Gravity Inversion of Blocky Basement Relief Using L0 Norm Constraint with Exponential Density Contrast Variation

Feng, Xuliang; Liu, Shengrong; Guo, Renhua; Wang, Pengfei; Zhang, Jinai

doi:10.1007/s00024-020-02423-1

Cited by 13 publications

(1 citation statement)

References 33 publications

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“…The compactness stabilizer (Last & Kubik, 1983) also known as the minimum support stabilizer (Port-niaguine & Zhdanov, 1999) has been borrowed and implemented by other researchers in various geophysical inversion methods (Ajo-Franklin et al, 2007;Stocco et al, 2009;Fei et al, 2018;Feng et al, 2020;Varfinezhad et al, 2020). As it was demonstrated by a number of researchers (Zhdanov & Tolstaya, 2004;Feng et al, 2020;Varfinezhad et al, 2022), this stabilizer is known to yield a compact or focused geophysical model with sharp boundaries. Apart from the inversion methods which produce focused images mentioned above, sparse geophysical inversion approaches derived from L p -norm (0 ≤ p ≤ 1) stabilization have been developed by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gebre

Lewi

2022

Preprint

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Abstract. We present a gravity inversion method that can produce compact and sharp images, to assist the modeling of non-smooth geologic features. The proposed iterative inversion approach makes use of L0-norm stabilizing functional, hard, and physical parameter inequality constraints, and depth weighting function. The method incorporates an auto-adaptive regularization technique, which automatically determines a suitable regularization parameter and error weighting function that helps to improve both the stability and convergence of the method. The auto-adaptive regularization and error weighting matrix are not dependent on the known noise level. Because of that, the method yields reasonable results even the noise level of the data is not known properly. The utilization of an effectively combined stopping rule to terminate the inversion process is another improvement that is introduced in this work. The capacity and the efficiency of the new inversion method were tested by inverting randomly chosen synthetic and measured data. The synthetic test models consist of multiple causative blocky bodies, with different geometries and density distributions that are vertically and horizontally distributed adjacent to each other. Inversion results of the synthetic data show that the developed method can recover models that adequately match the real geometry, location, and densities of the synthetic causative bodies. Furthermore, the testing of the improved approach using published real gravity data confirmed the potential, and practicality of the method in producing compact and sharp inverse images of the subsurface.

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Section: Introductionmentioning

confidence: 99%

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gebre

Lewi

2022

Preprint

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2.5D Regularized Gravity Data Inversion for the Detection of Faults in Basement Rocks

Feng

Yang

et al. 2023

Pure Appl. Geophys.

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L0-norm gravity inversion with new depth weighting function and bound constraints

Gebre

Lewi

2022

Acta Geophys.

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Gravity Inversion of Blocky Basement Relief Using L0 Norm Constraint with Exponential Density Contrast Variation

Cited by 13 publications

References 33 publications

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

2.5D Regularized Gravity Data Inversion for the Detection of Faults in Basement Rocks

L0-norm gravity inversion with new depth weighting function and bound constraints

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Gravity Inversion of Blocky Basement Relief Using L0 Norm Constraint with Exponential Density Contrast Variation

Cited by 13 publications

References 33 publications

Gravity Inversion Method to Produce Compact and Sharp Images Using L0-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gravity Inversion Method to Produce Compact and Sharp Images Using L0-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

2.5D Regularized Gravity Data Inversion for the Detection of Faults in Basement Rocks

L0-norm gravity inversion with new depth weighting function and bound constraints

Contact Info

Product

Resources

About

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria

Gravity Inversion Method to Produce Compact and Sharp Images Using L₀-norm Constraint with Auto-adaptive Regularization and Combined Stopping Criteria