High-Efficiency Gravity Data Inversion Method Based on Locally Adaptive Unstructured Meshing

Ma, Guoqing; Niu, Runxin; Gao, Tong; Li, Lili; Wang, Taihan; Meng, Qingfa

doi:10.1109/tgrs.2022.3142042

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…In this paper, we mainly use the BTTB-FFT algorithm to improve the efficiency; of course, parallel computation can also be applied on this basis to further improve the efficiency. In addition, when the large regional data inversion is carried out, the large observation surface undulation may affect the inversion, so the inversion strategy that considers the observation surface undulation is more consistent with the real situation [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature

Ma,

Meng,

2024

Remote Sensing

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The three-dimensional magnetic vector structure (magnetization intensity and direction) of the planet can be effectively used to analyze the characteristics of its formation and operation. However, the quick acquisition of a large region of the magnetic vector structure of the planet with bigger observation surfaces undulation is hard and indispensable. We firstly proposed a fast magnetization vector inversion method for the inversion of a magnetic anomaly with the undulating observation surfaces in the spherical coordinate system, which first transforms the data to a plane when the data are distributed on a surface. Then, it uses a block-Toeplitz-Toeplitz-block (BTTB)-FFT to achieve fast inversion with the constraint that the magnetization intensities of the grids between the transformed observation surfaces and the terrain are zero. In addition, Gramian constraint term is used to reduce the ambiguity of the magnetic vector inversion. The theoretical model tests show that the proposed method can effectively improve the computational efficiency by 23 times in the 60 × 60 × 10 grid division compared to the conventional inversion method, and the accuracy of the two computation methods is comparable. The root-mean-square error of the magnetization intensity is only 0.017, and the angle error is within 1°. The magnetization vector structure shows that the largest crater diameter does not exceed 340 km in the Mare Australe region, the amplitude of the magnetic anomaly is much higher than the current meteorite impact simulation results, and the depth of the magnetic source is less than 10 km, which cannot be explained by the impact simulation experiments. In addition, the magnetization directions of adjacent sources differ by 122° (or 238°), and the high-frequency dynamics of the Moon as well as the short-lived dynamics may be responsible for this phenomenon. The magnetization directions of the three adjacent sources in the Mare Crisium region are close to each other and differ in depth with different cooling times, making it difficult to record the transient fields produced by meteorite impacts. In addition to the above characteristics, the magnetization direction of the magnetic sources in both regions is uniformly distributed without reflecting the dispersion of the magnetization direction of the meteorite impact magnetic field. Therefore, it can be inferred that the magnetic anomalies in these two regions are related to the generator hypothesis.

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Section: Introductionmentioning

confidence: 99%

Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature

Ma,

Meng,

2024

Remote Sensing

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“…( 2) Selecting an efficient optimization method to solve the inverse problem corresponding to the method equation. Traditional methods used to solve the equation of the method include Conjugate Gradient (CG) method (Pilkington, 1997;Chen et al, 2012;Chen et al, 2014;Gao & Huang, 2017;Ma et al, 2022), Gauss-Newton method (Sun & Li, 2017;Peng Guomin & Liu Zhan, 2022), and Quasi-Newton method (Qin Pengbo & Huang Danian, 2016). Here, the CG method is an iterative algorithm for solving linear equations.…”

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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Joint Inversion Method of Gravity and Magnetic Analytic Signal Data With Adaptive Unstructured Tetrahedral Subdivision

Niu,

Ma,

Wang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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High-Efficiency Gravity Data Inversion Method Based on Locally Adaptive Unstructured Meshing

Cited by 6 publications

References 25 publications

Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature

Fast Magnetization Vector Inversion Method with Undulating Observation Surface in Spherical Coordinate for Revealing Lunar Weak Magnetic Anomaly Feature

3D Gravity Fast Inversion Based on Krylov Subspace Methods

Joint Inversion Method of Gravity and Magnetic Analytic Signal Data With Adaptive Unstructured Tetrahedral Subdivision

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