Fast 3D inversion of airborne gravity-gradiometry data using Lanczos bidiagonalization method

Meng, Zhaohai; Li, Fengting; Zhang, Dailei; Xu, Xuechun; Huang, Danian

doi:10.1016/j.jappgeo.2016.07.013

Cited by 9 publications

(5 citation statements)

References 56 publications

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“…Moreover, it is a predictive statistics-based method that does not require a Algorithm 1 sparsity inversion using the iterative SVD and modified Newton methods priori estimates of the error norm. The advantages of the W-GCV method have been investigated in many domains (Golub and Wahba, 1979;Golub and Von Matt, 1997;Hansen, 2005;Chung et al, 2008;Gholami and Siahkoohi, 2010;Meng et al, 2016).…”

Section: Choice Of the Regularization Parametermentioning

confidence: 99%

“…Therefore, a series of compression methods are introduced to reduce the dimension of large scale gravity, which resolves the application of SVD algorithm in large scale data inversion. The efficient methods have been studied, such as the frequency domain conversion (Li and Oldenburg, 2003), the symmetry of gravity forward model (Boulanger and Chouteau 2001), and the Lanczos bidiagonalization compression (Chan et al, 2005;Chung et al, 2008;Abedi et al, 2013;Toushmalani and Saibi, 2015;Voronin et al, 2015;Meng et al, 2016). Here, Lanczos bidiagonalization compression is an efficient algorithm, which has been studied and applied in many researches.…”

Section: Introductionmentioning

confidence: 99%

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The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

Meng

Wang²,

Li³

et al. 2023

Front. Earth Sci.

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Inversion of gravity data is one the important steps in the interpretation of practical data. The detection of sharp boundaries between anomalous bodies and host rocks is an interesting point in the geological frameworks. The gravity inversion with sparsity constraint is a useful method to recover block subsurface density distribution, which is efficiently used for the quantitative interpretation of gravity data. The reweighted regularized method is a useful method to solve the inverse problem. However, in this type, we must face the updating gravity forward matrix and large matrix operation. The application of Lanczos bidiagonalization method can reduce the size of data and matrix in the inversion to resolve the large scale inversion problem. However, a very important problem is not resolved, which is update of reweighted forward matrix and new Lanczos bidiagonalization matrix. Here, an adaptive Lanczos bidiagonalization method is studied to select the Lanczos bidiagonalization factor. And a new projected method with adaptive Lanczos bidiagonalization method is study to avoid the updating sparsity reweighted function. We calculate the reweighted forward matrix and Lanczos bidiagonalization matrix only one time, which can essentially reduce the computational complexity. The inversion results of synthetic data show that the new improved method is faster and better than common reweight regularized Lanczos bidiagonalization method to produce an acceptable solution for focusing inverse problem. The improvement of adaptive Lanczos bidiagonalization in sparsity gravity inversion is also tested on gravity data collected over the Mobrun massive sulfide ore body in Noranda, Quebec, Canada. The inversion results indicate a remarkable correlation with true structure of the ore body that is achieved from drilling data.

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Section: Choice Of the Regularization Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

Meng

Wang²,

Li³

et al. 2023

Front. Earth Sci.

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“…A detailed analysis of the CPU time performance as a function of the number of processors can be found in [28]. The interested reader can find another example of large-scale gravity and magnetic field modeling in [29,30].…”

Section: Geophysical Data Inversionmentioning

confidence: 99%

Large-Scale 3D Modeling and Inversion of Multiphysics Airborne Geophysical Data: A Case Study from the Arabian Shield, Saudi Arabia

et al. 2018

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Recent developments in large-scale geophysical inversions made it possible to invert the results of entire airborne geophysical surveys over large areas into 3D models of the subsurface. This paper presents the methods for and results of the interpretation of the data acquired by a multiphysics airborne geophysical survey in Saudi Arabia. The project involved the acquisition, processing, and interpretation of airborne electromagnetic, gravity, and magnetic geophysical data over an 8000 square kilometer area. All the collected data were carefully analyzed and inverted in 3D models of the corresponding physical properties of the subsurface, including 3D density, magnetization vector, and conductivity models. This paper summarizes the interpretation of all geophysical data sets collected during the field airborne survey. The goal of the paper is to demonstrate how the advanced 3D modeling and inversion methods can be effectively used for interpretation of multiphysics airborne survey data and to study and analyze the potential of the survey area for natural resource exploration in Saudi Arabia.

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“…Foks et al [36] developed an adaptive down-sampling method to reduce the number of observed data points and achieve data space compression. In addition, the fast gravity inversion method based on singular value decomposition [37] and Lanczos double diagonalization [38][39][40] have also had beneficial effects on large-scale data calculations.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression

et al. 2020

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Three-dimensional magnetic inversion allows the distribution of magnetic parameters to be obtained, and it is an important tool for geological exploration and interpretation. However, because of the redundancy of the data obtained from large-scale investigations or high-density sampling, it is very computationally intensive to use these data for iterative inversion calculations. In this paper, we propose a method for compressing magnetic data by using an adaptive quadtree decomposition method, which divides the two-dimensional data region into four quadrants and progressively subdivides them by recursion until the data in each quadrant meets the regional consistency criterion. The method allows for dense sampling at the abnormal boundaries with large amplitude changes and sparse sampling at regions with small amplitude changes, and achieves the best approximation to the original data with the least amount of data, thus retaining more anomalous information while achieving the purpose of data compression. In addition, assigning values to the data in the quadrants using the averaging method is essentially equivalent to average filtering, which reduces the noise of the magnetic data. Testing the synthetic model and applying the method to mineral exploration a prove that it can effectively compress the magnetic data and greatly improve the computational efficiency.

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Fast 3D inversion of airborne gravity-gradiometry data using Lanczos bidiagonalization method

Cited by 9 publications

References 56 publications

The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

The improvement of sparsity gravity inversion using an adaptive lanczos bidiagonalization method

Large-Scale 3D Modeling and Inversion of Multiphysics Airborne Geophysical Data: A Case Study from the Arabian Shield, Saudi Arabia

Three-Dimensional Magnetic Inversion Based on an Adaptive Quadtree Data Compression

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