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Combination of multifractal geostatistical interpolation and spectrum–area (S–A) fractal model for Cu–Au geochemical prospects in Feizabad district, NE Iran

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Abstract

Exploration geochemical surveys seek to delimit exploration targets through the analyses of geochemical exploration data. Different methods have been applied in the delineation of geochemical anomalies including frequency-based and frequency space–based methods. The success of the latter methods depends on the modeling of the spatial distribution of geochemical data. However, selection of an appropriate method for modeling the spatial distribution of geochemical data remains a challenge. The main objective of this study is to address the foregoing challenge through a comparative study of inverse distance weighting (IDW), ordinary Kriging (OK), multifractal IDW (MIDW), and multifractal Kriging (MK) surface interpolation techniques. Initially, a set of composite sediment geochemical data from Feizabad district, NE Iran, was subjected to multivariate geochemical analysis by which a multielement geochemical signature, representing Cu–Au-related mineralization, was derived. Four above-mentioned interpolation techniques were applied to model the spatial distribution of the derived geochemical signature. The effectiveness of four interpolated models was compared by success-rate curves through which the MK model was recognized to be the superior model. The spectrum–area (S–A) fractal model was then applied on the MK model to decompose the anomalous component. The t student spatial statistics method was employed to determine proper threshold values by which the anomalous component could be discretized. The resultant crisp model was considered as the map of exploration targets.

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References

  • Afzal P, Khakzad A, Moarefvand P, Omran NR, Esfandiari B, Alghalandis YF (2010) Geochemical anomaly separation by multifractal modeling in Kahang (Gor Gor) porphyry system, Central Iran. J Geochem Explor 104:34–46

    Article  Google Scholar 

  • Afzal P, Alghalandis YF, Khakzad A, Moarefvand P, Omran NR (2011) Delineation of mineralization zones in porphyry Cu deposits by fractal concentration–volume modeling. J Geochem Explor 108:220–232

    Article  Google Scholar 

  • Afzal P, Alghalandis YF, Moarefvand P, Omran NR, Haroni HA (2012) Application of power-spectrum–volume fractal method for detecting hypogene, supergene enrichment, leached and barren zones in Kahang Cu porphyry deposit, Central Iran. J Geochem Explor 112:131–138

    Article  Google Scholar 

  • Afzal P, Harati H, Fadakar Alghalandis Y, Yasrebi AB (2013) Application of spectrumearea fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit, Iran. Chem Erde 73: 533-543

    Article  Google Scholar 

  • Agterberg FP, Bonham-Carter GF (2005) Measuring the performance of mineral-potential maps. Nat Resour Res 14:1–17

    Article  Google Scholar 

  • Agterberg FP, Cheng Q, Brown A, Good D (1996) Multifractal modeling of fractures in the Lac du Bonnet batholith, Manitoba. Comput Geosci 22:497–507

    Article  Google Scholar 

  • Bai J, Porwal A, Hart C, Ford A, Yu L (2010) Mapping geochemical singularity using multifractal analysis: application to anomaly definition on stream sediments data from Funin Sheet, Yunnan, China. J Geochem Explor 104(1):1–11

    Article  Google Scholar 

  • Behroozi A (1987) Geological map of Iran 1: 100,000 series, Feizabad. Geological Survey of Iran, Tehran

    Google Scholar 

  • Bonham-Carter GF, Agterberg FP, Wright DF (1990) Weights of evidence modelling: a new approach to mapping mineral potential: geological survey of Canada 89:171–183

    Google Scholar 

  • Carranza EJM, Hale M (1997) A catchment basin approach to the analysis of reconnaissance geochemical-geological data from Albay Province, Philippines. J Geochem Explor 60:157–171

    Article  Google Scholar 

  • Carranza EJM (2004) Usefulness of stream order to detect stream sediment geochemical anomalies. Geochem Explor Environ Anal 4:341–352

    Article  Google Scholar 

  • Carranza E J M (2008) Geochemical anomaly and mineral prospectivity mapping in GIS (Vol. 11). Elsevier

  • Cheng QM (2001) Multifractal and geostatistic methods for characterizing local structure and singularity properties of exploration geochemical anomalies. J China Univ Geosci 26:161–166

    Google Scholar 

  • Cheng Q (1999) Multifractal interpolation. In: Proceedings of the Fifth Annual Conference of the International Association for Mathematical Geology, Trondheim, Norway, vol 1, pp 245–250

