The sedimentary record of ultrahigh-pressure metamorphism: a perspective review

J Schönig, H von Eynatten, G Meinhold… - Earth-Science …, 2022 - Elsevier
Tracing ultrahigh-pressure (UHP) metamorphism of crustal rocks through the geological
record is a key for understanding the evolution of plate tectonics on Earth due to the linkage …

Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier

J Schönig, H von Eynatten… - … to Mineralogy and …, 2021 - Springer
The major-element chemical composition of garnet provides valuable petrogenetic
information, particularly in metamorphic rocks. When facing detrital garnet, information about …

[HTML][HTML] Machine learning and tectonic setting determination: bridging the gap between earth scientists and data scientists

P Takaew, JC Xia, LS Doucet - Geoscience Frontiers, 2024 - Elsevier
Technological progress and the rapid increase in geochemical data often create bottlenecks
in many studies, because current methods are designed using limited number of data and …

Discriminating tectonic setting of igneous rocks using biotite major element chemistry− a machine learning approach

R Saha, D Upadhyay, B Mishra - Geochemistry, Geophysics …, 2021 - Wiley Online Library
The composition of igneous biotite is a potential indicator of the geologic environment of its
host rock. Here, we apply two machine learning models− eXtreemly Greedy tree Boosting …

[HTML][HTML] Machine learning prediction of mafic–ultramafic rock-related Cr-spinel formation environments and its application to the tectonic settings of magmatic sulfide …

J Zhao, S Xue, Y Li, Y Niu, X Wang, X Zhang… - Ore Geology …, 2023 - Elsevier
This study uses the random forest machine learning algorithm to classify and predict Cr-
spinel formation environments in mafic–ultramafic rocks. Cr-spinel is an early-crystallized …

Discriminating quartz host rock based on its trace element chemistry using machine learning-a new tool for sedimentary provenance studies

R Saha, D Upadhyay, B Mishra - Chemical Geology, 2024 - Elsevier
Quartz is one of the most abundant mineral in the continental crust, occurring in a wide
variety of igneous, metamorphic, and sedimentary rocks. It incorporates several trace …

Acoustic Sensing and Supervised Machine Learning for In Situ Classification of Semi-Autogenous (SAG) Mill Feed Size Fractions Using Different Feature Extraction …

KB Owusu, W Skinner, RK Asamoah - Powders, 2023 - mdpi.com
Highlights What are the main findings? Laboratory SAG mill acoustics are sensitive to
different feed size fractions. Supervised classification models and acoustic emissions were …

Basalt tectonic discrimination using combined machine learning approach

Q Ren, M Li, S Han, Y Zhang, Q Zhang, J Shi - Minerals, 2019 - mdpi.com
Geochemical discrimination of basaltic magmatism from different tectonic settings remains
an essential part of recognizing the magma generation process within the Earth's mantle …

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

M Petrelli - Journal of Petrology, 2024 - academic.oup.com
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 the basics of ML, including definitions …

Support vector machine for false alarm detection in wind turbine management

AMP Chacón, IS Ramirez… - 2021 7th International …, 2021 - ieeexplore.ieee.org
Wind energy is one of the most growing renewable energy. A proper maintenance
management policy is needed to ensure the viability of wind farms. Supervisory control and …