Document Type : Original Research Paper


1 Department of mining engineering, University of Gonabab, Iran

2 Mining Engineering Group, Faculty of Engineering, University of Zanjan, Zanjan, Iran


This work aims to investigate the geochemical signatures of the Cu porphyry deposit in the Dalli area using the geochemical soil samples. At the first step, the geochemical data was opened using the Centered Log-Ratio (CLR) transform method. Then those outlier samples that reduce the accuracy of the geochemical models were detected and removed using the Mahalanobis Distance (MD) method. We applied the Principal Component Analysis (PCA) and Geochemical Mineralization Prospectivity Index (GMPI) methods on the cleaned transformed geochemical dataset. The PCA method identified five principal components (PCs), from which PC1 including Cu, Au, and Mo, are specified as the mineralization factor (MF). The GMPI approach can improve the multivariate geochemical signature in geochemical mapping. Hence, the GMPI values of the samples were calculated based on the score values of MF (Cu, Au, Mo). The results convey that the large values of GMPI (MF) (Cu, Au, Mo) strongly correlate with the quartz diorite porphyry rocks and potassic alteration zones. The GMPI (MF (Cu, Au, Mo)) index was modeled using the Concentration-Number (C-N) fractal method. The C-N fractal model identified four geochemical populations based on the different fractal dimensions. The geochemical anomaly map of GMPI (MF) (Cu, Au, Mo) was delineated using these classified populations. The obtained promising areas were validated adequately by more detailed exploration works and deep drilled boreholes as well. The Cu-Au mineralization potential parts are appropriately mapped by this hybrid method. The results obtained demonstrate that this scenario can be adequately used for geochemical mapping on local scales.


[1]. Carranza, E.J.M. (2008). Geochemical anomaly and mineral prospectivity mapping in GIS. Elsevier.
[2]. Yousefi, M., Carranza, E.J.M., Kreuzer, O.P., Nykänen, V., Hronsky, J.M., and Mihalasky, M.J. (2021). Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: state-of-the-art and outlook. Journal of Geochemical Exploration, 229, 106839.
[3]. Seyedrahimi-Niaraq, M., and Mahdiyanfar, H. (2021). Introducing a new approach of geochemical anomaly intensity index (GAII) for increasing the probability of exploration of shear zone gold mineralization. Geochemistry. 81(4): 125830.
[4]. Yousefi, M. (2017). Analysis of zoning pattern of geochemical indicators for targeting of porphyry-Cu mineralization: a pixel-based mapping approach. Natural Resources Research. 26 (4): 429-441.
[5]. Plant, J.A., and Hale, M. (1994). Introduction: the foundations of modern drainage geochemistry. In Handbook of Exploration Geochemistry (Vol. 6, pp. 3-9). Elsevier Science BV.
[6]. Ghasemzadeh, S., Maghsoudi, A., Yousefi, M., and Mihalasky, M.J. (2019). Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and CA fractal modeling. Ore Geology Reviews, 113, 103115.
[7]. Zuo, R., and Wang, J. (2016). Fractal/multifractal modeling of geochemical data: A review. Journal of Geochemical Exploration, 164, 33-41.
[8]. Salimi, A., and Rafiee, A. (2022). A grid interpolation technique for anomaly separation of stream sediments geochemical data based on catchment basin modelling, U-statistics and fractal. Earth Science Informatics, 15(1), 151-161.
[9]. Yang, L., Wang, Q., and Liu, X. (2015). Correlation between mineralization intensity and fluid–rock reaction in the Xinli gold deposit, Jiaodong Peninsula, China: constraints from petrographic and statistical approaches. Ore Geology Reviews, 71, 29-39.
[10]. 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.
[11]. Chen, Y.Q., Zhao, B.N., Chen, C., Zhao, B.B., and Zhao, P.D. (2022). Identification of ore-finding targets using the anomaly components of ore-forming element associations extracted by SVD and PCA in the Jiaodong gold cluster area, Eastern China. Ore Geology Reviews, 144, 104866.
[12]. Yin, B., Zuo, R., Xiong, Y., Li, Y., and Yang, W. (2021). Knowledge discovery of geochemical patterns from a data-driven perspective. Journal of Geochemical Exploration, 231, 106872.
