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• W4385320806 endingPage "3708" @default.
• W4385320806 startingPage "3708" @default.
• W4385320806 abstract "The exploration of buried mineral deposits is required to generate innovative approaches and the integration of multi-source geoscientific datasets. Mining geochemistry methods have been generated based on the theory of multi-formational geochemical dispersion haloes. Satellite remote sensing data is a form of surficial geoscience datasets and can be considered as big data in terms of veracity and volume. The different alteration zones extracted using remote sensing methods have not been yet categorized based on the mineralogical and geochemical types (MGT) of anomalies and cannot discriminate blind mineralization (BM) from zone dispersed mineralization (ZDM). In this research, an innovative approach was developed to optimize remote sensing-based evidential variables using some constructed mining geochemistry models for a machine learning (ML)-based copper prospectivity mapping. Accordingly, several main steps were implemented and analyzed. Initially, the MGT model was executed by studying the distribution of indicator elements of lithogeochemical data extracted from 50 copper deposits from Commonwealth of Independent States (CIS) countries to identify the MGT of geochemical anomalies associated with copper mineralization. Then, the geochemical zonality model was constructed using the database of the porphyry copper deposits of Iran and Kazakhstan to evaluate the geochemical anomalies related to porphyry copper mineralization (e.g., the Saghari deposit located around the Chah-Musa deposit, Toroud-Chah Shirin belt, central north Iran). Subsequently, the results of mining geochemistry models were used to produce the geochemical evidential variable by vertical geochemical zonality (Vz) (Pb × Zn/Cu × Mo) and to optimize the remote sensing-based evidential variables. Finally, a random forest algorithm was applied to integrate the evidential variables for generating a provincial-scale prospectivity mapping of porphyry copper deposits in the Toroud-Chah Shirin belt. The results of this investigation substantiated that the machine learning (ML)-based integration of multi-source geoscientific datasets, such as mining geochemistry techniques and satellite remote sensing data, is an innovative and applicable approach for copper mineralization prospectivity mapping in metallogenic provinces." @default.
• W4385320806 created "2023-07-28" @default.
• W4385320806 creator A5010426660 @default.
• W4385320806 creator A5035548456 @default.
• W4385320806 creator A5084861735 @default.
• W4385320806 creator A5085075651 @default.
• W4385320806 date "2023-07-25" @default.
• W4385320806 modified "2023-09-23" @default.
• W4385320806 title "Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data" @default.
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• W4385320806 cites W1964776411 @default.
• W4385320806 cites W1967380063 @default.
• W4385320806 cites W1969669722 @default.
• W4385320806 cites W1973595880 @default.
• W4385320806 cites W1976571841 @default.
• W4385320806 cites W1978053173 @default.
• W4385320806 cites W1983865151 @default.
• W4385320806 cites W1987928348 @default.
• W4385320806 cites W1989232026 @default.
• W4385320806 cites W1995182681 @default.
• W4385320806 cites W2006482983 @default.
• W4385320806 cites W2010602539 @default.
• W4385320806 cites W2011814952 @default.
• W4385320806 cites W2013133114 @default.
• W4385320806 cites W2015550944 @default.
• W4385320806 cites W2025105970 @default.
• W4385320806 cites W2031471068 @default.
• W4385320806 cites W2038519446 @default.
• W4385320806 cites W2044187701 @default.
• W4385320806 cites W2058392115 @default.
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• W4385320806 cites W2125838356 @default.
• W4385320806 cites W2128598397 @default.
• W4385320806 cites W2129659014 @default.
• W4385320806 cites W2152753533 @default.
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• W4385320806 doi "https://doi.org/10.3390/rs15153708" @default.
• W4385320806 hasPublicationYear "2023" @default.
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