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Using fractal and multifractal methods to reveal geophysical anomalies in Sardouyeh District, Kerman, Iran

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Abstract

In recent decades, airborne geophysical technologies have gained popularity in mineral prospecting, especially at regional scales with limited rock outcrops and intricate geological conditions dominating the region, that impose errors to the obtained results. To address this, incorporation of fractal/multifractal methodologies into geophysical data will result in more certain outcomes. In this research, content-area (C-A), multifractal inverse distance weighting (MIDW) and singularity index (SI) methods were applied on airborne magnetometric and radiometric data to discover favorable copper mineralization areas in Sardouyeh district, SE Iran. Based on success-rate curves, MIDW and C-A fractal methods had far better performance in highlighting the main anomalies than ordinary IDW. However, they could not separate local anomalies. In contrast, the singularity mapping as a windows-based technique provided more optimal results, which, in addition to the main anomalies, could highlight the weak and local geophysical anomalies dominated by the strong background. Accordingly, the newly revealed anomalies using singularity technique are strongly correlated to the known copper deposits and geological evidences in the region. Moreover, to more accurately determine the geophysical anomalies spatially linked to Cu deposits and generate 2-class (favorable/non-favorable) singularity maps, the normalized density index was implemented. The results demonstrated that α values of 1.96 and 1.94 are optimal thresholds for mapping singularity values of magnetometric and radiometric anomalies, respectively. Therefore, the favorable classes derived by normalized density index are suggested to be used for tracing undiscovered Cu deposits in further exploration stages.

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References

  • Abedi M, Torabi SA, Norouzi GH, Hamzeh M (2012) ELECTRE III: A knowledge-driven method for integration of geophysical data with geological and geochemical data in mineral prospectivity mapping. J Appl Geophys 87:9–18

    Google Scholar 

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

    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(5):497–507

    Google Scholar 

  • Agterberg FP (2012) Multifractals and geostatistics. J Geochem Explor 122:113–122

    Google Scholar 

  • Akbari S, Ramazi H, Ghezelbash R, Maghsoudi A (2020) Geoelectrical integrated models for determining the geometry of karstic cavities in the Zarrinabad area, west of Iran: combination of fuzzy logic, CA fractal model and hybrid AHP-TOPSIS procedure. Carbonates Evaporites 35:1–16

    Google Scholar 

  • Ali MY, Fairhead JD, Green CM, Noufal A (2017) Basement structure of the United Arab Emirates derived from an analysis of regional gravity and aeromagnetic database. Tectonophysics 712:503–522

    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

    Google Scholar 

  • Baranov V (1957) A new method for interpretation of aeromagnetic maps: pseudogravimetric anomalies. Geophysics 22:359–383

    Google Scholar 

  • Baranov V, Naudy H (1964) Numerical calculation of the formula of reduction to the magnetic pole. Geophysics 29:67–79

    Google Scholar 

  • Bartier PM, Keller CP (1996) Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Comput Geosci 22(7):795–799

    Google Scholar 

  • Betts PG, Valenta RK, Finlay J (2003) Evolution of the Mount Woods Inlier, northern Gawler Craton, Southern Australia: an integrated structural and aeromagnetic analysis. Tectonophysics 366(1):83–111. https://doi.org/10.1016/S0040-1951(03)00062-3

    Article  Google Scholar 

  • Bonham-Carter GF (1994) Geographical information systems for geoscientists: modeling with GIS. Comput Methods Geosci 13

  • Carranza EJM, Zuo R, Cheng Q (2012) Fractal/multifractal modelling of geochemical exploration data. J Geochem Explor 122:1–3

    Google Scholar 

  • Carranza EJM (2008) Geochemical anomaly and mineral prospectivity mapping in GIS (vol. 11). Elsevier

  • Carranza EJM (2009) Controls on mineral deposit occurrence inferred from analysis of their spatial pattern and spatial association with geological features. Ore Geol Rev 35(3):383–400

    Google Scholar 

  • Chen X, Xu R, Zheng Y, Jiang X, Du W (2018) Identifying potential Au-Pb-Ag mineralization in SE Shuangkoushan, North Qaidam, Western China: combined log-ratio approach and singularity mapping. J Geochem Explor 189:109–121

