Day 4

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Paper title Bayesian inversion of magnetic data: A sensitivity study of Australia
Authors
  1. Yixiati Dilixiati University of Kiel, Germany Speaker
  2. Wolfgang Szwillus University of Kiel, Germany
  3. Jörg Ebbing Kiel University
Form of presentation Poster
Topics
  • B2. Earth Explorer missions
    • B2.05 Swarm - ESA's Extremely Versatile Magnetic Field and Geospace Explorer
Abstract text Estimating the susceptibility and depth to the bottom of the magnetic layer is an ill-posed problem. Therefore, assumptions of one of the parameters have to be made in order to estimate the other. Here, we apply a linearized two-step Bayesian inversion approach based on the Monte Carlo Markov chain sampling scheme to invert magnetic anomaly data over Australia considering independent estimates of the bottom of the magnetic layer from heat flow estimates. The approach integrates the ‘fractal’ description used in the spectral approaches by a Matérn covariance function and point constraints derived from heat flow data. In our inversion, we simultaneously solve for the susceptibility distribution and the thickness of the magnetic layer.
As input magnetic field, we combine the aeromagnetic data of Australia with the recent satellite magnetic model, LCS-1, by a regional spherical harmonic method based on a combination of an equivalent diploe layer and spherical harmonic analysis. The data are presented in various heights from 10 – 400 km in order to minimize local scale features and to maximize sensitivity to the thickness of the magnetic layer. As constraint, we use estimates of the magnetic layer based on measurements of geothermal heat flow and crustal rock properties. Hereby, we assume that the Curie isotherm does coincide with the deepest magnetic layer. We systematically explore the effect of increasing model resolution and of the geothermal heat flow values. Hereby, we consider the spatial distribution of geothermal heat flow values and consider their accuracy and quality. First result show, that if not sufficient constraints are provided, the inversion cannot outperform simple interpolation. However, we also study how heat flow constraints from seismic tomography models can complement the geothermal heat flow constraints.