[13]. Almasi, A., Jafarirad, A., Afzal, P., and Rahimi, M. (2015). Prospecting of gold mineralization in Saqez area (NW Iran) using geochemical, geophysical and geological studies based on multifractal modelling and principal component analysis. Arabian Journal of Geosciences. 8 (8): 5935-5947.
[14]. Chen, Y., Zhang, L., and Zhao, B. (2019). Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt, SW China. Geoscience Frontiers. 10 (4): 1561-1576.
[15]. Cheng, Q., Bonham-Carter, G., Wang, W., Zhang, S., Li, W., and Qinglin, X. (2011). A spatially weighted principal component analysis for multi-element geochemical data for mapping locations of felsic intrusions in the Gejiu mineral district of Yunnan, China. Computers and Geosciences. 37 (5): 662-669.
[16]. Zheng, C., Liu, P., Luo, X., Wen, M., Huang, W., Liu, G., and Albanese, S. (2021). Application of compositional data analysis in geochemical exploration for concealed deposits: A case study of Ashele copper-zinc deposit, Xinjiang, China. Applied Geochemistry, 130, 104997.
[17]. Elghonimy, R., and Sonnenberg, S. (2021). A Principal Component Analysis Approach to Understanding Relationships Between Elemental Geochemistry Data and Deposition, Niobrara Formation, Denver Basin, CO. In SPE/AAPG/SEG Unconventional Resources Technology Conference. OnePetro.
[18]. Shahi, H., Ghavami, R., and 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, 162, 29-39.
[19]. Shahi, H., Ghavami, R., Rouhani, A.K., Kahoo, A.R., and Haroni, H.A. (2015). Application of Fourier and wavelet approaches for identification of geochemical anomalies. Journal of African Earth Sciences, 106, 118-128.
[20]. Shahi, H., Ghavami, R., and Rouhani, A.K. (2016). Comparison of mineralization pattern of geochemical data in spatial and position-scale domain using new DWT-PCA approach. Journal of the Geological Society of India. 88 (2): 235-244.
[21]. Yousefi, M., Kamkar-Rouhani, A., and Carranza, E.J.M. (2012). Geochemical mineralization probability index (GMPI): a new approach to generate enhanced stream sediment geochemical evidential map for increasing probability of success in mineral potential mapping. Journal of Geochemical Exploration, 115, 24-35.
[22]. Afzal, P., Yusefi, M., Mirzaie, M., Ghadiri-Sufi, E., Ghasemzadeh, S., and Daneshvar Saein, L. (2019). Delineation of podiform-type chromite mineralization using geochemical mineralization prospectivity index and staged factor analysis in Balvard area (SE Iran). Journal of Mining and Environment. 10 (3): 705-715.
[23]. Yousefi, M., Kamkar-Rouhani, A., and Carranza, E. J. M. (2014). Application of staged factor analysis and logistic function to create a fuzzy stream sediment geochemical evidence layer for mineral prospectivity mapping. Geochemistry: Exploration, Environment, Analysis. 14 (1): 45-58.
[24]. Yousefi, M., and Carranza, E.J.M. (2015). Fuzzification of continuous-value spatial evidence for mineral prospectivity mapping. Computers and Geosciences, 74, 97-109.
[25]. Yousefi, M., and Carranza, E.J.M. (2017). Union score and fuzzy logic mineral prospectivity mapping using discretized and continuous spatial evidence values. Journal of African Earth Sciences, 128, 47-60.
[26]. Saadati, H., Afzal, P., Torshizian, H., and Solgi, A. (2020). Geochemical exploration for lithium in NE Iran using the geochemical mapping prospectivity index, staged factor analysis, and a fractal model. Geochemistry: Exploration, Environment, Analysis, 20(4), 461-472.
[27]. Cheng, Q. (1999). Spatial and scaling modelling for geochemical anomaly separation. Journal of Geochemical exploration. 65 (3): 175-194.
[28]. Goncalves, M.A., Mateus, A., and Oliveira, V. (2001). Geochemical anomaly separation by multifractal modelling. Journal of Geochemical Exploration. 72 (2): 91-114.
[29]. Sim, B.L., Agterberg, F.P., and Beaudry, C. (1999). Determining the cutoff between background and relative base metal smelter contamination levels using multifractal methods. Computers and Geosciences. 25 (9): 1023-1041.
