Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F22%3A00557132" target="_blank" >RIV/67985530:_____/22:00557132 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0377027322000014" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0377027322000014</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jvolgeores.2022.107470" target="_blank" >10.1016/j.jvolgeores.2022.107470</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data

Popis výsledku v původním jazyce

The location, volume and physical states of magma reservoirs are primary controls on the eruptive behavior of volcanic systems. Fundamental to understanding and monitoring these systems is the ability to identify reservoir size and physical properties, in particular melt fraction which plays an important role in the rheology and stability of a magmatic system. Large silicic volcanic eruptions in the geological record suggest that extensive pockets of melt-rich silicic magma must exist in the subsurface but such melt pockets have not been detected by geophysics. This has led to the question of whether the reservoirs that feed large volcanic eruptions are only melt-rich for a short time and thus would only be detected by geophysics shortly prior to an eruption. Magnetotelluric data measure the electrical resistivity of the subsurface and are sensitive to subsurface fluids and partial melts making it a powerful tool for imaging subvolcanic magma reservoirs. This study examines the ability for magnetotelluric data to accurately estimate melt fraction using both stochastic Bayesian inversion and deterministic regularized inversion. Results from synthetic modelling indicate that magnetotelluric data are best able to predict the melt fraction for the thick melt-rich layer using both inversion methods, though both methods under-estimate the true amount of melt. In addition, magnetotelluric data can accurately detect changes in melt fraction from crystal rich mush (0.1 melt fraction) to melt-rich magma (0.9 melt fraction) for thick layers. Thickness is a key parameter which provides a method to assess the total volume of melt present, but it is difficult to estimate using smooth regularized inversions.

Název v anglickém jazyce

Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data

Popis výsledku anglicky

The location, volume and physical states of magma reservoirs are primary controls on the eruptive behavior of volcanic systems. Fundamental to understanding and monitoring these systems is the ability to identify reservoir size and physical properties, in particular melt fraction which plays an important role in the rheology and stability of a magmatic system. Large silicic volcanic eruptions in the geological record suggest that extensive pockets of melt-rich silicic magma must exist in the subsurface but such melt pockets have not been detected by geophysics. This has led to the question of whether the reservoirs that feed large volcanic eruptions are only melt-rich for a short time and thus would only be detected by geophysics shortly prior to an eruption. Magnetotelluric data measure the electrical resistivity of the subsurface and are sensitive to subsurface fluids and partial melts making it a powerful tool for imaging subvolcanic magma reservoirs. This study examines the ability for magnetotelluric data to accurately estimate melt fraction using both stochastic Bayesian inversion and deterministic regularized inversion. Results from synthetic modelling indicate that magnetotelluric data are best able to predict the melt fraction for the thick melt-rich layer using both inversion methods, though both methods under-estimate the true amount of melt. In addition, magnetotelluric data can accurately detect changes in melt fraction from crystal rich mush (0.1 melt fraction) to melt-rich magma (0.9 melt fraction) for thick layers. Thickness is a key parameter which provides a method to assess the total volume of melt present, but it is difficult to estimate using smooth regularized inversions.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10507 - Volcanology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Volcanology and Geothermal Research

ISSN

0377-0273

e-ISSN

1872-6097

Svazek periodika

423

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

107470

Kód UT WoS článku

000764686300001

EID výsledku v databázi Scopus

2-s2.0-85122779667