A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation

Simulation-based probabilistic inversions of 3D magnetotelluric (MT) data are arguably the best option to deal with the non-linearity and non-uniqueness of the MT problem. However, the computational cost associated with the modeling of 3D MT data has so far precluded the community from adopting and/...

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Autores principales: Manassero, María Constanza, Afonso, Juan Carlos, Zyserman, Fabio Iván, Zlotnik, Sergio, Fomin, Ilya
Formato: Articulo
Lenguaje:Inglés
Publicado: 2020
Materias:
Astronomía
Composition and structure of the mantle
Magnetotellurics
Inverse theory
Numerical approximations and analysis
Numerical modelling
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/131838
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-131838
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Astronomía
Composition and structure of the mantle
Magnetotellurics
Inverse theory
Numerical approximations and analysis
Numerical modelling
spellingShingle Astronomía
Composition and structure of the mantle
Magnetotellurics
Inverse theory
Numerical approximations and analysis
Numerical modelling
Manassero, María Constanza
Afonso, Juan Carlos
Zyserman, Fabio Iván
Zlotnik, Sergio
Fomin, Ilya
A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
topic_facet Astronomía
Composition and structure of the mantle
Magnetotellurics
Inverse theory
Numerical approximations and analysis
Numerical modelling
description Simulation-based probabilistic inversions of 3D magnetotelluric (MT) data are arguably the best option to deal with the non-linearity and non-uniqueness of the MT problem. However, the computational cost associated with the modeling of 3D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT datasets. In this contribution, we present a novel and general inversion framework, driven by Markov chain Monte Carlo (MCMC) algorithms, which combines i) an efficient parallel-in-parallel structure to solve the 3D forward problem, ii) a reduced order technique to create fast and accurate surrogate models of the forward problem, and iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parameterizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.
format Articulo
Articulo
author Manassero, María Constanza
Afonso, Juan Carlos
Zyserman, Fabio Iván
Zlotnik, Sergio
Fomin, Ilya
author_facet Manassero, María Constanza
Afonso, Juan Carlos
Zyserman, Fabio Iván
Zlotnik, Sergio
Fomin, Ilya
author_sort Manassero, María Constanza
title A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
title_short A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
title_full A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
title_fullStr A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
title_full_unstemmed A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation
title_sort reduced order approach for probabilistic inversions of 3-d magnetotelluric data i: general formulation
publishDate 2020
url http://sedici.unlp.edu.ar/handle/10915/131838
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