Machine Learning in Petrology: State-of-the-Art and Future Perspectives

Journal of Petrology, Journal Year: 2024, Volume and Issue: 65(5)

Published: March 28, 2024

Abstract This article reports on the state-of-the-art and future perspectives of machine learning (ML) in petrology. To achieve this goal, it first introduces basics ML, including definitions, core concepts, applications. Then, starts reviewing ML Established applications mainly concern so-called data-driven discovery involve specific tasks like clustering, dimensionality reduction, classification, regression. Among them, clustering reduction have been demonstrated to be valuable for decoding chemical record stored igneous metamorphic phases enhance data visualization, respectively. Classification regression find applications, example, petrotectonic discrimination geo-thermobarometry, The main manuscript consists depicting emerging trends directions petrological investigations. I propose a scenario where methods will progressively integrate support established automating time-consuming repetitive tasks, improving current models, boosting discovery. In framework, promising include (1) acquisition new multimodal petrologic data; (2) development fusion techniques, physics-informed ML-supported numerical simulations; (3) continuous exploration potential boost contribution petrology, our challenges are: improve ability models capture complexity processes, link algorithms with physical thermodynamic nature investigated problems, start collaborative effort among researchers coming from different disciplines, both research teaching.

Language: Английский

Intelligent identification of carbonate components based on deep learning

Facies, Journal Year: 2025, Volume and Issue: 71(1)

Published: Jan. 10, 2025

Language: Английский

Sediment grain segmentation in thin-section images using dual-modal Vision Transformer

Computers & Geosciences, Journal Year: 2024, Volume and Issue: 191, P. 105664 - 105664

Published: June 21, 2024

Language: Английский