期刊
NEAR SURFACE GEOPHYSICS
卷 20, 期 6, 页码 623-636出版社
WILEY
DOI: 10.1002/nsg.12203
关键词
Integration; Gravity; Inversion; Magnetic; Modelling
资金
- University of Utah Consortium for Electromagnetic Modeling and Inversion and TechnoImaging
The inversion of geophysical data is a complex ill-posed problem due to uncertainty and ambiguity. Cooperative inversion, based on multiple geophysical data, can reduce this uncertainty by considering a shared earth model. This paper introduces a new approach to cooperative geophysical inversion without the need for prior knowledge of empirical or statistical relationships. The method uses a joint minimum entropy stabilizer to produce a consistent image of the same geological structure expressed in different geophysical data.
The inversion of geophysical data is a classical ill-posed problem that is complicated by considerable uncertainty and ambiguity in the resulting inverse models. One way to reduce this uncertainty is based on the cooperative inversion of multiphysics data. In most cases, the information provided by different geophysical data is mutually complementary, making it natural to consider a cooperative (joint) inversion of different geophysical data to a shared earth model. Many existing joint inversion methods are based on the known relationships between the different physical properties of the rocks. This paper introduces a new approach to cooperative geophysical inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different models' parameters. Our approach is based on a novel joint minimum entropy stabilizer, which forces the simplest multiphysics solution that fits the multimodal data. This novel stabilizer characterizes the degree of joint disorder or uncertainty in the distribution of the different model parameters. By minimizing this stabilizing functional in the framework of the regularized inversion, we produce a consistent image of the same geological structure expressed in different geophysical data. We implement a joint minimum entropy stabilizer in the context of re-weighted regularized conjugate gradient inversion. The paper demonstrates the developed method using a synthetic model study and by joint inversion of airborne gravity and magnetic data collected in the McFaulds Lake area of Ontario, Canada.
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