An entropy regularization method applied to the identification of wave distribution function for an ELF hiss event

Details An entropy regularization method applied to the identification of wave distribution function for an ELF hiss event An entropy regularization method applied to the identification of wave distribution function for an ELF hiss event

Jun 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006jgra..11106213p&link_type=abstract

Journal of Geophysical Research, Volume 111, Issue A6, CiteID A06213

Physics

Geophysics

Mathematical Geophysics: Inverse Theory, Radio Science: Waves In Plasma (7867), Radio Science: Signal Processing (0674), Atmospheric Composition And Structure: Cloud Physics And Chemistry

Scientific paper

An entropy regularization algorithm (ERA) has been developed to compute the wave-energy density from electromagnetic field measurements. It is based on the wave distribution function (WDF) concept. To assess its suitability and efficiency, the algorithm is applied to experimental data that has already been analyzed using other inversion techniques. The FREJA satellite data that is used consists of six spectral matrices corresponding to six time-frequency points of an ELF hiss-event spectrogram. The WDF analysis is performed on these six points and the results are compared with those obtained previously. A statistical stability analysis confirms the stability of the solutions. The WDF computation is fast and without any prespecified parameters. The regularization parameter has been chosen in accordance with the Morozov's discrepancy principle. The Generalized Cross Validation and L-curve criterions are then tentatively used to provide a fully data-driven method. However, these criterions fail to determine a suitable value of the regularization parameter. Although the entropy regularization leads to solutions that agree fairly well with those already published, some differences are observed, and these are discussed in detail. The main advantage of the ERA is to return the WDF that exhibits the largest entropy and to avoid the use of a priori models, which sometimes seem to be more accurate but without any justification.

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