EGU22-2347
https://doi.org/10.5194/egusphere-egu22-2347
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Parameter estimation using Cluster array magnetic field data: perfomance and limits of Capon's method

Yasuhito Narita1, Simon Toepfer2, Karl-Heinz Glassmeier3,4, and Uwe Motschmann2,5
Yasuhito Narita et al.
  • 1Space Research Institute, Austrian Academy of Sciences, Graz, Austria (yasuhito.narita@oeaw.ac.at)
  • 2Institut fuer Theoretische Physik, Technische Universitaet Braunschweig, Germany
  • 3Institut fuer Geophysik und extraterrestrische Physik, Technische Universitaet Braunschweig, Germany
  • 4Max-Planck-Institut fuer Sonnensystemforschung, Goettingen, Germany
  • 5Deutsches Zentrum fuer Luft- und Raumfahrt, Institut fuer Planetenforschung, Berlin, Germany

Finding a set of model parameters using the in-situ spacecraft data (such as in the Earth or planetary magnetospheres and in the solar wind) is one of the common exercises in the field of space physics. Above all, parameter estimation using Capon's minimum variance projection, originally developed in the field of array seismology, has successfully been applied to recognizing various structures or spatial patterns in space. Examples of the Capon method can be found in the analysis of the wave structures (plane waves, spherical waves, and phase-shifted waves) and the static, large scale structures (planetary dipolar field and higher-order fields). In order to extend the scientific potential of array magnetic field data such as the Cluster, THEMIS, and MMS missions, the performance and the limits of Capon's method are studied in detail using both analytical and numerical approaches. Our findings are: 1) Capon's method is a simple yet robust implementation of the maximum likelihood method, and 2) its accuracy or error can be evaluated analytically. It is suggested that other inversion techniques such as the least square fitting, the singular value decomposition, the Tikhonov regularization, and the eigenvector-based method may be as competitive as Capon's method when the statistical method is limited in the data analysis. Data analysts have thus a wider range of choices for the structure recognition using array data. 

How to cite: Narita, Y., Toepfer, S., Glassmeier, K.-H., and Motschmann, U.: Parameter estimation using Cluster array magnetic field data: perfomance and limits of Capon's method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2347, https://doi.org/10.5194/egusphere-egu22-2347, 2022.