Comparison of Mineralization Pattern of Geochemical Data in Spatial and Position-scale Domain Using New DWT- PCA Approach

Shahi, Hossein; Ghavami, Reza; Rouhani, Abolghasem Kamkar

doi:10.1007/s12594-016-0482-x

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…Different PS-based filter functions were designed to separate the geochemical classes detected by the fractal plot. The PS-based filter functions only filter some PS values and ignore the frequencies and wavelength values (Mahdiyanfar, 2021;Shahi et al, 2016). were filtered and removed from data using newly designed filter function that preserves the PS values between 0.083 and 271082.…”

Section: Discussionmentioning

confidence: 99%

Fractal modeling of multivariate principal component of contaminating toxic elements in Irankooh Pb-Zn mining area

Mahdiyanfar

Seyedrahimi-Niaraq

2022

Preprint

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The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population which has not been affected or is less affected by these activities. Five geochemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geochemical populations were achieved using geochemical filtering and two-dimensional inverse Fourier transformation. The geochemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geochemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements of Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations.

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Section: Discussionmentioning

confidence: 99%

Fractal modeling of multivariate principal component of contaminating toxic elements in Irankooh Pb-Zn mining area

Mahdiyanfar

Seyedrahimi-Niaraq

2022

Preprint

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Evidential belief functions modeling of geophysical and multi-element geochemical data for Pb-Zn mineral potential targeting

Farzamian

Mahdiyanfar²,

Rouhani³

2022

Journal of African Earth Sciences

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Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF)

Seyedrahimi-Niaraq

Mahdiyanfar

2022

GEEA

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Geochemical anomaly mapping is a significant method in exploring for Au and associated elements using fine-fraction sediments. The fractal behaviour of geochemical elements has frequently been modelled for geochemical prospectivity mapping. In this study, concentration–area (C-A) and power spectrum–area (S-A) fractal modelling were utilized for Au concentration in stream sediments near the Saqqez mineralization area, NW Iran. A novel integrated approach based on the S-A fractal method and principal component analysis (PCA) was used to improve the geochemical anomaly map. In this approach, the multi-element mineralization factor returned from PCA was modelled by the S-A fractal method, and the obtained results were compared to the C-A and S-A fractal models of the element Au. Six known mineralization zones in the studied area were revisited to validate the fractal models. The elements Au, As, W, and Sb were identified as associated elements based on the log-ratio-transformed data in the shear zone gold mineralization. S-A fractal modelling of the multi-element mineralization factor (SAF-MF) of the Au-associated elements in the mineralization zones enhanced the geochemical halos and increased the prediction rate of mineralization zones using stream sediment geochemical data. The SAF-MF that models the behaviour of frequency signals of associated elements upgraded the geochemical anomaly mapping compared to conventional fractal models. Supplementary material: Supplementary information including figures S1–S5 is available at https://doi.org/10.6084/m9.figshare.c.6123537

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Comparison of Mineralization Pattern of Geochemical Data in Spatial and Position-scale Domain Using New DWT- PCA Approach

Cited by 3 publications

References 31 publications

Fractal modeling of multivariate principal component of contaminating toxic elements in Irankooh Pb-Zn mining area

Fractal modeling of multivariate principal component of contaminating toxic elements in Irankooh Pb-Zn mining area

Evidential belief functions modeling of geophysical and multi-element geochemical data for Pb-Zn mineral potential targeting

Improvement of geochemical prospectivity mapping using power spectrum–area fractal modelling of the multi-element mineralization factor (SAF-MF)

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