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dc.contributor.author
Macchioli Grande, Franco Sebastián

dc.contributor.author
Zyserman, Fabio Ivan

dc.contributor.author
Monachesi, Leonardo Bruno

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Jouniaux, Laurence
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Rosas Carbajal, Marina Andrea

dc.date.available
2021-09-01T01:24:59Z
dc.date.issued
2020-02-05
dc.identifier.citation
Macchioli Grande, Franco Sebastián; Zyserman, Fabio Ivan; Monachesi, Leonardo Bruno; Jouniaux, Laurence; Rosas Carbajal, Marina Andrea; Bayesian inversion of joint SH seismic and seismoelectric data to infer glacier system properties; Wiley Blackwell Publishing, Inc; Geophysical Prospecting; 68; 5; 5-2-2020; 1633-1656
dc.identifier.issn
0016-8025
dc.identifier.uri
http://hdl.handle.net/11336/139380
dc.description.abstract
In glacial studies, properties such as glacier thickness and the basement permeability and porosity are key to understand the hydrological and mechanical behaviour of the system. The seismoelectric method could potentially be used to determine key properties of glacial environments. Here we analytically model the generation of seismic and seismoelectric signals by means of a shear horizontal seismic wave source on top of a glacier overlying a porous basement. Considering a one-dimensional setting, we compute the seismic waves and the electrokinetically induced electric field. We then analyse the sensitivity of the seismic and electromagnetic data to relevant model parameters, namely depth of the glacier bottom, porosity, permeability, shear modulus and saturating water salinity of the glacier basement. Moreover, we study the possibility of inferring these key parameters from a set of very low noise synthetic data, adopting a Bayesian framework to pay particular attention to the uncertainty of the model parameters mentioned above. We tackle the resolution of the probabilistic inverse problem with two strategies: (1) we compute the marginal posterior distributions of each model parameter solving multidimensional integrals numerically and (2) we use a Markov chain Monte Carlo algorithm to retrieve a collection of model parameters that follows the posterior probability density function of the model parameters, given the synthetic data set. Both methodologies are able to obtain the marginal distributions of the parameters and estimate their mean and standard deviation. The Markov chain Monte Carlo algorithm performs better in terms of numerical stability and number of iterations needed to characterize the distributions. The inversion of seismic data alone is not able to constrain the values of porosity and permeability further than the prior distribution. In turn, the inversion of the electric data alone, and the joint inversion of seismic and electric data are useful to constrain these parameters as well as other glacial system properties. Furthermore, the joint inversion reduces the uncertainty of the model parameters estimates and provides more accurate results.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley Blackwell Publishing, Inc

dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ELECTROMAGNETICS
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INVERSION
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MODELLING
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PARAMETER ESTIMATION
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SEISMICS
dc.subject.classification
Geoquímica y Geofísica

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Ciencias de la Tierra y relacionadas con el Medio Ambiente

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CIENCIAS NATURALES Y EXACTAS

dc.title
Bayesian inversion of joint SH seismic and seismoelectric data to infer glacier system properties
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-05-11T18:27:24Z
dc.journal.volume
68
dc.journal.number
5
dc.journal.pagination
1633-1656
dc.journal.pais
Reino Unido

dc.description.fil
Fil: Macchioli Grande, Franco Sebastián. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Zyserman, Fabio Ivan. Universidad Nacional de La Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Monachesi, Leonardo Bruno. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Negro; Argentina
dc.description.fil
Fil: Jouniaux, Laurence. Université de Strasbourg; Francia
dc.description.fil
Fil: Rosas Carbajal, Marina Andrea. Universite de Paris; Francia. Institut de Physique Du Globe de Paris; Francia
dc.journal.title
Geophysical Prospecting

dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1111/1365-2478.12940
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1111/1365-2478.12940
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