Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences,Mathematics (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s11004-023-10133-2.pdf
Reference52 articles.
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2. Carranza E, Ruitenbeek F, Hecker C, Meijde M, Meer F (2008) Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs Geoinf 10(3):374–387. https://doi.org/10.1016/j.jag.2008.02.008
3. Chen J, Wang R, Zhu J, Lu J, Ma D (2013) Multiple-aged granitoids and related tungsten-tin mineralization in the Nanling Range. South China Sci China Earth Sci 56(12):2045–2055. https://doi.org/10.1007/s11430-013-4736-9
4. Cheng Q (2007) Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan Province China. Ore Geol Rev 32(1–2):314–324. https://doi.org/10.1016/j.oregeorev.2006.10.002
5. Cheng Q, Agterberg F, Ballantyne S (1994) The separation of geochemical anomalies from background by fractal methods. J Geochem Explor 51(2):109–130. https://doi.org/10.1016/0375-6742(94)90013-2
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