    Google Scholar 

  • Cheng Q (2000) Interpolation by means of multiftractal, kriging and moving average techniques. In GAC/MAC meeting of GeoCanada2000 Calgary

  • Cheng Q (2007) Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province, China. Ore Geol Rev 32:314–324

    Article  Google Scholar 

  • 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.), Proceedings of the Fifth Annual Conference of the International Association for Mathematical Geology, Trondheim, Norway 6 e11th August. 11, pp 87-92

  • Cheng Q, Xu Y, Grunsky E (2000) Integrated spatial and spectrum method for geochemical anomaly separation. Nat Resour Res 9:43-52

  • Cheng Q, Agterberg FP, Ballantyne SB (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51:109–130

    Article  Google Scholar 

  • Cheng Q, Agterberg FP, Bonham-Carter GF (1996) A spatial analysis method for geochemical anomaly separation. J Geochem Explor 56:183–195

    Article  Google Scholar 

  • Cheng Q, Bonham-Carter G, Wang W, Zhang S, Li W, 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. Comput Geosci 37:662–669

    Article  Google Scholar 

  • Cheng Q, Xia Q, Li W, Zhang S, Chen Z, Zuo R, Wang W (2010) Density/area power-law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China. Biogeosciences 7:3019–3025

    Article  Google Scholar 

  • Davis CJ (2002) Statistics and data analysis geology, 3th edn. John Wiley & Sons Inc, New York, pp 342–353

    Google Scholar 

  • Ghezelbash R, Maghsoudi A (2018a) Comparison of U-spatial statistics and C-A fractal models for delineating anomaly patterns of porphyry-type Cu geochemical signatures in the Varzaghan district, NW Iran. C R Geosci 350:180–191

    Article  Google Scholar 

  • Ghezelbash R, Maghsoudi A (2018b) A hybrid AHP-VIKOR approach for prospectivity modeling of porphyry Cu deposits in the Varzaghan District, NW Iran. Arab J Geosci 11:275

  • Ghezelbash R, Maghsoudi A (2018c) Application of hybrid AHP-TOPSIS method for prospectivity modeling of Cu porphyry in Varzaghan district, Iran. ULUM-I ZAMIN (In Persion) 28:33-42. https://doi.org/10.22071/gsj.2017.86299.1107

  • Ghezelbash R, Maghsoudi A, Daviran M (2018) Prospectivity modeling of porphyry copper deposits: recognition of efficient mono-and multi-element geochemical signatures in the Varzaghan district, NW Iran. Acta Geochim 1-14

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019a) Mapping of single-and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models. J Geochem Explor 199:90-104

    Article  Google Scholar 

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019b) An Improved Data-Driven Multiple Criteria Decision-Making Procedure for Spatial Modeling of Mineral Prospectivity: Adaption of Prediction–Area Plot and Logistic Functions. Nat Resour Res. https://doi.org/10.1007/s11053-018-9448-6

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019c) Performance evaluation of RBF-and SVM-based machine learning algorithms for predictive mineral prospectivity modeling: integration of SA multifractal model and mineralization controls. Earth Sci Inf. https://doi.org/10.1007/s12145-018-00377-6

  • Hengl T (2006) Finding the right pixel size. Comput Geosci 32:1283–1298

    Article  Google Scholar 

  • Hu D, Liu D, Xue S (1995) Explanatory text of geochemical map of Feizabad (7760). Geological Survey of Iran, Tehran

    Google Scholar 

  • Hu S, Cheng Q, Wang L, Xu D (2013) Modeling land price distribution using multifractal IDW interpolation and fractal filtering method. Landscape Urban Plan 110:25–35

    Article  Google Scholar 

  • Jolliffe IT (2002) Principal component analysis, second ed. Springer, New York, 547 NY, 487pp

  • Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic press

  • Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20:141–151

    Article  Google Scholar 

  • Li C, Ma T, Shi J (2003) Application of a fractal method relating concentrations and distances for separation of 386 geochemical anomalies from background. J Geochem Explor 77:167–175

    Article  Google Scholar 

  • Li Q (2005) Multifractal-krige interpolation method. Adv Earth Sci 20:248–255

    Google Scholar 

  • Lima A, De Vivo B, Cicchella D, Cortini M, Albanese S (2003) Multifractal IDW interpolation and fractal filtering method in environmental studies: an application on regional stream sediments of (Italy), Campania region. Appl Geochem 18:1853–1865

