Mapping mineral prospectivity using an extreme learning machine regression
Sign Up to like & getrecommendations! Published in 2017 at "Ore Geology Reviews"
DOI: 10.1016/j.oregeorev.2016.06.033
Abstract: Abstract In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used… read more here.
Keywords: logistic regression; regression; prospectivity; elm regression ... See more keywords
GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China
Sign Up to like & getrecommendations! Published in 2019 at "Ore Geology Reviews"
DOI: 10.1016/j.oregeorev.2019.04.003
Abstract: Abstract Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning methods, including support vector machine (SVM), artificial neural… read more here.
Keywords: mineral prospectivity; tongling ore; machine; ore district ... See more keywords
Controls on and prospectivity mapping of volcanic-type uranium mineralization in the Pucheng district, NW Fujian, China
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DOI: 10.1016/j.oregeorev.2019.103028
Abstract: Abstract A number of volcanic-type uranium deposits occur in Pucheng district, Fujian province, China. Structural controls and volcanic rocks clearly explain the uranium mineralization in this area. To explore the subtle spatial associations of uranium… read more here.
Keywords: prospectivity; uranium mineralization; mineralization; pucheng district ... See more keywords
Prospectivity modelling of Canadian magmatic Ni (±Cu ± Co ± PGE) sulphide mineral systems
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DOI: 10.1016/j.oregeorev.2021.103985
Abstract: Abstract New mineral deposit discoveries are required to meet the forecasted demand for some critical raw materials. Governments are responding to that challenge by investing in mineral systems research and by by making government geoscience… read more here.
Keywords: prospectivity modelling; geoscience; mineral system; mineral systems ... See more keywords
Mineral prospectivity prediction based on convolutional neural network and ensemble learning
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DOI: 10.1038/s41598-024-73357-0
Abstract: Current research in deep learning, which is widely used in mineral prospectivity prediction, focuses on obtaining high-performance models to predict mineral resources. However, because the network structure and depth of different algorithms differ, there are… read more here.
Keywords: prospectivity prediction; mineral prospectivity; ensemble learning; prediction ... See more keywords
Prospectivity analysis of gold and iron oxide copper-gold-(silver) mineralizations from the Faina Greenstone Belt, Brazil, using multiple data sets
Sign Up to like & getrecommendations! Published in 2017 at "Brazilian Journal of Geology"
DOI: 10.1590/2317-4889201720170012
Abstract: The Faina Greenstone Belt is located in the southern sector of the Goias Archean Block and has been investigated since the 18th century because of its gold deposits. Recent studies have revealed the polymetallic potential… read more here.
Keywords: greenstone belt; multiple data; prospectivity; faina greenstone ... See more keywords
Integrated Prospectivity Mapping for Copper Mineralization in the Koldar Massif, Kazakhstan
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DOI: 10.3390/min15080805
Abstract: This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults… read more here.
Keywords: massif kazakhstan; koldar massif; mineralization; prospectivity ... See more keywords
Mineral Prospectivity Mapping for Exploration Targeting of Porphyry Cu-Polymetallic Deposits Based on Machine Learning Algorithms, Remote Sensing and Multi-Source Geo-Information
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DOI: 10.3390/min15101050
Abstract: Machine learning (ML) algorithms have promoted the development of predictive modeling of mineral prospectivity, enabling data-driven decision-making processes by integrating multi-source geological information, leading to efficient and accurate prediction of mineral exploration targets. However, it… read more here.
Keywords: information; exploration; machine; source ... See more keywords