The anatomy of the plumbing system of active volcanoes is fundamental to understand how magma is stored and channeled to the surface. Reliable geothermobarometric estimates are, therefore, critical to assess the depths and temperatures of the complex system of magmatic reservoirs that form a volcano apparatus. Here, we developed a novel Machine Learning approach (named GAIA, Geo Artificial Intelligence thermobArometry) based upon Feedforward Neural Networks to estimate P-T conditions of magma (clinopyroxene) storage and migration within the crust. Our Feedforward Neural Network method applied to clinopyroxene compositions yields better uncertainties (Root-Mean-Square Error and R2 score) than previous Machine Learning methods and set the basis for a novel generation of reliable geothermobarometers, which extends beyond the paradigm associated to crystal-liquid equilibrium. Also, the bootstrap procedure, inherent to the Feedforward Neural Network architecture, permits to perform a rigorous assessment of the P-T uncertainty associated to each clinopyroxene composition, as opposed to the Root-Mean-Square Error representing the P-T uncertainty of whole set of clinopyroxene compositions. As a test, we applied GAIA to assess P-T conditions of five Italian volcanoes (Somma-Vesuvius, Campi Flegrei, Etna, Stromboli, Volcano), which are among the most dangerous volcanic centres in Europe. The results on the depths of the plumbing systems are in excellent agreement with those obtained with independent geophysical and geodetic surveys, and provide further evidence to current models of volcano plumbing systems consisting of physically-separated reservoirs interconnected by a network of conduits channelling magma en route to the surface. The results on the magma (clinopyroxene crystallization) temperatures are also in agreement with other estimates, albeit obtained considering - mainly but not only - thermodynamically-based clinopyroxene-liquid geothermometers. GAIA can set robust estimates of magma storage, segregation, and ascent conditions within the plumbing system of active volcanoes, helping to unravel P-T variations, if any, during their eruptive history and providing robust clues to volcanic hazard assessment.

Frontiers of thermobarometry: GAIA, a novel Deep Learning-based tool for volcano plumbing systems / Chicchi, Lorenzo; Bindi, Luca; Fanelli, Duccio; Tommasini, Simone. - In: EARTH AND PLANETARY SCIENCE LETTERS. - ISSN 0012-821X. - ELETTRONICO. - 620:(2023), pp. 118352.1-118352.12. [10.1016/j.epsl.2023.118352]

Frontiers of thermobarometry: GAIA, a novel Deep Learning-based tool for volcano plumbing systems

Chicchi, Lorenzo;Bindi, Luca;Fanelli, Duccio;Tommasini, Simone
2023

Abstract

The anatomy of the plumbing system of active volcanoes is fundamental to understand how magma is stored and channeled to the surface. Reliable geothermobarometric estimates are, therefore, critical to assess the depths and temperatures of the complex system of magmatic reservoirs that form a volcano apparatus. Here, we developed a novel Machine Learning approach (named GAIA, Geo Artificial Intelligence thermobArometry) based upon Feedforward Neural Networks to estimate P-T conditions of magma (clinopyroxene) storage and migration within the crust. Our Feedforward Neural Network method applied to clinopyroxene compositions yields better uncertainties (Root-Mean-Square Error and R2 score) than previous Machine Learning methods and set the basis for a novel generation of reliable geothermobarometers, which extends beyond the paradigm associated to crystal-liquid equilibrium. Also, the bootstrap procedure, inherent to the Feedforward Neural Network architecture, permits to perform a rigorous assessment of the P-T uncertainty associated to each clinopyroxene composition, as opposed to the Root-Mean-Square Error representing the P-T uncertainty of whole set of clinopyroxene compositions. As a test, we applied GAIA to assess P-T conditions of five Italian volcanoes (Somma-Vesuvius, Campi Flegrei, Etna, Stromboli, Volcano), which are among the most dangerous volcanic centres in Europe. The results on the depths of the plumbing systems are in excellent agreement with those obtained with independent geophysical and geodetic surveys, and provide further evidence to current models of volcano plumbing systems consisting of physically-separated reservoirs interconnected by a network of conduits channelling magma en route to the surface. The results on the magma (clinopyroxene crystallization) temperatures are also in agreement with other estimates, albeit obtained considering - mainly but not only - thermodynamically-based clinopyroxene-liquid geothermometers. GAIA can set robust estimates of magma storage, segregation, and ascent conditions within the plumbing system of active volcanoes, helping to unravel P-T variations, if any, during their eruptive history and providing robust clues to volcanic hazard assessment.
2023
620
1
12
Chicchi, Lorenzo; Bindi, Luca; Fanelli, Duccio; Tommasini, Simone
File in questo prodotto:
File Dimensione Formato  
2023EPSL-Gaia-preprint.pdf

Accesso chiuso

Tipologia: Preprint (Submitted version)
Licenza: Tutti i diritti riservati
Dimensione 7.34 MB
Formato Adobe PDF
7.34 MB Adobe PDF   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1326032
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact