Incorporating Geological Knowledge into Deep Learning to Enhance Geochemical Anomaly Identification Related to Mineralization and Interpretability

Effective geochemical anomaly identification is crucial in mineral exploration. Recent trends have favored deep learning (DL) to decipher geochemical survey data. Yet purely data-driven DL algorithms often lack logical explanations and geological consistency, occasionally clashing with known geologi...

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Vydané v:	Mathematical geosciences Ročník 56; číslo 6; s. 1233 - 1254
Hlavní autori:	Zhang, Chunjie, Zuo, Renguang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Predmet:	Chemistry and Earth Sciences Computer Science Earth and Environmental Science Earth Sciences Geotechnical Engineering & Applied Earth Sciences Hydrogeology Physics Statistics for Engineering Latent variables Geochemical anomalies detection Adversarial autoencoder Prior geological knowledge
ISSN:	1874-8961, 1874-8953
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