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

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This summary is machine-generated.

This study introduces a machine learning approach for clinopyroxene thermobarometry, improving pressure and temperature estimations in volcanic systems. Using median values in random forest models enhances accuracy for magma research.

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Area of Science:

  • Geochemistry
  • Volcanology
  • Machine Learning Applications

Background:

  • Thermobarometry is crucial for understanding magma plumbing systems and volcanic processes.
  • Machine learning, specifically random forests, offers a data-driven method for clinopyroxene thermobarometry.
  • Existing methods can be enhanced by leveraging large experimental datasets.

Purpose of the Study:

  • To assess the performance of random forest-based clinopyroxene thermobarometry using the R package extraTrees.
  • To investigate the impact of hyperparameter tuning and uncertainty calculation methods.
  • To provide open-source scripts for applying this methodology to natural samples.

Main Methods:

  • Utilized the extraTrees package in R for random forest modeling.
  • Evaluated model performance by varying hyperparameters and comparing aggregation methods (mean, median, mode).
  • Developed and tested two R scripts for data processing and PT estimation.

Main Results:

  • Model performance is minimally affected by hyperparameter deviations but significantly influenced by the choice of aggregation (median preferred over mean).
  • The median aggregation method yields smaller residuals between experimental and predicted pressure (P) and temperature (T).
  • Open-source scripts provide PT estimates with reported standard error estimates (SEE) of 3.2 kbar, 72.5°C (clinopyroxene-only) and 2.7 kbar, 44.9°C (liquid-clinopyroxene).

Conclusions:

  • Random forest thermobarometry, particularly using median aggregation, provides a robust and accurate method for determining P-T conditions in magma systems.
  • The provided open-source scripts facilitate the application of this advanced technique to geological samples, aiding in magma research.
  • This data-driven approach enhances our quantitative understanding of volcanic processes and magma evolution.