American Institute of Mathematical Sciences

Advanced
November  2009, 3(4): 711-730. doi: 10.3934/ipi.2009.3.711

Model reduction and pollution source identification from remote sensing data

A Voutilainen 1, and  Jari P. Kaipio 2,

1. 

Department of Physics, University of Kuopio, P.O. Box 1627, FIN-70211 Kuopio
2. 

Department of Physics, University of Kuopio, P.O.Box 1627, 70211 Kuopio, Finland

Received  December 2008 Revised  July 2009 Published  October 2009

We consider a source identification problem related to determination of contaminant source parameters in lake environments using remote sensing measurements. We carry out a numerical example case study in which we employ the statistical inversion approach for the determination of the source parameters. In the simulation study a pipeline breaks on the bottom of a lake and only low-resolution remote sensing measurements are available. We also describe how model uncertainties and especially errors that are related to model reduction are taken into account in the overall statistical model of the system. The results indicate that the estimates may be heavily misleading if the statistics of the model errors are not taken into account.
Keywords: approximation error, Bayesian inference., model reduction, inverse problem, Source identification.
Mathematics Subject Classification: Primary: 35R30, 65N21; Secondary: 62F1.
Citation: A Voutilainen, Jari P. Kaipio. Model reduction and pollution source identification from remote sensing data. Inverse Problems & Imaging, 2009, 3 (4) : 711-730. doi: 10.3934/ipi.2009.3.711
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