the Highland Valley Copper district, Ore geology reviews, 107, 201-217.
Bemani, M., Mojtahedzade, S. H. and Ansari. A., 2019, Investigation And Adaptation Of Geophysical Data With Alteration Zones Of Aliabad Damak Copper Deposit, Journal of Mineral Resources Engineering, 4(1), 21-43.
Babaei, M., Abedi, M., Norouzi, G. H. and Kazem Alilou, S., 2020, Geostatistical modeling of electrical resistivity tomography for imaging porphyry Cu mineralization in Takht-e-Gonbad deposit, Iran, Journal of Mining and Environment, 11(1), 143-159.
Cella, F. and Fedi, M., 2012, Inversion of potential field data using the structural index as weighting function rate decay, Geophysical Prospecting, 60(2), 313-336.
Clark, D. A., 2014, Magnetic effects of hydrothermal alteration in porphyry copper and iron-oxide copper–gold systems: A review, Tectonophysics, 624, 46-65.
Chai, T. and Draxler, R. R., 2014, Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature, Geoscientific model development, 7(3), 1247-1250.
Coleman, C. and Li, Y., 2018, Quantifying the error level in computed magnetic amplitude data for 3D magnetization inversion, Geophysics, 83(5), J75-J84.
Ellis, R. G. and Oldenburg, D. W., 1994, Applied geophysical inversion, Geophysical Journal International, 116(1), 5-11.
Essa, K. S., Mehanee, S. and Elhussein, M., 2021, Magnetic Data Profiles Interpretation for Mineralized Buried Structures Identification Applying the Variance Analysis Method, Pure and Applied Geophysics, 178(3), 973-993.
Fedi, M. and Quarta, T., 1998, Wavelet analysis for the regional‐residual and local separation of potential field anomalies [Link]. Geophysical prospecting, 46(5), 507-525.
FitzGerald, D., Reid, A. and McInerney, P., 2004, New discrimination techniques for Euler deconvolution, Computer and Geoscience, 30(5), 461-469.
Hansen, R. O. and Suciu, L., 2002, Multiple-source Euler deconvolution, Geophysics, 67(2), 525-535.
Holden, E. J., Fu, S. C., Kovesi, P., Dentith, M., Bourne, B. and Hope, M., 2011, Automatic identification of responses from porphyry intrusive systems within magnetic data using image analysis, Journal of Applied Geophysics, 74(4), 255-262.
Harimei, B., 2019, Analysis of Regional Anomaly on Magnetic Data Using the Upward Continuation Method, IOP Conference Series: Earth and Environmental Science, 279(1), 012037.
Kumar, R., Bansal, A. R. and Ghods, A., 2020, Estimation of depth to bottom of magnetic sources using spectral methods: Application on Iran's aeromagnetic data, Journal of Geophysical Research: Solid Earth, 125(3), e2019JB018119.
Lowell, J. D. and Guilbert, J. M., 1970, Lateral and vertical alteration-mineralization zoning in porphyry ore deposits, Economic Geology, 65(4), 373–408.
Li, Y. and Oldenburg, D. W., 1996, 3-D inversion of magnetic data, Geophysics, 61(2), 394–408.
Li, Y. and Oldenburg, D. W., 2003, Fast inversion of large-scale magnetic data using wavelet transforms and a logarithmic barrier method, Geophysical Journal International, 152(2), 251 – 265.
Li, W., Lu, W., Qian, J. and Li, Y., 2017, A multiple level-set method for 3D inversion of magnetic data, Geophysics, 82(5), J61-J81.
Melo, A. T., Sun, J. and Li, Y., 2017, Geophysical inversions applied to 3D geology characterization of an iron oxide copper-gold deposit in Brazil, Geophysics, 82(5), K1-K13.
Mostafaei, K. and Ramazi, H. R., 2018, 3D model construction of induced polarization and resistivity data with quantifying uncertainties using geostatistical methods and drilling (Case study: Madan Bozorg, Iran), Journal of Mining and Environment, 9(4), 857–872.
Oldenburg, D. W. and Li, Y., 1994, Inversion of induced polarization data, Geophysics, 59(9), 1327-1341.
Oldenburg, D. W., Li, Y. and Ellis, R. G., 1997, Inversion of geophysical data over a copper gold porphyry deposit: A case history for Mt. Milligan, Geophysics, 62(5), 1419-1431.
Oldenburg, D. W. and Li, Y., 2005, Inversion for applied geophysics: A tutorial, Near-surface geophysics, 89-150.
Oldenburg, D. W. and Pratt, D. A., 2007, Geophysical Inversion for Mineral Exploration: a Decade of Progress in Theory and Practice, Fifth Decennial International Conference on Mineral Exploration, 61–95.
Okwesili, N. A., Chiebonam, E. K. and Awucha, I. E., 2019, Euler Deconvolution and Source Parameter Imaging of aeromagnetic data of Guzabure and Gudumbali regions, Chad Basin, North Eastern Nigeria, IOSR Journal of Applied Physics (IOSR-JAP), 11(3), 01-10.
Reid, A. B., Allsop, J. M., Granser, H., Millett, A. J. and Somerton, I. W., 1990, Magnetic interpretation in 3-D using euler deconvolution, Geophysics, 55(1), 80-91.
Spector, A. and Grant, F. S., 1970, Statistical models for interpreting aeromagnetic data, Geophysics, 35(2), 293-302.
Sillitoe, R., 2010, Porphyry Copper Systems, Economic Geology, 105(1), 3-41.
Tripp, A. C., Hohmann, G.W. and Swift Jr, C.M., 1984, Two-dimensional resistivity inversion, Geophysics, 49(10), 1708-1717.
Vogel, A., Gorenflo, R., Kummer, B., Ofoegbu, C. O., Ursin, B., Inversion, G. D., Sarwar, A. K. and Kounchev, O. I., 1988, Theory and Practice of Applied Geophysics, F. Vieweg.
Vallée, M. A., Byrne, K., King, J. J., Lee, R. G., Lesage, G., Farquharson, C. G., Chouteau, M. and Enkin, R. J., 2020, Imaging porphyry copper alteration using aeromagnetic data at Highland Valley Copper, British Columbia, Canada, Exploration Geophysics, 51(3), 388-400.
Wondimu, H. D., Mammo, T. and Webster, B., 2018, 3D joint inversion of Gradient and Mise-à-la-Masse borehole IP/Resistivity data and its application to magmatic sulfide mineral deposit exploration, Acta Geophysica, 66(5), 1031-1045.
Yuval and Oldenburg, D. W., 1997, Computation of Cole-Cole parameters from IP data, Geophysics, 62(2), 436-448.
Zhdanov, M. and Portniaguine, O., 2002, 3-D magnetic inversion with data compression and image focusing, Geophysics, 67(5), 1532–1541.