Fast 3D Focusing Inversion of Gravity Data Using Reweighted Regularized Lanczos Bidiagonalization Method

Rezaie, Mohammad; Moradzadeh, Ali; Kalate, Ali Nejati; Aghajani, Hamid

doi:10.1007/s00024-016-1395-8

Cited by 25 publications

(8 citation statements)

References 39 publications

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“…Due to algorithmic stability and smooth results, Occam's-style algorithms are used to solve Equations ( 45) and (47). The RRCG is the most widely used solver in focusing inversion and can obtain better depth resolution [55,80]. Considering the above two points, following Zhdanov [81,82], the RRCG algorithm is used as the solver for the multilevel algorithm in this paper.…”

Section: Algorithm Detailsmentioning

confidence: 99%

“…Therefore, in order to avoid the overfocused phenomenon, in the focusing inversion algorithm verification, the upper density bound is usually the maximum value of the model parameters, and the lower density bound is the background value of the model [37]. In practical applications, geological data or human judgment has often been used to determine the upper and lower density bounds [50,55]. Essentially, each method has a different physical basis.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Algorithm for Large-Scale Gravity Inversion

Cao,

Chen,

et al. 2024

Symmetry

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Surface gravity inversion attempts to recover the density contrast distribution in the 3D Earth model for geological interpretation. Since airborne gravity is characterized by large data volumes, large-scale 3D inversion exceeds the capacity of desktop computing resources, making it difficult to achieve the appropriate depth/lateral resolution for geological interpretation. In addition, gravity data are finite and noisy, and their inversion is ill posed. Especially in the absence of a priori geological information, regularization must be introduced to overcome the difficulty of the non-uniqueness of the solutions to recover the most geologically plausible ones. Because the use of Haar wavelet operators has an edge-preserving property and can preserve the sensitivity matrix structure at each level of the multilevel method to obtain faster solvers, we present a multilevel algorithm for large-scale gravity inversion solved by the re-weighted regularized conjugate gradient (RRCG) algorithm to reduce the inversion computational resources and improve the depth/lateral resolution of the inversion results. The RRCG-based multilevel inversion was then applied to synthetic cases and airborne gravity data from the Quest-South project in British Columbia, Canada. Results from synthetic models and field data show that the RRCG-based multilevel inversion is suitable for obtaining density contrast distributions with appropriate horizontal and vertical resolution, especially for large-scale gravity inversions compared to Occam’s inversion.

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Section: Algorithm Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Algorithm for Large-Scale Gravity Inversion

Cao,

Chen,

et al. 2024

Symmetry

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show abstract

“…Therefore, the low resolution of the smoothed inversion method increases the difficulty of subsequent interpretation, especially in mineral exploration. [8,9].…”

Section: Introductionmentioning

confidence: 99%

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

Ming,

Ma,

Wang

et al. 2024

Remote Sensing

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The inversion of gravity and magnetic data can obtain the density and magnetic structure of underground space, which provide important information for resource exploration and geological structure division. The most commonly used inversion method is smooth inversion in which the objective function is built with L2-norm, which has good stability, but it produces non-focused results that make subsequent interpretation difficult. The power-type structural self-constrained inversion (PTSS) method with L2-norm is proposed to improve the resolution of smooth inversion. A self-constraint term based on the power gradient of the results is introduced, which takes advantage of the structural feature that the power gradient can better focus on the model boundary to improve the resolution. For the joint inversion of gravity and magnetic data, the power-type mutual-constrained term between different physical structures and the self-constrained term can be simultaneously used to obtain higher-resolution results. The modeling tests demonstrated that the PTSS method can produce converged high-resolution results with good noise immunity in both the respective inversions and the joint inversion. Then, the PTSS joint inversion was applied to the airborne gravity and magnetic data of the iron ore district in Shandong, revealing the shape and location of the mineralized rock mass, which are crucial information for subsequent detailed exploration.

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“…Li et al (2016) introduced the algebraic reconstruction algorithm that avoids the inverse of the overall coefficient matrix, but the algorithm requires prior information or constraint information in order to provide the initial value. Rezaie et al (2017aRezaie et al ( , 2017b adopted the double diagonalized Lancozs method to solve the optimization problem of gravity inversion. They could effectively reduce the number of iterations to some extent and improved the computational efficiency of inversion.…”

Section: Introductionmentioning

confidence: 99%

3D Gravity Fast Inversion Based on Krylov Subspace Methods

Yang¹,

Xu²,

Wang

et al. 2023

Preprint

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Mapping the density contrast through the 3D gravity inversion can help detect the goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. The Krylov subspace method is widely used to solve large linear problems because of its small storage capacity and high computational efficiency. In this study, two classical algorithms of Krylov subspace method, i.e., the Generalized Minimum Residual method and the Conjugate Gradient method, are applied to 3D gravity inversion. Based on the results of the deep mineral model and the shallow L-shaped tunnel model tests, the Generalized Minimum Residual method was found to provide density contrast results similar to the Conjugate Gradient method. The distribution position of the density contrast obtained by inversion corresponds well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D underground distribution of Fe content is obtained by performing inversion of the measured gravity data of the Olympic Dam in Australia, and the obtained results correspond well with the distribution position of Fe content in the collected geological profile. Under the same conditions, the inversion accuracy of the Generalized Minimum Residual method is similar to that of the Conjugate Gradient method. However, the Generalized Minimum Residual method has faster convergence speed than the Conjugate Gradient method, and the inversion efficiency is increased by about 90%, which greatly reduces the inversion time and improves the inversion efficiency.

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Fast 3D Focusing Inversion of Gravity Data Using Reweighted Regularized Lanczos Bidiagonalization Method

Cited by 25 publications

References 39 publications

Multilevel Algorithm for Large-Scale Gravity Inversion

Multilevel Algorithm for Large-Scale Gravity Inversion

Power-Type Structural Self-Constrained Inversion Methods of Gravity and Magnetic Data

3D Gravity Fast Inversion Based on Krylov Subspace Methods

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