Sequential Joint inversion of gravity and magnetic data via the cross gradient constraint

Author(s):  
M. Tavakoli ◽  
A. Nejati Kalateh ◽  
M. Rezaie ◽  
L. Gross ◽  
M. Fedi
2020 ◽  
Vol 224 (2) ◽  
pp. 1344-1359
Author(s):  
Zhengwei Xu ◽  
Guangui Zou ◽  
Qianqian Wei ◽  
Junqi Tian ◽  
Hemin Yuan

SUMMARY This paper develops a minimum-support focusing stabilizer to perform a joint inversion of the vertical components of gravity and magnetic data using fuzzy c-means clustering (FCM) with the regularized Newton method in a space of weighted parameters. Not only does this joint inversion technology arrive at the conditionally well-posed traditional potential field inversion, but it also increases the structural correlation between multiple inverted models. The FCM and the focusing stabilizer make it possible to balance the convergence of the data space (D) and the model space (M), guiding multimodal geophysical parameters toward assigned petrophysical values, which makes the results more stable and realistic. Two model studies are presented to illustrate the method, a simple synthetic model with two rectangular bodies in a homogenous background and a realistic model of the Volcanogenic Massive Sulfide (VMS) deposits in northeastern New Brunswick, Canada. These models demonstrate that the new focusing joint inversion algorithm produces better images than traditional methods because the FCM function uses the structural correlation of density contrast and magnetic susceptibility as constraints.


2019 ◽  
Author(s):  
Zhengwei Xu* ◽  
Guangui Zou ◽  
Jiang Wang ◽  
Junqi Tian ◽  
Yue Mao ◽  
...  

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. L31-L42 ◽  
Author(s):  
Emilia Fregoso ◽  
Luis A. Gallardo

We extend the cross-gradient methodology for joint inversion to three-dimensional environments and introduce a solution procedure based on a statistical formulation and equality constraints for structural similarity resemblance. We apply the proposed solution to the joint 3D inversion of gravity and magnetic data and gauge the advantages of this new formulation on test and field-data experiments. Combining singular-value decomposition (SVD) and other conventional regularizing constraints, we determine 3D distributions of the density and magnetization with enhanced structural similarity. The algorithm reduces some misleading features of the models, which are introduced commonly by conventional separate inversions of gravity and magnetic data, and facilitates an integrated interpretation of the models.


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