3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

Liu, Jinzhao; Liu, Lintao; Liang, Xinghui; Ye, Zhou-Run

doi:10.1007/s11770-015-0491-2

Cited by 10 publications

(3 citation statements)

References 21 publications

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“…Commer [12] proposed a depth-weighting function based on the depth of the top surface of the geological body, which could obtain accurate inversion results. This function was improved to be more accurate in recognizing the range of preset parameters and the location of the filed sources [13]. Imposing hard constraints on physical bounds is essential to recovering a geologically plausible model [14].…”

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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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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“…In this paper, by analyzing the gradient of objective functional, we find that the distribution of density is related to the conventional gradient formulation of objective functional. Subsequently, two mechanisms of depth weighting are compared and analyzed in detail: (1) a regularization-based strategy: applying a depth weighting to the regularization term [9,11,14,16]; and (2) gradient-based strategy: applying a depth weighting to the gradient of data misfit [21,22]. The comparative analysis indicates that the second method would improve gravity depth resolution more directly and effectively, and this improvement is less influenced by regularization parameters.…”

Section: Introductionmentioning

confidence: 99%

3D Gravity Inversion on Unstructured Grids

2021

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Compared with structured grids, unstructured grids are more flexible to model arbitrarily shaped structures. However, based on unstructured grids, gravity inversion results would be discontinuous and hollow because of cell volume and depth variations. To solve this problem, we first analyzed the gradient of objective function in gradient-based inversion methods, and a new gradient scheme of objective function is developed, which is a derivative with respect to weighted model parameters. The new gradient scheme can more effectively solve the problem with lacking depth resolution than the traditional inversions, and the improvement is not affected by the regularization parameters. Besides, an improved fuzzy c-means clustering combined with spatial constraints is developed to measure property distribution of inverted models in both spatial domain and parameter domain simultaneously. The new inversion method can yield a more internal continuous model, as it encourages cells and their adjacent cells to tend to the same property value. At last, the smooth constraint inversion, the focusing inversion, and the improved fuzzy c-means clustering inversion on unstructured grids are tested on synthetic and measured gravity data to compare and demonstrate the algorithms proposed in this paper.

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“…Zhdanov and Lin proposed an adaptive polynomial inversion based on a regularized conjugate gradient inversion method and achieved suitable results [35]. Liu proposed a joint density inversion for gravity gradient data based on the Tikhonov regularization method [36]. The structure constrained joint inversion method is mostly based on cross-gradient function, which is an effective method for the joint inversion of different geophysical data.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Cooperate Density-Integrated Inversion Method of Airborne Gravity and Its Gradient Data

Gao

Liu

et al. 2021

Remote Sensing

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Airborne (or satellite) gravity measurement is a commonly used remote sensing method to obtain the underground density distribution. Airborne gravity gradiometry data have a higher horizontal resolution to shallower causative sources than airborne gravity anomaly, so joint exploration of airborne gravity and its gradient data can simultaneously obtain the anomaly feature of sources with different depths. The most commonly used joint inversion method of gravity and its gradient data is the data combined method, which is to combine all the components into a data matrix as mutual constraints to reduce ambiguity and non-uniqueness. In order to obtain higher resolution results, we proposed a cooperate density-integrated inversion method of airborne gravity and its gradient data, which firstly carried out the joint inversion using cross-gradient constraints to obtain two density structures, and then fused two recovered models into a result through Fourier transform; finally, data combined joint inversion of airborne gravity, and gradient data were reperformed to achieve high-resolution density result using fused density results as a reference model. Compared to the data combined joint inversion method, the proposed cooperate density-integrated inversion method can obtain higher resolution and more accurate density distribution of shallow and deep bodies meanwhile. We also applied it to real data in the mining area of western Liaoning Province, China. The results showed that the depth of the skarn-type iron mine in the region is about 900–1300 m and gives a more specific distribution compared to the geological results, which provided reliable data for the next exploration plan.

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3D density inversion of gravity gradient data using the extrapolated Tikhonov regularization

Cited by 10 publications

References 21 publications

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

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

3D Gravity Inversion on Unstructured Grids

High-Resolution Cooperate Density-Integrated Inversion Method of Airborne Gravity and Its Gradient Data

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