    Article  Google Scholar 

  • Lima A, Plant JA, De Vivo B, Tarvainen T, Albanese S, Cicchella D (2008) Interpolation methods for geochemical maps: a comparative study using arsenic data from European stream waters. Geochem Explor Env 8:41–48

    Article  Google Scholar 

  • Lin YP (2002) Multivariate geostatistical methods to identify and map spatial variations of soil heavy metals. Environ Geol 42:1–10

    Article  Google Scholar 

  • Macklin MG, Ridgway J, Passmore DG, Rumsby BT (1994) The use of overbank sediment for geochemical mapping and contamination assessment: results from selected welsh flood plains. Appl Geochem 9:698–700

    Article  Google Scholar 

  • Mandelbrot BB, Pignoni R (1983) The fractal geometry of nature, vol 173. WH freeman, New York

    Google Scholar 

  • Muller J, Kylander M, Martinez-Cortizas A, Wüst RA, Weiss D, Blake K, Garcia-Sanchez R (2008) The use of principle component analyses in characterising trace and major elemental distribution in a 55kyr peat deposit in tropical Australia: implications to paleoclimate. Geochim Cosmochim Acta 72:449–463

    Article  Google Scholar 

  • Oliver MA, Webster R (1990) Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4:313–332

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Yousefi M (2017c) An improved data-driven fuzzy mineral prospectivity mapping procedure; cosine amplitude-based similarity approach to delineate exploration targets. Int J Appl Earth Obs Geoinf 58:157–167

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Ghezelbash R (2016c) Decomposition of anomaly patterns of multi-element geochemical signatures in Ahar area, NW Iran: a comparison of U-spatial statistics and fractal models. Arab J Geosci 9:260

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Yousefi M, Carranza EJM (2017a) Multifractal interpolation and spectrum–area fractal 404 modeling of stream sediment geochemical data: implications for mapping exploration targets. J Afr Earth Sci 128:5–15

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2017b) Multifractal analysis of stream sediment geochemical data: implications for hydrothermal nickel prospection in an arid terrain, eastern Iran. J Geochem Explor 181:305–317

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2016a) Prospectivity modeling of porphyry-Cu deposits by identification and integration of efficient mono-elemental geochemical signatures. J Afr Earth Sci 114:228–241

    Article  Google Scholar 

  • Parsa M, Maghsoudi A, Yousefi M, Sadeghi M (2016b) Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran. J Geochem Explor 165:111–124

    Article  Google Scholar 

  • Shuguang Z, Kefa Z, Yao C, Jinlin W, Jianli D (2015) Exploratory data analysis and singularity mapping in geochemical anomaly identification in Karamay, Xinjiang, China. J Geochem Explor 154:171–179

    Article  Google Scholar 

  • Sinclair AJ (1974) Selection of threshold values in geochemical data using probability graphs. J Geochem Explor 3:129–149

    Article  Google Scholar 

  • Sinclair AJ (1976) Applications of probability graphs in mineral exploration (no. 4). In: Association of Exploration Geochemists

    Google Scholar 

  • Sinclair AJ (1991) A fundamental approach to threshold estimation in exploration geochemistry: probability plots revisited. J Geochem Explor 41:1–22

    Article  Google Scholar 

  • Spadoni M (2006) Geochemical mapping using a geomorphologic approach based on catchments. J Geochem Explor 90:183–196

    Article  Google Scholar 

  • Spadoni M, Voltaggio M, Cavarretta G (2005) Recognition of areas of anomalous concentration of potentially hazardous elements by means of a subcatchmentbased discriminant analysis of stream sediments. J Geochem Explor 87:83–91

    Article  Google Scholar 

  • Stanley CR, Sinclair AJ (1989) Comparison of probability plots and the gap statistic in the selection of thresholds for exploration geochemistry data. J Geochem Explor 32:355–357

    Article  Google Scholar 

  • Wang J, Zuo R (2015) A MATLAB-based program for processing geochemical data using fractal/multifractal modeling. Earth Sci Inf 8:937–947

    Article  Google Scholar 

  • Wang W, Zhao J, Cheng Q, Liu J (2012) Tectonicegeochemical exploration modeling for characterizing geo-anomalies in southeastern Yunnan district, China. J Geochem Explor 122:71–80

