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

Gebre, Mesay Geletu; Lewi, Elias

doi:10.1007/s11600-022-00806-7

Cited by 5 publications

(4 citation statements)

References 67 publications

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“…Depth weighting is used and its effect is investigated by different authors (Pilkington, 2008;Commer, 2011). Based on Gebre & Lewi (2022), the recently proposed depth weighting function is given as follows:…”

Section: Depth Weightingmentioning

confidence: 99%

“…The problem with Lewi (1997, p. 89) method arises when dealing with a multiple-source model, where the inversion algorithm tends to concentrate densities towards the surface regardless of the true depth of the causative bodies. In overcoming this drawback, Gebre & Lewi (2022) improved the compact gravity inversion method by incorporating a new depth weighting function. In this paper, we present a gravity inversion method that can produce compact and sharp images, to assist the modeling of non-smooth, blocky geologic features with sharp boundaries.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present a gravity inversion method that can produce compact and sharp images, to assist the modeling of non-smooth, blocky geologic features with sharp boundaries. The proposed approach is based on the authors previous work (Gebre & Lewi, 2022), to which the reader is referred for further details, with the following two main differences and advancements. The first is proposing and incorporating an auto-adaptive regularization and error weighting function.…”

Section: Introductionmentioning

confidence: 99%

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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: Depth Weightingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Vitale and Fedi [18] extended this hypothesis to fields with complex structures and obtained depth-weighted indices from multi-scale field uniformity estimates. Gebre et al [19] proposed a new depthweighting function that can automatically determine the value of the β parameter using standard optimization methods. A spherical coordinate inversion method based on hybrid regularization and the depth-weighting function has also been put forward [20].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Space–Location-Weighting Function Method for High-Precision Density Inversion of Gravity Data

Ma,

Niu,

et al. 2023

Remote Sensing

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Underground 3D density variation can be obtained via the inversion of gravity data, which is a very important basis for structural division, oil and gas structure definition, and mineral resource evaluation. A depth-weighting function is usually introduced as a structural constraint in density inversion to solve the skin effect. We propose an adaptive space–location-weighting (ASW) function for gravity field data to improve the resolution of the inversion, which adds the position and depth information provided by the DEXP method to form a new weighting function. The weighting function is partitioned according to the horizontal distribution of the source and can effectively improve the resolution of field sources with different positions and different depths. The results of model tests have shown that the ASW function method can significantly improve the precision and resolution of density inversion results and has good noise immunity. The ASW method was applied to interpret the real gravity data of a mining area in Shandong, and we speculated potential mineralization based on the inversion results, which corresponded well with the logging results.

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Efficient 3-D Gravity Data Inversion With Depth Weighting Function

Wang,

Li,

Zhang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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

Cited by 5 publications

References 67 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

Adaptive Space–Location-Weighting Function Method for High-Precision Density Inversion of Gravity Data

Efficient 3-D Gravity Data Inversion With Depth Weighting Function

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

Cited by 5 publications

References 67 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

Adaptive Space–Location-Weighting Function Method for High-Precision Density Inversion of Gravity Data

Efficient 3-D Gravity Data Inversion With Depth Weighting Function

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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