Variability of protoliths and pressure-temperature conditions of amphibolites from the Ohmachi Seamount (Izu-Bonin-Mariana arc): evidence of a fossil subduction channel in a modern intra-oceanic arc

Imayama, Takeshi; Ueda, Hayato; Usuki, Tadashi; Minami, Masayo; Asahara, Yoshihiro; Nagahashi, Toru

doi:10.1007/s00710-020-00705-z

Miner Petrol

2020

DOI: 10.1007/s00710-020-00705-z

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Variability of protoliths and pressure-temperature conditions of amphibolites from the Ohmachi Seamount (Izu-Bonin-Mariana arc): evidence of a fossil subduction channel in a modern intra-oceanic arc

Takeshi Imayama

Hayato Ueda

Tadashi Usuki

et al.

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2024

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Identifying serpentine minerals by their chemical compositions with machine learning

Ji,

Huang,

Wang

et al. 2024

American Mineralogist

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The three main serpentine minerals, chrysotile, lizardite, and antigorite, form in various geological settings and have different chemical compositions and rheological properties. The accurate identification of serpentine minerals is thus of fundamental importance to understanding global geochemical cycles and the tectonic evolution of serpentine-bearing rocks. However, it is challenging to distinguish specific serpentine species solely based on geochemical data obtained by traditional analytical techniques. Here, we apply machine learning approaches to classify serpentine minerals based on their chemical compositions alone. Using the Extreme Gradient Boosting (XGBoost) algorithm, we trained a classifier model (overall accuracy of 87.2%) that is capable of distinguishing between low-temperature (chrysotile and lizardite) and high-temperature (antigorite) serpentines mainly based on their SiO2, NiO, and Al2O3 contents. We also utilized a k-means model to demonstrate that the tectonic environment in which serpentine minerals form correlates with their chemical compositions. Our results obtained by combining these classification and clustering models imply the increase of Al2O3 and SiO2 contents and the decrease of NiO content during the transformation from low-to high-temperature serpentine (i.e., lizardite and chrysotile to antigorite) under greenschist–blueschist conditions. These correlations can be used to constrain mass transfer and the surrounding environments during the subduction of hydrated oceanic crust.

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Identifying serpentine minerals by their chemical compositions with machine learning

Ji,

Huang,

Wang

et al. 2024

American Mineralogist

View full text Add to dashboard Cite

show abstract

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Variability of protoliths and pressure-temperature conditions of amphibolites from the Ohmachi Seamount (Izu-Bonin-Mariana arc): evidence of a fossil subduction channel in a modern intra-oceanic arc

Cited by 1 publication

References 50 publications

Identifying serpentine minerals by their chemical compositions with machine learning

Identifying serpentine minerals by their chemical compositions with machine learning

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