    Article  Google Scholar 

  • Webster R, Oliver MA (2007) Characterizing spatial processes: the covariance and variogram. Geostatistics for Environmental Scientists, Second Edition:47–76

  • Xie S, Cheng Q, Xing X, Bao Z, Chen Z (2010) Geochemical multifractal distribution patterns in sediments from ordered streams. Geoderma 160:36–46

    Article  Google Scholar 

  • Yousefi M, Carranza EJM, Kamkar-Rouhani A (2013) Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling. J Geochem Explor 128:88–96

    Article  Google Scholar 

  • Yousefi M, Nykänen V (2016) Data-driven logistic-based weighting of geochemical and geological evidence layers in mineral prospectivity mapping. J Geochem Explor 164:94–106

    Article  Google Scholar 

  • Yuan F, Li X, Zhou T, Deng Y, Zhang D, Xu C, Zhang R, Jia C, Jowitt SM (2015) Multifractal modelling-based mapping and identification of geochemical anomalies associated with Cu and Au mineralisation in the NW Junggar area of northern Xinjiang Province, China. J Geochem Explor 154:252–264

    Article  Google Scholar 

  • Zhang C, Tang Y, Xu X, Kiely G (2011) Towards spatial geochemical modelling: use of geographically weighted regression for mapping soil organic carbon contents in Ireland. Appl Geochem 26:1239–1248

    Article  Google Scholar 

  • Zhang Y, Zhou YZ, Wang LF, Wang ZH, He JG, An YF, Li HZ, Zeng CY, Liang J, Lü WC, Gao L (2013) Mineralization-related geochemical anomalies derived from stream sediment geochemical data using multifractal analysis in Pangxidong area of Qinzhou-Hangzhou tectonic joint belt, Guangdong Province, China. J Central South Univ 20:184–192

    Article  Google Scholar 

  • Zhong X, Kealy A, Duckham M (2016) Stream kriging: incremental and recursive ordinary kriging over spatiotemporal data streams. Comput Geosci 90:134–143

    Article  Google Scholar 

  • Zuo R, Wang J (2016) Fractal/multifractal modeling of geochemical data: a review. J Geochem Explor 164:33–41

    Article  Google Scholar 

  • Zuo R (2011a) Decomposing of mixed pattern of arsenic using fractal model in Gangdese belt, Tibet, China. Appl Geochem 26:S271–S273

    Article  Google Scholar 

  • Zuo R (2011b) Identifying geochemical anomalies associated with Cu and Pb–Zn skarn mineralization using principal component analysis and spectrum–area fractal modeling in the Gangdese Belt, Tibet (China). J Geochem Explor 111:13–22

    Article  Google Scholar 

  • Zuo R, Carranza EJM, Wang J (2016) Spatial analysis and visualization of exploration geochemical data. Earth Sci Rev 158:9–18

    Article  Google Scholar 

  • Zuo R, Cheng Q, Agterberg FP, Xia Q (2009) Application of singularity mapping technique to identify local anomalies using stream sediment geochemical data, a case study from Gangdese, Tibet, western China. J Geochem Explor 101:225–235

    Article  Google Scholar 

  • Zuo R, Wang J (2015) Fractal/multifractal modeling of geochemical data: a review. J Geochem Explor 164:33–41

    Article  Google Scholar 

  • Zuo R, Xia Q, Wang H (2013) Compositional data analysis in the study of integrated geochemical anomalies associated with mineralization. Appl Geochem 28:202–211

    Article  Google Scholar 

  • Zuo R, Zhang Z, Zhang D, Carranza EJM, Wang H (2015) Evaluation of uncertainty in mineral prospectivity mapping due to missing evidence: a case study with skarn type Fe deposits in Southwestern Fujian Province, China. Ore Geol Rev 71:502–515

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Mining and Metallurgical Engineering, Amirkabir University of Technology, Tehran, Iran

    Reza Ghezelbash & Abbas Maghsoudi

  2. School of Mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    Mehrdad Daviran

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  1. Reza Ghezelbash
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  2. Abbas Maghsoudi
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Correspondence to Abbas Maghsoudi.

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Editorial handling: Shifeng Dai

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Ghezelbash, R., Maghsoudi, A. & Daviran, M. Combination of multifractal geostatistical interpolation and spectrum–area (S–A) fractal model for Cu–Au geochemical prospects in Feizabad district, NE Iran. Arab J Geosci 12, 152 (2019). https://doi.org/10.1007/s12517-019-4318-z

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