[30]. Ouchchen, M., Boutaleb, S., Abia, E.H., El Azzab, D., Miftah, A., Dadi, B. and Abioui, M. (2022). Exploration targeting of copper deposits using staged factor analysis, geochemical mineralization prospectivity index, and fractal model (Western Anti-Atlas, Morocco). Ore Geology Reviews, 143, 104762.
[31]. Agterberg, F.P. (1996). Multifractal modelling of the sizes and grades of giant and supergiant deposits. Global tectonics and metallogeny, 131-136.
[32]. Mao, Z., Peng, S., Lai, J., Shao, Y., and Yang, B. (2004). Fractal study of geochemical prospecting data in south area of Fenghuanshan copper deposit, Tongling Anhui. Journal of Earth Sciences and Environment. 26 (4): 11-14.
[33]. Ghannadpour, S.S., and Hezarkhani, A. (2022). Delineation of geochemical anomalies for mineral exploration using combining U-statistic method and fractal technique: UN and UA models. Applied Earth Science. 131 (1): 32-48.
[34]. Cheng, Q., Agterberg, F.P., and Ballantyne, S.B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical exploration. 51 (2): 109-130.
[35]. Khammar, F., Yousefi, S., and Joonaghani, S.A. (2021). Analysis of lithogeochemical data using log-ratio transformations and CA fractal to separate geochemical anomalies in Tak-Talar, Iran. Arabian Journal of Geosciences. 14 (8): 1-15.
[36]. Li, C., Ma, T., and Shi, J. (2003). Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. Journal of Geochemical exploration. 77 (2-3): 167-175.
[37]. Cheng, Q., Xu, Y., and Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research. 9 (1): 43-52.
[38]. Koohzadi, F., Afzal, P., Jahani, D., and Pourkermani, M. (2021). Geochemical exploration for Li in regional scale utilizing Staged Factor Analysis (SFA) and Spectrum-Area (SA) fractal model in north central Iran. Iranian Journal of Earth Sciences. 13(4): 299-307.
[39]. Mahdiyanfar, H. (2020). Prediction of economic potential of deep blind mineralization by Fourier transform of a geochemical dataset. Periodico di Mineralogia. 90 (1).
[40]. Heidari, S.M., Afzal, P., Ghaderi, M., and Sadeghi, B. (2021). Detection of mineralization stages using zonality and multifractal modeling based on geological and geochemical data in the Au-(Cu) intrusion-related Gouzal-Bolagh deposit, NW Iran. Ore Geology Reviews, 139, 104561.
[41]. Afzal, P., Alghalandis, Y.F., Khakzad, A., Moarefvand, P., and Omran, N.R. (2011). Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. Journal of Geochemical exploration. 108 (3): 220-232.
[42]. Afzal, P., Farhadi, S., Boveiri Konari, M., Shamseddin Meigooni, M., and Daneshvar Saein, L. (2022). Geochemical anomaly detection in the Irankuh District using Hybrid Machine learning technique and fractal modeling. Geopersia.
[43]. Farhadi, S., Afzal, P., Boveiri Konari, M., Daneshvar Saein, L., and Sadeghi, B. (2022). Combination of Machine Learning Algorithms with Concentration-Area Fractal Method for Soil Geochemical Anomaly Detection in Sediment-Hosted Irankuh Pb-Zn Deposit, Central Iran. Minerals. 12 (6): 689.
[44]. Hassanpour, S., and Afzal, P. (2013). Application of concentration–number (C–N) multifractal modeling for geochemical anomaly separation in Haftcheshmeh porphyry system, NW Iran. Arabian Journal of Geosciences. 6 (3): 957-970.
[45]. Shahbazi, S., Ghaderi, M., and Afzal, P. (2021). Prognosis of of gold mineralization phases by multifractal modeling in the Zehabad epithermal deposit, NW Iran. Iranian Journal of Earth Sciences. 13 (1): 31-40.
[46]. Leung, R., Balamurali, M., and Melkumyan, A. (2021). Sample truncation strategies for outlier removal in geochemical data: the MCD robust distance approach versus t-SNE ensemble clustering. Mathematical Geosciences. 53 (1): 105-130.
[47]. Garrett, R.G., Reimann, C., Hron, K., Kynčlová, P., and Filzmoser, P. (2017). Finally, a correlation coefficient that tells the geochemical truth. Explore, 176, 1-10.