    Google Scholar 

  • 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(1–2):314–324

    Google Scholar 

  • Cheng Q (2008) Modeling local scaling properties for multiscale mapping. Vadose Zone J 7(2):525–532

    Google Scholar 

  • Cheng Q (2012) Singularity theory and methods for mapping geochemical anomalies caused by buried sources and for predicting undiscovered mineral deposits in covered areas. J Geochem Explor 122:55–70

    Google Scholar 

  • Cheng Q, Agterberg FP (2009) Singularity analysis of ore-mineral and toxic trace elements in stream sediments. Comput Geosci 35(2):234–244

    Google Scholar 

  • Cheng Q (1999) Spatial and scaling modelling for geochemical anomaly separation. J Geochem Explor 65(3):175–194

    Google Scholar 

  • Cheng Q, Agterberg FP, Ballantyne SB (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51(2):109–130

    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(10):3019–3025

    Google Scholar 

  • Cheng Q, Xu Y, Grunsky E (2000) Integrated spatial and spectrum method for geochemical anomaly separation. Nat Resour Res 9(1):43–52

    Google Scholar 

  • Cheng WL (2001) Spatio-temporal variations of sulphur dioxide patterns with wind conditions in central Taiwan. Environ Monit Assess 66(1):77–98

    Google Scholar 

  • Cooper GRJ (2003) Feature detection using sun shading. Comput Geosci 29:941–948

    Google Scholar 

  • Cordell L, Grauch VJS (1985) Mapping basement magnetization zones from aeromagnetic data in the San Juan Basin, New Mexico. In The utility of regional gravity and magnetic anomaly maps (pp. 181–197). Society of Exploration Geophysicists

  • Daviran M, Maghsoudi A, Ghezelbash R, Pradhan B (2021) A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach. Comput Geosci 148:104688

    Google Scholar 

  • Edwards DJ, Lyatsky HV, Brown RJ (1998) Regional interpretation of steep faults in the Alberta Basin from public-domain gravity and magnetic data: an update. Can Soc Explor Geophys 23(1):15–24

    Google Scholar 

  • Fedi M, Florio G (2001) Detection of potential fields source boundaries by enhanced horizontal derivative method. Geophys Prospect 49(1):40–58

    Google Scholar 

  • Ferdows MS, Ramazi H (2015a) Application of the fractal method to determine the membership function parameter for geoelectrical data (case study: Hamyj copper deposit, Iran). J Geophys Eng 12(6):909–921

    Google Scholar 

  • Ferdows MS, Ramazi H (2016) Performing the power spectrum-area method to separate anomaly from background for induced polarization data:(a case study; Hamyj copper deposit, Iran). Arab J Geosci 9(10):1–8

    Google Scholar 

  • Ferdows MS, Ramazi HR (2015b) Application of the singularity mapping technique to identify local anomalies by polarization data (a case study: Hamyj Copper Deposit, Iran). Acta Geod Geoph 50(3):365–374

    Google Scholar 

  • Forson ED, Menyeh A, Wemegah DD (2021) Mapping lithological units, structural lineaments and alteration zones in the Southern Kibi-Winneba belt of Ghana using integrated geophysical and remote sensing datasets. Ore Geol Rev 137:104271

    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. Compt Rendus Geosci 350(4):180–191

    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(11):275

    Google Scholar 

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019a) 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 28(4):1299–1316

    Google Scholar 

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2019b) 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 12(3):277–293

    Google Scholar 

  • Ghezelbash R, Maghsoudi A, Carranza EJM (2020) Sensitivity analysis of prospectivity modeling to evidence maps: Enhancing success of targeting for epithermal gold, Takab district, NW Iran. Ore Geol Rev 120:103394

    Google Scholar 

  • Ghezelbash R, Maghsoudi A, Daviran M (2019c) 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(5):1–14

    Google Scholar 

  • Ghezelbash R, Maghsoudi A, Shamekhi M, Pradhan B, Daviran M (2022) Genetic algorithm to optimize the SVM and K-means algorithms for mapping of mineral prospectivity. Neural Comput Applic 1–15

