A Machine Learning‐Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences-Reference-Cited by-同舟云学术

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A Machine Learning‐Based Approach to Clinopyroxene Thermobarometry: Model Optimization and Distribution for Use in Earth Sciences

Published:2022-04 Issue:4 Volume:127 Page:
ISSN:2169-9313
Container-title:Journal of Geophysical Research: Solid Earth
language:en
Short-container-title:JGR Solid Earth

Author:

Jorgenson C.¹^ORCID,Higgins O.¹,Petrelli M.²^ORCID,Bégué F.¹,Caricchi L.¹^ORCID

Affiliation:

1. Department of Earth Sciences University of Geneva Geneva Switzerland

2. Department of Physics and Geology University of Perugia Perugia Italy

Funder

Università degli Studi di Perugia

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021JB022904

Reference252 articles.

1. The Genesis of Intermediate and Silicic Magmas in Deep Crustal Hot Zones

2. Mount Etna pyroxene as tracer of petrogenetic processes and dynamics of the feeding system

3. Solubility of H2O and CO2 in ultrapotassic melts at 1200 and 1250 C and pressure from 50 to 500 MPa

4. Recent crustal foundering in the Northern Volcanic Zone of the Andean arc: Petrological insights from the roots of a modern subduction zone

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