Effects of the site distribution and the prior information on the inverted geomagnetic field model: A case study applying the ABIC method to the synthetic datasets

Abstract

When we use stochastic inversion and Bayesian modelling in order to obtain geomagnetic field models from paleomagnetic data, there are two major factors controlling the solution: determination of the hyperparameter and the type of the smoothing constraint on the model. To investigate contributions of the factors, we calculated some patterns of inversions from synthetic datasets from ideal and real site distributions. The ABIC (Akaike's Bayesian Information Criteria) minimization method was used to determine the hyperparameter, and then the relationship between the hyperparameter and the ABIC index was demonstrated. Using results of an inversion of synthetic datasets with errors, the most suitable hyperparameters were found for each site distribution, and the good and stable solutions were obtained. However, when number of the sites is few or coverage of the site distribution is not uniform, it is found that the solution is not clearly determined. Moreover, it seems that the solution does not significantly depend on the type of the model constraint. Copyright © The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS).