Data Fusion for Electromagnetic and Electrical Resistive Tomography Based on Maximum Likelihood
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 meas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2011-01-01
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| Series: | International Journal of Geophysics |
| Online Access: | http://dx.doi.org/10.1155/2011/617089 |
| Summary: | 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
multiphysics 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 product is defined 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 multiphysics 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. |
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| ISSN: | 1687-885X 1687-8868 |