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Articles citing this paper

Lithological Mapping via Random Forests: Information Entropy as a Proxy for Inaccuracy

Stephen Kuhn, Matthew J. Cracknell and Anya M. Reading
2016(1) pp.1 - 4


5 articles found in Crossref database.

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Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia
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GEOPHYSICS. 2018 83(4). p.B183
Lithology identification method based on integrated K-means clustering and meta-object representation
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Arabian Journal of Geosciences. 2022 15(17).
Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier
Kuhn Stephen, Cracknell Matthew J., Reading Anya M., Sykora Stephanie
GEOPHYSICS. 2020 85(6). p.B249
DRSN-GAF: Deep Residual Shrinkage Network (DRSN) for Lithology Classification Through Well Logging Data Transformed by Gram Angle Field
Sun Youzhuang, Pang Shanchen, Zhang Junhua, Zhang Yongan
IEEE Geoscience and Remote Sensing Letters. 2024 21 p.1
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