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| Titel |
Tunnel detection based on data fusion with radio frequency and electrical resistive tomography |
| VerfasserIn |
Sven Nordebo, Mats Gustafsson, Francesco Soldovieri |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250049876
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| Zusammenfassung |
| Future non-destructive monitoring systems based on ICT and sensor technologies will
provide emergency and disasters stakeholders with high situation awareness by means of real
time and detailed information and images of the infrastructure status [1]. Useful
technologies include Electrical Resistive Tomography (ERT) as well as Ground
Penetrating Radar (GPR), which have become widely applied to obtain images of the
subsurface in areas of complex geology [2, 3, 4]. However, the development and
implementation of complex ICT based monitoring systems will also rely on new
technologies, techniques and algorithms including the integration and correlation of the
electromagnetic properties corresponding to imaging modalities such as ERT and GPR
[1].
It is natural to adopt a statistical or Bayesian approach towards multi-sensor data
fusion [5, 6, 7]. In this paper, the general approach to information fusion is hence
to perform a multi-sensor, multimodal or multi-physics data fusion based on the
statistically optimum Maximum Likelihood (ML) principle, and in particular to
exploit the principles of Fisher information analysis that has been developed in
[8, 9, 10].
As a generic example concerning a multi-physics inverse problem based on geophysical
sensing, this paper addresses the problem of tunnel detection [4] based on data fusion with
radio frequency and electrical resistive tomography. A two-dimensional problem is
considered with radio frequency as well as electrical sensors places horizontally above a void
in the ground. The example is concerned with information fusion for a linearized inverse
problem similar to the Born approximation [3], and a truncated Singular Value
Decomposition (SVD) based algorithm which combines the two imaging modalities in a way
that is optimal in the sense of maximum likelihood. A Green’s function approach
is used to obtain the gradients for the Fisher information analysis [10] as well as
for the related SVD based algorithm. The examples demonstrate that proper data
fusion can be of crucial importance for ill-posed inverse problems in geophysical
applications.
Acknowledgement
The research leading to these results has received funding from the European Community’s
Seventh Framework Programme (FP7/2007-2013) under Grant Agreement no 225663.
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