The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Data fusion for reconstruction algorithms via different sensors in geophysical sensing

Author

Summary, in English

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.

Publishing year

2011

Language

English

Pages

54-60

Publication/Series

Journal of Geophysics and Engineering

Volume

8

Issue

3

Document type

Journal article

Publisher

IOP Publishing

Topic

  • Electrical Engineering, Electronic Engineering, Information Engineering

Keywords

  • data fusion
  • inverse problems
  • Fisher information
  • electrical impedance tomography

Status

Published

Project

  • EIT_ISTIMES Integrated System for Transport Infrastructures surveillance and Monitoring by Electromagnetic Sensing

Research group

  • Electromagnetic theory

ISBN/ISSN/Other

  • ISSN: 1742-2140