Abstract
Cross-gradients joint inversion of gravity and magnetic data is the focus of this work. Cross-gradients are introduced as a constraint in the minimization of a least square functional including the misfits of the available data. We propose to initialize the cross gradients iterations with a surrogate density model. The latter is constructed by means of Bayesian estimation in a low dimensional parameter space. To sample from the posterior, an affine invariant MCMC is also introduced. The proposed methodology is successfully tested on synthetic models consisting of isolated sources.
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Acknowledgements
This research was carried out while M. A. Moreles was a on sabbatical leave at the mathematics Department of the Universidad de Guadalajara. Their hospitality is greatly appreciated. Also, M. A. Moreles would like to acknowledge the support of ECOS-NORD project number 000000000263116/M15M01. The authors wish to thank the anonymous referees. The manuscript was greatly improved by their very constructive comments.
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Fregoso, E., Palafox, A. & Moreles, M.A. Initializing Cross-Gradients Joint Inversion of Gravity and Magnetic Data with a Bayesian Surrogate Gravity Model. Pure Appl. Geophys. 177, 1029–1041 (2020). https://doi.org/10.1007/s00024-019-02334-w
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DOI: https://doi.org/10.1007/s00024-019-02334-w