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

0557132 - GFÚ 2023 RIV NL eng J - Článek v odborném periodiku
Cordell, Darcy - Hill, Graham J. - Bachmann, O. - Moorkamp, M. - Huber, Ch.
Estimating melt fraction in silicic systems using Bayesian inversion of magnetotelluric data.
Journal of Volcanology and Geothermal Research. Roč. 423, March (2022), č. článku 107470. ISSN 0377-0273. E-ISSN 1872-6097
Grant ostatní: AV ČR(CZ) LQ100121901
Program: Prémie Lumina quaeruntur
Institucionální podpora: RVO:67985530
Klíčová slova: magnetotellurics * magma reservoir * melt fraction * volcano geophysics
Obor OECD: Volcanology
Impakt faktor: 2.9, rok: 2022
Způsob publikování: Open access
https://www.sciencedirect.com/science/article/pii/S0377027322000014

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.
Trvalý link: http://hdl.handle.net/11104/0331191