Application of Fourier and wavelet approaches for identification of geochemical anomalies

Shahi, Hossein; Ghavami, Reza; Rouhani, Abolghasem Kamkar; Kahoo, Amin Roshandel; Haroni, Hooshang Asadi

doi:10.1016/j.jafrearsci.2015.03.017

Cited by 19 publications

(4 citation statements)

References 31 publications

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“…Principal components analysis (PCA) is a useful multivariate statistical technique for identifying complex associations, which has been used by geologists to study geochemical anomalies associated with various hydrothermal alteration and mineralization types (Grunsky, 1986(Grunsky, , 2010Shikazono et al, 2008;Zuo, 2011;Wang et al, 2013;Wang et al, 2014;Shahi et al, 2015), metal pollution in sediments (Rubio et al, 2000), and petrology (Ragland et al, 1997). The objective of PCA is to reduce the dimensionality of a large dataset by transforming interrelated variables into new uncorrelated artificial variables (principal components) in decreasing order (Jolliffe, 2002).…”

Section: Indicator Of Mineralizationmentioning

confidence: 99%

Petrogeochemical assessment of rock units and identification of alteration/mineralization indicators using portable X-ray fluorescence measurements: Applications to the Fire Tower Zone (W-Mo-Bi) and the North Zone (Sn-Zn-In), Mount Pleasant deposit, New Brunswick, Canada

Zhang

Lentz

Charnley³

2017

Journal of Geochemical Exploration

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The Mount Pleasant polymetallic deposits are located along the southwestern margin of the Late Devonian Mount Pleasant Caldera Complex in southwestern New Brunswick, Canada. The Fire Tower Zone (W-Mo-Bi) and the North Zone (Sn-Zn-In) comprise the main mineralized zones within the Mount Pleasant deposit. The rock units examined at surface in both zones are highly altered and are surface weathered to some extent. In order to classify these rock units with confidence, the Olympus X-5000 portable X-ray Fluorescence (pXRF) spectrometer was used in this study to rapidly obtain the geochemical composition of these rocks, identify lithologic discriminants, and find alteration/mineralization indicators in these areas. The immobile elements Ti, Zr, Nb, Y, and Th were selected to discriminate among various rock types. The Little Mount Pleasant Formation has the highest Ti and Zr and the Mount Pleasant Granitic Suite (MPGS, GI and GII) contains the highest Y, Nb, and Th. The McDougall Brook Granitic Suite (MBGS) has an intermediate composition between them. The element ratios of Zr/Ti, Nb/Ti, Y/Ti, and Th/Ti effectively discriminate these rock units and increase from the Little Mount Pleasant Formation, to the MBGS, and then to the MPGS. Greisen and propylitic alteration are reflected by strong depletion of K and Rb in some samples. The Fe and Mn typically are leached out of the mineralization system during the greisen alteration. Principal component analysis (PCA) was performed on the pXRF datasets. The first principal component (PC1) of the pXRF data from both the Fire Tower Zone and the North Zone mainly extracts information on Ti, Zr, Ce, Cr, V, Nb, Y, U, and Th, and thus represents different rock units. The PC2 is characterized by high-positive loadings of K, Rb, Fe, and Mn and negative loading of As and Mo. It is interpreted as the W-Mo mineralization indicator showing by the enrichment in Mo and depletion of K, Rb, Fe, and Mn associated with quartz+topaz+sericite+fluorite alteration. The PC3 consists of positive loadings of Sn, Zn, Cu,

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Section: Indicator Of Mineralizationmentioning

confidence: 99%

Petrogeochemical assessment of rock units and identification of alteration/mineralization indicators using portable X-ray fluorescence measurements: Applications to the Fire Tower Zone (W-Mo-Bi) and the North Zone (Sn-Zn-In), Mount Pleasant deposit, New Brunswick, Canada

Zhang

Lentz

Charnley³

2017

Journal of Geochemical Exploration

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“…Multivariate signatures in mining and environmental geochemistry are not considered by applying simple fractal models. PCA has been applied for discerning geochemical patterns in the spatial, frequency, and Wavelet domains in mining geochemistry (Yousefi et al 2012;Shahi et al, 2015Farzamian et al, 2016;Mahdiyanfar, 2020;Behera and Panigrahi, 2021;Seyedrahimi-Niaraq and Mahdiyanfar, 2021). PCA can be used to reduce the dimensions of the environmental dataset and identify the heavy and toxic elements related to the pollution process.…”

Section: Introductionmentioning

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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“…In the simplest case, it can be said that the threshold is the upper limit of the background so that the values above this limit are the anomaly, and the lower values are the background. However, in most cases, two or more threshold values may be detected [20][21][22][23][24][25] . In geochemical investigations, anomalous values can be related to mineralized rocks.…”

Section: Introductionmentioning

confidence: 99%

Comparison of U-spatial statistics method with Classical Statistics results in the determination of geochemical anomalies of Epithermal Gold in Khoshnameh area, Hashtjin, Iran

Seyedrahimi-Niaraq

2021

j. of geol. res.

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In this study, methods based on the distribution model (with and without personal opinion) were used for the separation of anomalous zones, which include two different methods of U-spatial statistics and mean plus values of standard deviation (). The primary purpose is to compare the results of these methods with each other. To increase the accuracy of comparison, regional geochemical data were used where occurrences and mineralization zones of epithermal gold have been introduced. The study area is part of the Hashtjin geological map, which is structurally part of the folded and thrust belt and part of the Alborz Tertiary magmatic complex. Samples were taken from secondary lithogeochemical environments. Au element data concerning epithermal gold reserves were used to investigate the efficacy of these two methods. In the U- spatial statistics method, and criteria were used to determine the threshold, and in the method, the element enrichment index of the region rock units was obtained with grouping these units. The anomalous areas were identified by, and criteria. Comparison of methods was made considering the position of discovered occurrences and the occurrences obtained from these methods, the flexibility of the methods in separating the anomalous zones, and the two-dimensional spatial correlation of the three elements As, Pb, and Ag with Au element. The ability of two methods to identify potential areas is acceptable. Among these methods, it seems the method with criteria has a high degree of flexibility in separating anomalous regions in the case of epithermal type gold deposits.

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Application of Fourier and wavelet approaches for identification of geochemical anomalies

Cited by 19 publications

References 31 publications

Petrogeochemical assessment of rock units and identification of alteration/mineralization indicators using portable X-ray fluorescence measurements: Applications to the Fire Tower Zone (W-Mo-Bi) and the North Zone (Sn-Zn-In), Mount Pleasant deposit, New Brunswick, Canada

Petrogeochemical assessment of rock units and identification of alteration/mineralization indicators using portable X-ray fluorescence measurements: Applications to the Fire Tower Zone (W-Mo-Bi) and the North Zone (Sn-Zn-In), Mount Pleasant deposit, New Brunswick, Canada

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

Comparison of U-spatial statistics method with Classical Statistics results in the determination of geochemical anomalies of Epithermal Gold in Khoshnameh area, Hashtjin, Iran

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