Cooperative inversion of multiphysics data using joint minimum entropy constraints. Issue 6 (26th March 2022)

• Record Type: Journal Article
• Authors: Zhdanov, Michael S.; Tu, Xiaolei; Čuma, Martin
• Other Names: Zhdanov Michael S. guestEditor.; Apostolopoulos George guestEditor.; DiFrancesco Dan guestEditor.
• Is Part Of: Near surface geophysics. Volume 20:Issue 6(2022)
• Page Start: 623
• Page End: 636
• Publication Date: 2022-03-26
• Subjects: Integration -- Gravity -- Inversion -- Magnetic -- Modelling; Earth (Planet) -- Surface -- Periodicals; Geophysics -- Technique -- Periodicals; Engineering geology -- Periodicals; Geophysics -- Periodicals; Planets -- Surfaces; Engineering geology; Geophysics -- Technique; Geophysics; Earth (Planet); Periodicals; 550
• DOI: 10.1002/nsg.12203
• Languages: English
• ISSNs: 1569-4445

Abstract

ABSTRACT: 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.