A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran

Farzamian, Mohammad; Rouhani, A. Kamkar; Yarmohammadi, Ali; Shahi, Hossein; Sabokbar, H A Faraji; Ziaiie, M.

doi:10.1007/s12517-015-2202-z

Cited by 8 publications

(2 citation statements)

References 52 publications

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“…Recently, there has been a growing interest in the geological big data mining through the use of some novel computational intelligent methods-for example, rough set [37] and fuzzy aggregation [38]. Moreover, with the development of those neural network based machine learning algorithms in recent years, some popular methods, including extreme learning machine [39,40], approximate dynamic programming [41], and kernel learning [42], could be used to further improve mining effectiveness for geological big data in the future.…”

Section: Geological Big Data Miningmentioning

confidence: 99%

Big Data Management for Cloud-Enabled Geological Information Services

Zhu

Tan

Luo

et al. 2018

Scientific Programming

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Cloud computing as a powerful technology of performing massive-scale and complex computing plays an important role in implementing geological information services. In the era of big data, data are being collected at an unprecedented scale. Therefore, to ensure successful data processing and analysis in cloud-enabled geological information services (CEGIS), we must address the challenging and time-demanding task of big data processing. This review starts by elaborating the system architecture and the requirements for big data management. This is followed by the analysis of the application requirements and technical challenges of big data management for CEGIS in China. This review also presents the application development opportunities and technical trends of big data management in CEGIS, including collection and preprocessing, storage and management, analysis and mining, parallel computing based cloud platform, and technology applications.

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Section: Geological Big Data Miningmentioning

confidence: 99%

Big Data Management for Cloud-Enabled Geological Information Services

Zhu

Tan

Luo

et al. 2018

Scientific Programming

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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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Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields

Carranza

2017

Nat Resour Res

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A weighted fuzzy aggregation GIS model in the integration of geophysical data with geochemical and geological data for Pb–Zn exploration in Takab area, NW Iran

Cited by 8 publications

References 52 publications

Big Data Management for Cloud-Enabled Geological Information Services

Big Data Management for Cloud-Enabled Geological Information Services

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

Natural Resources Research Publications on Geochemical Anomaly and Mineral Potential Mapping, and Introduction to the Special Issue of Papers in These Fields

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