  • Ghezelbash R, Daviran M, Maghsoudi A, Ghaeminejad H, Niknezhad M (2023) Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran. Appl Geochem 148:105538

    Google Scholar 

  • Gonçalves BF, Sampaio EE (2013) Interpretation of airborne and ground magnetic and gamma-ray spectrometry data in prospecting for base metals in the central-north part of the Itabuna-Salvador-Curaçá Block, Bahia, Brazil Interpretation of Mag-Gama data. Interpretation 1(1):T85–T100

    Google Scholar 

  • Goncalves MA, Mateus A, Oliveira V (2001) Geochemical anomaly separation by multifractal modelling. J Geochem Explor 72(2):91–114

    Google Scholar 

  • Guan ZN (2005) Geomagnetic field and magnetic exploration. Geological Publishing House, Beijing

    Google Scholar 

  • Gunn PJ, Dentith MC (1997) Magnetic responses associated with mineral deposits. J Aust Geol Geophys 17:145–158

    Google Scholar 

  • Guo S, Lu YH, Zhang L, Zhang H (2008) Study on Fuzhou land price gradient field based on GIS. J Lanzhou Univ 44:33–38 ((in Chinese with English abstract))

    Google Scholar 

  • José-Ma M, Beatriz L (2006) Estimating housing price: Kriging the mean. Int Adv Econ Res 12:419

    Google Scholar 

  • Karim A, Mohamed H (2008) Regional-scale aeromagnetic survey of the south-west of Algeria: A tool for area selection for diamond exploration. J Afr Earth Sci 50:67–78

    Google Scholar 

  • Kalantari M, Ghezelbash S, Ghezelbash R, Yaghmaei B (2020) Developing a fractal model for spatial mapping of crime hotspots. Eur J Crim Policy Res 26:571–591

    Google Scholar 

  • Li C, Ma T, Shi J (2003) Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background. J Geochem Explor 77(2–3):167–175

    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(12):1853–1865

    Google Scholar 

  • Liu Y, Cheng Q, Xia Q, Wang X (2013) Application of singularity analysis for mineral potential identification using geochemical data—A case study: Nanling W-Sn–Mo polymetallic metallogenic belt, South China. J Geochem Explor 134:61–72

    Google Scholar 

  • Liu Y, Xia Q, Cheng Q (2021) Aeromagnetic and geochemical signatures in the Chinese Western Tianshan: Implications for tectonic setting and mineral exploration. Nat Resour Res 30(5):3165–3195

    Google Scholar 

  • Mandelbrot BB (1983) The fractal geometry of nature (vol. 173). Macmillan

  • Miller HG, Singh V (1994) Potential field tilt—a new concept for location of potential field sources. J Appl Geophys 32(2–3):213–217

    Google Scholar 

  • Rodriguez-Galiano V, Sanchez-Castillo M, Chica-Olmo M, Chica-Rivas MJOGR (2015) Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geol Rev 71:804–818

    Google Scholar 

  • Sabzehie M, Afrooz A (1990) An analysis of lead and zinc mineralization in Dehaj-Sarduiye volcanic belt

  • Akbari S, Ramazi H (2023) Application of AHP -SWOT and geophysical methods to develop a reasonable planning for Zagheh tourist destination considering environmental criteria. Int J Environ Sci 8:11–56

    Google Scholar 

  • Sun C, Liu G, Xue S (2016) Natural succession of grassland on the Loess Plateau of China affects multifractal characteristics of soil particle-size distribution and soil nutrients. Ecol Res 31(6):891–902

    Google Scholar 

  • Sun T, Li H, Wu K, Chen F, Zhu Z, Hu Z (2020) Data-driven predictive modelling of mineral prospectivity using machine learning and deep learning methods: a case study from southern Jiangxi Province, China. Minerals 10(2):102

    Google Scholar 

  • Sun X, Barros AP (2010) An evaluation of the statistics of rainfall extremes in rain gauge observations, and satellite-based and reanalysis products using universal multifractals. J Hydrometeorol 11(2):388–404

