Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background

Li, Changjiang; Ma, Tuhua; Shi, Jingli

doi:10.1016/s0375-6742(02)00276-5

Cited by 279 publications

(107 citation statements)

References 12 publications

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“…One of the most off use fractal modeling types is ConcentrationArea (C-A) fractal modeling developed by Cheng et al [11] and Li et al [12]. In this study, anomaly delineation and geochemical behavior of gold has been studied in Alut district, Iran.…”

Section: Introductionmentioning

confidence: 99%

Geochemical Studies for Gold in Alut Anomaly District

Agharezaei¹,

Hezarkhani²

2017

IJSEA

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Abstract:The target in this research is detecting Gold anomalies in Alut district. This area is located in western Iran. The area mostly consists of metamorphic rocks extended along geological structures. Geochemical anomaly detection for gold begins with sampling in the study area. The samples are obtained from drainages of the area thus the samples are surface soil specimens. These samples are analyzed by ICP-MS. Then statistical and non-statistical methods are applied to define anomalies in the area. Finally anomaly maps are generated.

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Section: Introductionmentioning

confidence: 99%

Geochemical Studies for Gold in Alut Anomaly District

Agharezaei¹,

Hezarkhani²

2017

IJSEA

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“…Stream sediment and lithogeochemical studies are essential for prospecting of different ore deposits (Hawkes & Webb, 1979). Several methods are used for geochemical data interpretation and modelling such as classical statistics (e.g., Tukey, 1977;Hawkes & Webb, 1979;Reimann et al, 2005), fractal and multifractal modelling (Cheng et al, 1994;Agterberg et al, 1996;Cheng, 1999;Li et al, 2003;Zuo et al, 2009;Afzal et al, 2010a;Afzal et al, 2010b) and singularity modeling (Cheng, 2007). Fractal theory has been established by Mandelbrot (1983) as an important non-Euclidean branch in geometry.…”

Section: Introductionmentioning

confidence: 99%

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

Mansouri¹,

Feizi²

2016

Archives of Mining Sciences

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The studied area -Chartagh -is located in the East of Azerbaijan gharbi Province, Iran. In this paper, geology map, ASTER satellite images were used and after processing these images with ENVI softwares, geochemical data analysis consisting of lithogeochemical samples, within geological field observations. On ASTER data; using a number of selected methods including band ratio, Minimum Noise Fraction (MNF) and Spectral Angle Maper (SAM) distinguished alternation zones. Geochemical anomalies were separated by number -size (N-S) fractal method. (N-S) fractal method was utilized for High intensive Au, As and Ag anomalies.

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“…Since that time, several fractal models have been developed and applied to geochemical exploration; these models work by separating geochemical populations into component mineralized zone and phase populations. Examples of fractal models include the number-size (N-S) model proposed by Mandelbrot [14], the concentration-area (C-A) model of Cheng et al [16], the concentration-perimeter (C-P) model of Cheng et al [17], the concentration-distance (C-D) model proposed by Li et al [18], and the concentrationvolume (C-V) model of Afzal et al [19]. Fractal/multifractal modeling assists in identifying relationships between geological, geochemical, and mineralogical settings and linking them to spatial information obtained from mineral deposits [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

et al. 2016

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Abstract:The aim of this paper is to delineate the different lead-zinc mineralized zones in the Zardu area of the Kushk zinc-lead stratabound SEDEX deposit, Central Iran, through concentration-volume (C-V) modeling of geological and lithogeochemical drillcore data. The geological model demonstrated that the massive sulfide and pyrite+dolomite ore types as main rock types hosting mineralization. The C-V fractal modeling used lead, zinc and iron geochemical data to outline four types of mineralized zones, which were then compared to the mineralized rock types identified in the geological model. 'Enriched' mineralized zones contain lead and zinc values higher than 6.93% and 19.95%, respectively, with iron values lower than 12.02%. Areas where lead and zinc values were higher than 1.58% and 5.88%, respectively, and iron grades lower than 22% are labelled "high-grade" mineralized zones, and these zones are linked to massive sulfide and pyrite+dolomite lithologies of the geological model. Weakly mineralized zones, labelled 'low-grade' in the C-V model have 0-0.63% lead, 0-3.16% zinc and > 30.19% iron, and are correlated to those lithological units labeled as gangue in the geological model, including shales and dolomites, pyritized dolomites. Finally, a log-ratio matrix was employed to validate the results obtained and check correlations between the geological and fractal modeling. Using this method, a high overall accuracy (OA) was confirmed for the correlation between the enriched and highgrade mineralized zones and two lithological units -the massive sulfide and pyrite+dolomite ore types.

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Application of a fractal method relating concentrations and distances for separation of geochemical anomalies from background

Cited by 279 publications

References 12 publications

Geochemical Studies for Gold in Alut Anomaly District

Geochemical Studies for Gold in Alut Anomaly District

Introducing Au Potential Areas, Using Remote Sensing and Geochemical Data Processing Using Fractal Methods in Chartagh, Western Azerbijan – Iran

Identification of mineralized zones in the Zardu area, Kushk SEDEX deposit (Central Iran), based on geological and multifractal modeling

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