Advanced

Data fusion for electromagnetic and electrical resistive tomography based on maximum likelihood

Nordebo, Sven LU ; Gustafsson, Mats LU ; Sjöden, Therese and Soldovieri, Francesco (2011) In International Journal of Geophysics
Abstract
This paper presents a maximum likelihood based approach to data fusion

for electromagnetic (EM) and electrical resistive (ER) tomography. The statistical maximum likelihood criterion is closely linked to the additive Fisher information measure, and it facilitates an appropriate weighting of the measurement data which can be useful with multi-physics inverse problems. The Fisher information is particularly useful for inverse problems which can be linearized similar to the Born approximation. In this paper, a proper scalar productis dened for the measurements and a truncated Singular Value Decomposition (SVD) based algorithm is devised which combines the measurement data of the two imaging modalities in a way that is optimal in the... (More)
This paper presents a maximum likelihood based approach to data fusion

for electromagnetic (EM) and electrical resistive (ER) tomography. The statistical maximum likelihood criterion is closely linked to the additive Fisher information measure, and it facilitates an appropriate weighting of the measurement data which can be useful with multi-physics inverse problems. The Fisher information is particularly useful for inverse problems which can be linearized similar to the Born approximation. In this paper, a proper scalar productis dened for the measurements and a truncated Singular Value Decomposition (SVD) based algorithm is devised which combines the measurement data of the two imaging modalities in a way that is optimal in the sense of maximum likelihood.

As a multi-physics problem formulation with applications in geophysics, the

problem of tunnel detection based on EM and ER tomography is studied in this paper. To illustrate the connection between the Green's functions, the gradients and the Fisher information, two simple and generic forward models are described in detail regarding two-dimensional EM and ER tomography, respectively. Numerical examples are included to illustrate the potential impact of an imbalance between the singular values and the variance of the measurement noise when dierent imaging modalities are incorporated in the inversion. The examples furthermore illustrate the signicance of taking a statistically based weighting of the measurement data into proper account. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
International Journal of Geophysics
publisher
Hindawi Publishing Corporation
external identifiers
  • scopus:84871880554
ISSN
1687-885X
DOI
10.1155/2011/617089
language
English
LU publication?
yes
id
b3e0e18a-0200-4964-947e-bc047c65ed2c (old id 2018541)
alternative location
http://www.hindawi.com/journals/ijgp/aip/617089/
date added to LUP
2011-07-04 08:04:03
date last changed
2017-01-01 03:36:35
@article{b3e0e18a-0200-4964-947e-bc047c65ed2c,
  abstract     = {This paper presents a maximum likelihood based approach to data fusion<br/><br>
for electromagnetic (EM) and electrical resistive (ER) tomography. The statistical maximum likelihood criterion is closely linked to the additive Fisher information measure, and it facilitates an appropriate weighting of the measurement data which can be useful with multi-physics inverse problems. The Fisher information is particularly useful for inverse problems which can be linearized similar to the Born approximation. In this paper, a proper scalar productis dened for the measurements and a truncated Singular Value Decomposition (SVD) based algorithm is devised which combines the measurement data of the two imaging modalities in a way that is optimal in the sense of maximum likelihood.<br/><br>
As a multi-physics problem formulation with applications in geophysics, the<br/><br>
problem of tunnel detection based on EM and ER tomography is studied in this paper. To illustrate the connection between the Green's functions, the gradients and the Fisher information, two simple and generic forward models are described in detail regarding two-dimensional EM and ER tomography, respectively. Numerical examples are included to illustrate the potential impact of an imbalance between the singular values and the variance of the measurement noise when dierent imaging modalities are incorporated in the inversion. The examples furthermore illustrate the signicance of taking a statistically based weighting of the measurement data into proper account.},
  articleno    = {617089},
  author       = {Nordebo, Sven and Gustafsson, Mats and Sjöden, Therese and Soldovieri, Francesco},
  issn         = {1687-885X},
  language     = {eng},
  publisher    = {Hindawi Publishing Corporation},
  series       = {International Journal of Geophysics},
  title        = {Data fusion for electromagnetic and electrical resistive tomography based on maximum likelihood},
  url          = {http://dx.doi.org/10.1155/2011/617089},
  year         = {2011},
}