The separation of geochemical anomalies from background by fractal methods

Cheng, Qiuming; Agterberg, Frits; Ballantyne, S B

doi:10.1016/0375-6742(94)90013-2

Cited by 832 publications

(401 citation statements)

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“…A number of authors have attempted to develop techniques for quantifying anisotropic properties. Fox and Hayes (1985) introduced different scaling exponents for different directions; Agterberg, Cheng, and Wright (1993) applied anisotropic transformation to locations of gold deposits to convert anisotropic to isotropic distributions; Schertzer and Lovejoy (1991) introduced the concept of generalized scale invariance; Lewis and others (1999) proposed a scale invariant generator technique; Cheng, Agterberg, and Ballantyne (1994) developed the C-A method for quantifying anisotropy of geochemical data. In the current paper, a new technique is proposed to quantify anisotropy in the frequency domain.…”

Section: Quantification Of Anisotropic Scale Invariancementioning

confidence: 99%

A New Model for Quantifying Anisotropic Scale Invariance and for Decomposition of Mixing Patterns

Cheng

2004

Mathematical Geology

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A new power-law function has been derived to represent the relationship between area of the set consisting of wave numbers with spectral energy density above S (A(>S)) on the two-dimensional frequency plane and S. The power-law relation holds if the field concerned possessing isotropic scale invariance or generalized scaling invariance involves rotational and ratio-scale changing transforms. The equation is valid for dealing with common exploration geophysical and geochemical fields encountered in mineral exploration and environmental assessment. This power-law function not only provides a new model for characterizing anisotropic scaling invariance for generalized scaling field, for example, estimating the power exponent of power spectrum of generalized scale invariance measure in frequency domain, but also forms a theoretical base for the S-A filtering technique developed for decomposing a mixing field into components on the basis of distinct scaling properties in the frequency domain. It is demonstrated that the method has potential to become a general technique for image processing and pattern recognition.

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Section: Quantification Of Anisotropic Scale Invariancementioning

confidence: 99%

A New Model for Quantifying Anisotropic Scale Invariance and for Decomposition of Mixing Patterns

Cheng

2004

Mathematical Geology

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“…All elemental log-log plots for this area show four distinct geochemical populations based on sudden changes in the rate of volume decrease, as depicted in Fig. 8 and Table 1; in the "enriched" population, the slope of the segments is close to 90 ∘ [16,67]. Using the log-log plots, the furthermost element populations are interpreted as "enriched" zinc-lead zones, having lead and zinc concentrations higher than 19.95% and 6.3%, respectively.…”

Section: C-v Fractal Modelingmentioning

confidence: 84%

“…Bolviken et al [15] and Cheng et al [16] revealed that geochemical patterns of various ore elements have fractal dimensions. 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.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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“…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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The separation of geochemical anomalies from background by fractal methods

Cited by 832 publications

References 20 publications

A New Model for Quantifying Anisotropic Scale Invariance and for Decomposition of Mixing Patterns

A New Model for Quantifying Anisotropic Scale Invariance and for Decomposition of Mixing Patterns

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

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

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