[48]. Shafiei, B., Haschke, M., and Shahabpour, J. (2009). Recycling of orogenic arc crust triggers porphyry Cu mineralization in Kerman Cenozoic arc rocks, southeastern Iran. Mineralium Deposita. 44 (3): 265-283.
[49]. Waterman, G.C., and Hamilton, R.L. (1975). The Sar Cheshmeh porphyry copper deposit. Economic Geology. 70 (3): 568-576.
[50]. Asadi, H.H., Porwal, A., Fatehi, M., Kianpouryan, S., and Lu, Y.J. (2015). Exploration feature selection applied to hybrid data integration modeling: Targeting copper-gold potential in central Iran. Ore Geology Reviews, 71, 819-838.
[51]. Ayati, F., Yavuz, F., Asadi, H.H., Richards, J.P., and Jourdan, F. (2013). Petrology and geochemistry of calc-alkaline volcanic and subvolcanic rocks, Dalli porphyry copper–gold deposit, Markazi Province, Iran. International Geology Review. 55 (2): 158-184.
[52]. Asadi Haroni, H. (2008). First stage drilling report on Dalli porphyry Cu-Au prospect, Central Province of Iran. Technical of Iran, Isfahan, Report, 1, 24.
[53]. Asadi, H.H. (2008). Final exploration report of Dalli porphyry Cu–Au deposit. Markazi province. Technical Report. Dorsa Pardazeh Company, Isfahan, Report 01.
[54]. Aitchison, J. (1982). The statistical analysis of compositional data. Journal of the Royal Statistical Society: Series B (Methodological). 44 (2): 139-160.
[55]. Carranza, E.J.M. (2011). Analysis and mapping of geochemical anomalies using logratio-transformed stream sediment data with censored values. Journal of Geochemical Exploration. 110 (2): 167-185.
[56]. Graffelman, J., Pawlowsky-Glahn, V., Egozcue, J.J., and Buccianti, A. (2018). Exploration of geochemical data with compositional canonical biplots. Journal of geochemical exploration, 194, 120-133.
[57]. Filzmoser, P., and Hron, K. (2008). Outlier detection for compositional data using robust methods. Mathematical Geosciences. 40 (3): 233-248.
[58]. Owen, D.D.R., Pawlowsky‐Glahn, V., Egozcue, J.J., Buccianti, A., and Bradd, J.M. (2016). Compositional data analysis as a robust tool to delineate hydrochemical facies within and between gas‐bearing aquifers. Water Resources Research. 52 (8): 5771-5793.
[59]. Begashaw, G.B., and Yohannes, Y.B. (2020). Review of outlier detection and identifying using robust regression model. International Journal of Systems Science and Applied Mathematics. 5 (1): 4-11.
[60]. Filzmoser, P., Reimann, C., and Garrett, R. G. (2004). A multivariate outlier detection method (pp. 18-22). na.
[61]. Rousseeuw, P.J., and Van Zomeren, B. C. (1990). Unmasking multivariate outliers and leverage points. Journal of the American Statistical association. 85 (411): 633-639.
[62]. Farzamian, M., Rouhani, A. K., Yarmohammadi, A., Shahi, H., Sabokbar, H. A., and 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): 1-17.
[63]. Mahdiyanfar, H. (2020). A Critique on Power Spectrum–Area Fractal Method for Geochemical Anomaly Mapping. Journal of Analytical and Numerical Methods in Mining Engineering. 10 (25): 33-41.
[64]. Mahdiyanfar, H. (2021). Identification of Buried Metal Ore Deposits using Geochemical Anomaly Filtering and Principal Factors of Power Spectrum. Journal of Mining and Environment. 12 (1): 205-218.
[65]. Seyedrahimi-Niaraq, M., Mahdiyanfar, H., and Mokhtari, A.R. (2022). Integrating principal component analysis and U-statistics for mapping polluted areas in mining districts. Journal of Geochemical Exploration, 234, 106924.
[66]. Afzal, P., Mirzaei, M., Yousefi, M., Adib, A., Khalajmasoumi, M., Zarifi, A. Z. and Yasrebi, A.B. (2016). Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis. Journal of African Earth Sciences, 119, 139-149.