    Google Scholar 

  • Telford WM, Geldart LP, Sheriff RE, Keys DA (1976) Applied Geophysics. Cambridge University Press

    Google Scholar 

  • Uwiduhaye JDA, Mizunaga H, Saibi H (2018) Geophysical investigation using gravity data in Kinigi geothermal field, northwest Rwanda. J Afr Earth Sc 139:184–192

    Google Scholar 

  • Valenta RK, Jessell MW, Jung G, Bartlett J (1992) Geophysical interpretation and modelling of three-dimensional structure in the Duchess area, Mount Isa, Australia. Explor Geophys 23(2):393–400

    Google Scholar 

  • Verduzco B, Fairhead JD, Green CM, MacKenzie C (2004) New insights into magnetic derivatives for structural mapping. Lead Edge 23(2):116–119

    Google Scholar 

  • Wang F, Liao GP, Zhou XY, Shi W (2013) Multifractal detrended cross-correlation analysis for power markets. Nonlinear Dyn 72(1):353–363

    Google Scholar 

  • Wang J, Meng X (2019) An aeromagnetic investigation of the Dapai deposit in Fujian Province, South China: Structural and mining implications. Ore Geol Rev 112:103061

    Google Scholar 

  • Wang J, Zuo R (2018) Identification of geochemical anomalies through combined sequential Gaussian simulation and grid-based local singularity analysis. Comput Geosci 118:52–64

    Google Scholar 

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

    Google Scholar 

  • Wang Z, Zuo R (2022) Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network. Comput Geosci 158:104974

    Google Scholar 

  • Wijns C, Perez C, Kowalczyk P (2005) Theta map: Edge detection in magnetic data. Geophysics 70(4):L39–L43

    Google Scholar 

  • William JH, Ralph RBVF, Saad AH (2013) Gravity and Magnetic Exploration: Principles. Cambridge University Press, New York, Practices and Applications

    Google Scholar 

  • Wu YZ (2005) GIS-based exploratory data analysis on the spatial–temporal evolvement of urban housing price and its application. Ph.D. dissertation thesis. Zhejiang Univ. pp 150 (in Chinese with English abstract)

  • Xiao F, Chen J, Zhang Z, Wang C, Wu G, Agterberg FP (2012) Singularity mapping and spatially weighted principal component analysis to identify geochemical anomalies associated with Ag and Pb-Zn polymetallic mineralization in Northwest Zhejiang, China. J Geochem Explor 122:90–100

    Google Scholar 

  • Yang N, Zhang Z, Yang J, Hong Z, Shi J (2021) A convolutional neural network of GoogLeNet applied in mineral prospectivity prediction based on multi-source geoinformation. Nat Resour Res 30(6):3905–3923

    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

    Google Scholar 

  • Zhang P, Du J, Wang Z, Yang M, Chen C (2022) Extraction, evaluation and replacement techniques of long wavelength components from compiled regional aeromagnetic anomaly data. Chin J Geophys 7:2595–2612

    Google Scholar 

  • Zhao J, Chen S, Zuo R (2016) Identifying geochemical anomalies associated with Au–Cu mineralization using multifractal and artificial neural network models in the Ningqiang district, Shaanxi, China. J Geochem Explor 164:54–64

    Google Scholar 

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

    Google Scholar 

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Acknowledgements

The authors would like to thank Prof. Babaie, the Editor-in-chief, for handling this manuscript. We also thank the anonymous reviewer for his/her constructive comments. Moreover, we want to thank the Geological Survey of Iran (GSI) for providing the geophysical database used in this paper.

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The authors did not receive support from any organization for the submitted work.

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All authors contributed to the study conception and design. Data collection and modeling were performed by Sarina Akbari, Hamidreza Ramazi and Reza Ghezelbash. The first draft of the manuscript was written by Sarina Akbari. All authors read and approved the final manuscript.

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Correspondence to Hamidreza Ramazi.

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Akbari, S., Ramazi, H. & Ghezelbash, R. Using fractal and multifractal methods to reveal geophysical anomalies in Sardouyeh District, Kerman, Iran. Earth Sci Inform 16, 2125–2142 (2023). https://doi.org/10.1007/s12145-023-01016-5

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