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Data fusion for reconstruction algorithms via different sensors in geophysical sensing
Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.ORCID iD: 0000-0002-7018-6248
2011 (English)In: Journal of Geophysics and Engineering, ISSN 1742-2132, E-ISSN 1742-2140, Vol. 8, no 3, p. S54-S60Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2011. Vol. 8, no 3, p. S54-S60
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Physics, Waves and Signals
Identifiers
URN: urn:nbn:se:lnu:diva-16727DOI: 10.1088/1742-2132/8/3/S06Scopus ID: 2-s2.0-80052163966OAI: oai:DiVA.org:lnu-16727DiVA, id: diva2:476295
Available from: 2012-01-11 Created: 2012-01-11 Last updated: 2017-12-08Bibliographically approved

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Nordebo, Sven

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  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
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