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Scientific article
Open access
English

Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas

Publication date2020
Abstract

We introduce a new approach, based on Machine Learning, to estimate pre‐eruptive temperatures and storage depths using clinopyroxene‐melt pairs and clinopyroxene‐only chemistry. The model is calibrated for magmas of a wide compositional range, it complements existing models, and it can be applied independently of tectonic setting. Additionally, it allows the identification of the main chemical exchange mechanisms occurring in response to pressure and temperature variations on the base of experimental data without a‐priori assumptions. After the validation process, performances are assessed with test data never used during the training phase. We estimate the uncertainty using the Root Mean Square Error (RMSE) and the coefficient of determination (R2). The application of the best performing algorithm (trained in the range 0‐40 kbar and 952‐1882 K) to clinopyroxene‐melt pairs from primitive to extremely differentiated magmas of both sub‐alkaline and alkaline systems returns a RMSE on the order of 2.6 kbar and 40 K for pressure and temperature, respectively. We additionally present a melt‐ and temperature‐independent clinopyroxene barometer in the range 0‐40 kbar, characterized by a RMSE of the order of 3 kbar. Tested for tholeiitic compositions in the range 0‐10 kbar, the melt‐ and temperature‐independent clinopyroxene barometer has a RMSE of 1.7 kbar. We finally apply the proposed approach to clinopyroxenes from Iceland, providing new, independent, insights about pre‐eruptive storage depths of Icelandic volcanoes. The general applicability of this model will promote the comparison between the architecture of plumbing systems across tectonic settings and facilitate the comparison between petrologic and geophysical studies.

Keywords
  • Machine Learning
  • Petrology
  • Volcanology
  • Barometry
  • Thermometry
Funding
  • European Commission - Forecasting the recurrence rate of volcanic eruptions [677493]
Citation (ISO format)
PETRELLI, Maurizio, CARICCHI, Luca, PERUGINI, Diego. Machine Learning Thermo‐Barometry: Application to Clinopyroxene‐Bearing Magmas. In: Journal of Geophysical Research: Solid Earth, 2020. doi: 10.1029/2020JB020130
Main files (1)
Article (Accepted version)
accessLevelPublic
Identifiers
ISSN of the journal2169-9356
316views
222downloads

Technical informations

Creation09/04/2020 11:17:00 AM
First validation09/04/2020 11:17:00 AM
Update time03/15/2023 10:30:33 PM
Status update03/15/2023 10:30:32 PM
Last indexation01/17/2024 10:44:46 AM
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