Advanced

Data fusion for reconstruction algorithms via different sensors in geophysical sensing

Nordebo, Sven LU ; Gustafsson, Mats LU and Soldovieri, Francesco (2011) In Journal of Geophysics and Engineering 8(3). p.54-60
Abstract (Swedish)
Abstract in Undetermined

Information fusion via multimodal inverse problems and different sensors is addressed using a Fisher information analysis approach. The Fisher information measure is inherently additive, and it facilitates an appropriate weighting of the measurement data that is statistically optimal and can hence be useful with reconstruction algorithms in geophysical sensing. Given that there exists proper knowledge about the sensor noise statistics, correlations and spectral contents, as well as a correct forward model, the Fisher information is a natural measure of information because it is closely linked to the statistical maximum likelihood principle. To illustrate the concept of data correlation based on... (More)
Abstract in Undetermined

Information fusion via multimodal inverse problems and different sensors is addressed using a Fisher information analysis approach. The Fisher information measure is inherently additive, and it facilitates an appropriate weighting of the measurement data that is statistically optimal and can hence be useful with reconstruction algorithms in geophysical sensing. Given that there exists proper knowledge about the sensor noise statistics, correlations and spectral contents, as well as a correct forward model, the Fisher information is a natural measure of information because it is closely linked to the statistical maximum likelihood principle. To illustrate the concept of data correlation based on statistical Fisher information analysis, two simple and generic examples are employed in electrical resistivity and electromagnetic tomography, which are motivated by geophysical applications, such as tunnel detection. The examples demonstrate that a properly weighted data fusion can be of crucial importance for an ill-posed multimodal inverse problem. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
data fusion, inverse problems, Fisher information, electrical impedance tomography
in
Journal of Geophysics and Engineering
volume
8
issue
3
pages
54 - 60
publisher
IOP Publishing
external identifiers
  • wos:000294161500007
  • scopus:80052163966
ISSN
1742-2140
DOI
10.1088/1742-2132/8/3/S06
project
EIT_ISTIMES Integrated System for Transport Infrastructures surveillance and Monitoring by Electromagnetic Sensing
language
English
LU publication?
yes
id
3bd070a1-c609-4a44-b8a5-42dc449f45cc (old id 2018546)
date added to LUP
2011-07-04 08:06:35
date last changed
2017-01-01 03:35:53
@article{3bd070a1-c609-4a44-b8a5-42dc449f45cc,
  abstract     = {<b>Abstract in Undetermined</b><br/><br>
Information fusion via multimodal inverse problems and different sensors is addressed using a Fisher information analysis approach. The Fisher information measure is inherently additive, and it facilitates an appropriate weighting of the measurement data that is statistically optimal and can hence be useful with reconstruction algorithms in geophysical sensing. Given that there exists proper knowledge about the sensor noise statistics, correlations and spectral contents, as well as a correct forward model, the Fisher information is a natural measure of information because it is closely linked to the statistical maximum likelihood principle. To illustrate the concept of data correlation based on statistical Fisher information analysis, two simple and generic examples are employed in electrical resistivity and electromagnetic tomography, which are motivated by geophysical applications, such as tunnel detection. The examples demonstrate that a properly weighted data fusion can be of crucial importance for an ill-posed multimodal inverse problem.},
  author       = {Nordebo, Sven and Gustafsson, Mats and Soldovieri, Francesco},
  issn         = {1742-2140},
  keyword      = {data fusion,inverse problems,Fisher information,electrical impedance tomography},
  language     = {eng},
  number       = {3},
  pages        = {54--60},
  publisher    = {IOP Publishing},
  series       = {Journal of Geophysics and Engineering},
  title        = {Data fusion for reconstruction algorithms via different sensors in geophysical sensing},
  url          = {http://dx.doi.org/10.1088/1742-2132/8/3/S06},
  volume       = {8},
  year         = {2011},
}