Abstract
We present results of a classical global induction analysis of the geomagnetic variation data in the range of daily Sq variations, as well as for long period variations within the period range of about 8 to 400 days. The Sq data from 88 to 94 world observatories are processed in two ways, first by constructing and analyzing average monthly daily variations for the whole months of the International Quiet Sun Year (IQSY) 1995, and second by analyzing the individual, especially quiet Q* daily records from the same year. The electrical images of the Sq response functions obtained via the Schmucker’s ρ* — z* procedure show a good fit with results of other induction studies, though especially our global impedance phases show a larger scatter than two other published data sets used for comparison.
The long period variations from three 3-years’ intervals with different solar and geomagnetic activities and for 44 to 57 world observatories have been processed by power spectral and Fourier analyses, as well as by a simplified GDS approach. The induction response functions show a good correspondence with other deep induction studies, the seasonal processing did not, however, allow us to detect any significant effects of the solar/geomagnetic activity on the transfer functions.
The obtained global geomagnetic induction functions along with other two published data sets are analyzed by a bayesian Monte Carlo analysis for the mantle conductivity distribution. We use a modified version of the Monte Carlo method with Markov chains based on an effective, data adaptive Metropolis sampling approach, and simulate samples from the posterior probability distribution of the resistivities in the mantle. Stochastic sampling provides comprehensive maps of the parameter space based on fairly ranking the models according to their ability to explain the experimental data, as well as on respecting the prior information on the model parameters. From four generally formulated and tested priors for the mantle resistivities, the non-informative distribution on strictly increasing conductance is the most non-restricting prior that, at the same, avoids the non-likely high-resistivity tails in the marginal resistivity distributions. A prediction power of the Monte Carlo sampling approach is demonstrated by a comparison of published maximum likelihood bounds on average conductivities in specific mantle zones with those produced simply by computing the average conductivities from the Markov chain of models.
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Praus, O., Pěčová, J., Červ, V. et al. Electrical conductivity at mid-mantle depths estimated from the data of Sq and long period geomagnetic variations. Stud Geophys Geod 55, 241–264 (2011). https://doi.org/10.1007/s11200-011-0014-5
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DOI: https://doi.org/10.1007/s11200-011-0014-5