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246 results on '"Ren, Zhengyong"'

6. Exact gravity field for polyhedrons with polynomial density contrasts of arbitrary orders

Author

Ren, Zhengyong, Chen, Chaojian, Zhong, Yiyuan, Chen, Huang, Kalscheuer, Thomas, Maurer, Hansruedi, Tang, Jingtian, and Hu, Xiangyun

Subjects
Physics - Geophysics
Abstract

Computing gravity field of a mass body is a core routine to image anomalous density structures in the Earth. In this study, we report the existence of analytical routines to accurately compute the gravity potential and gravity field of a general polyhedral mass body. The density contrasts in the polyhedral body can be general polynomial functions up to arbitrary non-negative orders and also can vary in both horizontal and vertical directions. The newly derived analytical expressions of gravity fields are also singularity-free which means that observation sites can have arbitrary geometric relationships with polyhedral mass bodies. One synthetic prismatic body with different density contrasts is used to verify the accuracies of our new closed-form solutions. Excellent agreements are obtained among our solutions and other published solutions. Our work is the first-of-its kind to completely answer the fundamental question on existence of analytic solutions of gravitational field for general mass bodies. It may put an end of searching closed-form solutions in gravity surveying., Comment: 16 pages, 2 figures and one long appendix

Published
2018

7. 3-D parallel anisotropic inversion of controlled-source electromagnetic data using nested tetrahedral grids.

Author

Ren, Zhengyong, Liu, Zhengguang, and Tang, Jingtian

Subjects
*OPTIMIZATION algorithms, *FINITE element method, *ELECTROMAGNETIC theory, *MESSAGE passing (Computer science), *ELECTRIC conductivity
Abstract

Geophysicists today face the challenge of quickly and reliably interpreting extensive controlled-source electromagnetic (CSEM) data sets to map subsurface conductivity structures within realistic geological environments. An ideal 3-D CSEM inversion algorithm using tetrahedral grids should be capable of distinguishing different resolution requirements between forward modelling and inversion grids, have an optimal parallel strategy that fully exploits the inherent independence of CSEM data sets while also possessing the capability to handle large-scale geo-electrical models, and incorporate conductivity anisotropy which should be a common characteristic in realistic subsurface environments. However, existing tools in the geo-electromagnetic community often fall short of these three demands. Addressing this gap, our study introduces a scalable and parallel anisotropic inversion technique for CSEM data, capitalizing on the potential of unstructured tetrahedral grids. We first apply the tetrahedral longest-edge bisection method to create a refined dense, heterogeneous forward modelling grid from a coarse inversion grid. This refinement, focused on areas around transmitters and receivers, is seamlessly integrated within the coarser inversion grid's topology, enabling precise conductivity mapping and preserving electromagnetic response accuracy during model updates. We further innovate with a source-mesh double-level parallel strategy, utilizing the message passing interface technique for parallel handling of independent CSEM data sets and large-scale geo-electrical models. Externally, we dedicate a processor for inversion model updates employing the Limited-memory Broyden–Fletcher–Goldfarb–Shanno optimization algorithm and divide other processors into groups, each associated with specific transmitting sources and frequencies. Internally, in each group, we employ a domain-decomposition-based scalable and robust iterative solvers using the Auxiliary-Space Maxwell pre-conditioner to parallel quickly calculate the electromagnetic responses from its assigned source-frequency set. Additionally, recognizing the potential for electrical conductivity anisotropy in field data, we incorporate the case of vertical transverse isotropy. We validate the effectiveness of our method through examples, including an isotropic land model with undulating topography, an anisotropic marine model and a real-field data case. Results from both synthetic and field data inversions underscore our method's significant advancements in efficiency and practicality, particularly in addressing large-scale 3-D CSEM data sets inversion challenges in realistic geological environments. [ABSTRACT FROM AUTHOR]

Published
2024
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30. A 3-D Magnetotelluric Inversion Method Based on the Joint Data-Driven and Physics-Driven Deep Learning Technology

Author

Ling, Weiwei, Pan, Kejia, Zhang, Jiajing, He, Dongdong, Zhong, Xin, Ren, Zhengyong, and Tang, Jingtian

Abstract

The conventional magnetotelluric (MT) inversion method is subject to the influence of the initial model, which leads to an unstable inversion process and a tendency to get trapped at local optima. In contrast, deep learning technology relies on its powerful nonlinear fitting capability and can construct complex nonlinear mappings directly from observation data (input) to model (output). In recent years, it has received extensive attention from researchers. Due to the difficulties in creating a sufficiently large dataset and performing extensive neural network training, most current MT inversion methods for geophysical exploration remain limited to 1-D or 2-D scenarios. To the best of our knowledge, for deep learning-based 3-D MT inversion, currently there is no reported work in the literature. In this work, we propose a 3-D MT inversion method based on deep learning technology. By designing a neural network architecture for 3-D structures (MT3D-Net), we achieve an end-to-end mapping from the network input to output. To alleviate the excessive dependence of the network on the training set, we introduce a joint weighted loss function based on data-driven and physics-driven method, allowing the network to follow the physical constraints of MT data during the training process and thus more reasonably guide the update of network parameters. Numerical experiments show that this method combines the advantages of the traditional and data-driven inversions, significantly improving the stability and accuracy of MT inversion. The proposed method has been successfully applied to synthetic models and measured field data, and it has good application prospects.

Published
2024
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33. A three-dimensional magnetic data inversion algorithm using a prior location and boundary estimation

Author

Ren, Zhengyong and Zhang, Yuanlei

Abstract

The magnetic exploration method is a crucial geophysical technique for metal ore, which uses anomalous magnetic signals (magnetic field or magnetic gradient tensor) caused by magnetic targets. However, 3D magnetic inversion algorithms still face the issue of multiple solutions and low reliability. In this study, we propose a method to construct an initial and prior inversion model to improve the inversion. Firstly, the boundary recognition method is used to determine the approximate horizontal range of anomalous bodies. Then using the magnetic source location estimation algorithm, the approximate horizontal and vertical region of anomalous bodies is determined as well as the average magnetic susceptibility. The initial and prior model constructed by combining these two algorithms is closer to the unknown and true model, thus the convergence rate of the iterative inversion might be improved. In addition, only the magnetic susceptibility of elements in the recognition range is updated during the iterative inversion process. This magnetic inversion method can reduce the number of unknowns, improves the computational efficiency of inversion, and a well-estimated initial model can also improve the reliability of the recovered model. In the end, synthetic and field data sets will be used to verify its reliability, stability, and practicability., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Published
2023

40. 3-D Magnetotelluric Adaptive Finite-Element Modeling

Author

Liu, Changsheng, Yu, Yan, Ren, Zhengyong, Wu, Qi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cai, Zhihua, editor, Hu, Chengyu, editor, Kang, Zhuo, editor, and Liu, Yong, editor

Published
2010
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45. Featured Cover

Author

Wu, Xiaoxin, primary, Pan, Kejia, additional, Li, Jin, additional, Yu, Yunlong, additional, Yuan, Guangwei, additional, and Ren, Zhengyong, additional

Published
2022
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