scholarly journals Simultaneous Structure-Coupled Joint Inversion of Gravity and Magnetic Data Based on a Damped Least-Squares Technique

2015 ◽  
Vol 06 (02) ◽  
pp. 172-179 ◽  
Author(s):  
Junjie Zhou ◽  
Chunxiao Xiu ◽  
Xingdong Zhang
Keyword(s):  
Least Squares ◽  
Joint Inversion ◽  
Magnetic Data ◽  
Gravity And Magnetic ◽  
Damped Least Squares ◽  
Gravity And Magnetic Data

Joint inversion of gravity and magnetic data for two-layer models

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. L35-L42 ◽  
Author(s):  
Mark Pilkington
Keyword(s):  
Least Squares ◽  
Joint Inversion ◽  
Physical Property ◽  
Magnetic Data ◽  
Model Parameters ◽  
Constant Density ◽  
Central Uplift ◽  
Gravity And Magnetic ◽  
Damped Least Squares ◽  
Gravity And Magnetic Data

Gravity and magnetic data are inverted jointly in terms of a model consisting of an interface separating two layers having a constant density and magnetization contrast. A damped least-squares inversion is used to determine the topography of the interface. The inversion requires knowledge of the physical property contrasts across the interface and its average depth. Since the relationship between model parameters and data is weakly nonlinear, a constant damped least-squares inverse is used during the iterative solution search. The effect of this inverse is closely related to a downward continuation of the field to the average interface depth. The method is used to map the base of the Sept-Iles mafic intrusion, Quebec, Canada, and the shape of the central uplift at the Chicxulub impact crater, Yucatan, Mexico. At Sept-Iles, the intrusion reaches a thickness of [Formula: see text], coincident with the maximum gravity anomaly, south of the intrusion center. At Chicxulub, the top of the central uplift is modeled to be [Formula: see text] deep and has a single peak form.

Focusing joint inversion of gravity and magnetic data using a clustering stabilizer in a space of weighted parameters

2020 ◽  
Vol 224 (2) ◽  
pp. 1344-1359
Author(s):  
Zhengwei Xu ◽  
Guangui Zou ◽  
Qianqian Wei ◽  
Junqi Tian ◽  
Hemin Yuan
Keyword(s):  
Joint Inversion ◽  
Model Space ◽  
Massive Sulfide ◽  
Realistic Model ◽  
Magnetic Data ◽  
Inversion Algorithm ◽  
Model Studies ◽  
Structural Correlation ◽  
Gravity And Magnetic ◽  
Gravity And Magnetic Data

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.

Focusing Joint Inversion of Gravity and Magnetic Data using Weighted Fuzzy Clustering

2019 ◽  
Author(s):  
Zhengwei Xu* ◽  
Guangui Zou ◽  
Jiang Wang ◽  
Junqi Tian ◽  
Yue Mao ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Joint Inversion ◽  
Magnetic Data ◽  
Gravity And Magnetic ◽  
Gravity And Magnetic Data

Cross-gradients joint 3D inversion with applications to gravity and magnetic data

Geophysics ◽  
2009 ◽  
Vol 74 (4) ◽  
pp. L31-L42 ◽  
Author(s):  
Emilia Fregoso ◽  
Luis A. Gallardo
Keyword(s):  
Joint Inversion ◽  
Three Dimensional ◽  
Structural Similarity ◽  
Solution Procedure ◽  
Magnetic Data ◽  
3D Inversion ◽  
Gravity And Magnetic ◽  
New Formulation ◽  
Value Decomposition ◽  
Gravity And Magnetic Data

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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