Yunhe Liu

Jilin University

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Changchun Yin

Jilin University

Bo Zhang

Jilin University

Xiuyan Ren

Jilin Province Science and Technology Department

Yang Su

Jilin Province Science and Technology Department

Xiuyan Ren

Jilin University

Changkai Qiu

China Geological Survey

Bin Xiong

East China Normal University

Xinpeng Ma

Jilin Province Science and Technology Department

Luyuan Wang

China Academy of Space Technology

Luyuan Wang

Jilin University

Cooperative Institutions

Jilin University

Jilin Province Science and Technology Department

Ministry of Natural Resources

Chinese Academy of Sciences

Jilin Medical University

China Geological Survey

Chengdu University of Technology

University of Chinese Academy of Sciences

Central South University

University of Science and Technology of China

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Three-dimensional inversion of controlled-source audio-frequency magnetotelluric data based on unstructured finite-element method

Applied Geophysics (2020)

Xiang‐Zhong Chen Yunhe Liu Changchun Yin Changkai Qiu Jie Zhang

Magnetotellurics

Inverse transform sampling

10.1007/s11770-020-0812-z

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Citations (9)

Stochastic inversion of magnetotelluric data using deep reinforcement learning

Geophysics (2021)

Han Wang Yunhe Liu Changchun Yin Jinfeng Li Yang Su

We have adopted a new tool to invert magnetotelluric data for the 1D model based on deep Q-networks (DQN), which works as a stochastic optimization method. By transforming the inversion problem into a Markov decision process, the tool learns by trial and error to find the optimal path for updating the model to fit the observed data. The DQN method converges to the target through different paths (e.g., Bayesian or other stochastic methods) and can partially provide the probability distribution of the inversion results, which can be used for uncertainty estimation. The DQN search space gradually decreases as the learning experience progresses, accelerating the single inversion and approximating the optimal result. To check the effectiveness of the DQN inversion, the five- and eight-layer models were separately designed to test the robustness of the DQN for the initial model and the noise level. A further comparison with Occam’s method and the Bayesian method indicated that our DQN obtained more robust inversion results for data contaminated by different noise levels. The inversion results with the survey data from Zhagaitunuoergong area, Inner Mongolia, China, well reveals the shape of the interface basement.

occam

Robustness

Magnetotellurics

10.1190/geo2020-0425.1

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Citations (8)

INVERSION OF TEM DATA FROM RECTANGULAR LOOP ON A LAYERED EARTH MODEL

Computing Techniques for Geophysical and Geochemical Exploration (2008)

Yunhe Liu

The paper presents the inversion of transient electromagnetic (TEM) sounding data of rectangular loop over layered conductive earths by a model constraint method. The TEM response of rectangular loop is estimated by partitioning large loop into much smaller square loops and integrating the response of each square loop. Inversion is adapted from Occams inversion with both type constraints of the flattest model and the smoothest model. The inversion method is applied to a synthetic layered earth model and field data, and the results show that the model constraint inversion is feasible to rectangular data, and the inversed model is irrelevant with loop shape.

occam

Source

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3D airborne EM anisotropic effect and identification modeling by SE method

ASEG Extended Abstracts (2018)

Xin Huang Changchun Yin Xiaoyue Cao Yunhe Liu Bo Zhang

Spectral-element (SE) method is a kind of higher-order finite-element method based on weighted residual technique; however, the basis functions for SE are polynomial, like Gauss-Lobatto-Legendre (GLL) or Gauss-Lobatto-Chebyshev (GLC) polynomials. Because of its high modeling accuracy and flexibility, it has been successfully used in computational electromagnetism. In this paper, we use the SE method for 3D frequency-domain airborne electromagnetic (AEM) modeling for an anisotropic earth and we take horizontal coplanar and vertical coaxial coil systems as example for the modeling. We first derive the discrete governing equation from Maxwell equations, in which the conductivity tensor is obtained by 3 Euler rotations of a principal conductivity tensor. GLL polynomial is selected as the vector SE basis functions, while GLL integration is applied for calculating matrix elements. A direct solver is used for the solution of the matrix equations system. The modeling accuracy is checked against a semi-analytical solution. Further, we calculate AEM responses for different anisotropic models and demonstrate that SE method canobtain high precision by either increasing SE order or refining meshes, so that it can save computation cost vastly. Numerical results further confirm that the anisotropy of both 3D body and host rock can be identified from the polar plots of ratio of magnetic field components.

Basis function

Matrix (chemical analysis)

10.1071/aseg2018abp048

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Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints

Applied Geophysics (2020)

Man Kai-Feng Changchun Yin Yunhe Liu Xiuyan Ren Siyuan Sun

Induced polarization

occam

10.1007/s11770-020-0832-8

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Citations (5)

Gradient filtering regularization for 3-D MT inversion based on unstructured tetrahedral discretization

Geophysical Journal International (2023)

Zhihao Rong Yunhe Liu Changchun Yin Xinpeng Ma Luyuan Wang

SUMMARY We propose a novel smoothing regularization scheme for 3-D magnetotelluric (MT) inversion based on unstructured tetrahedral discretization. Different from conventional methods that explicitly add smoothing constraints to model parameters, we choose to do the gradient filtering to smooth the model updates in an implicit way. By transforming the model into a constraint domain, the gradient of the objective function for the parameters in the new domain can be taken as a product of transpose of inverse transformation operator and the conventional gradient. Since the transpose of inverse transformation is designed to be an inverse distance interpolation operator for each tetrahedron, the data fitting term in the gradient can be smoothed in a filtering-like process. We compare our new strategy with the conventional explicit smoothing ones by testing on synthetic data for different noise levels, initial models and regularization factors. The numerical results show that suffering from the unequal volume and random centroid location of adjacent tetrahedrons, the inversion results of conventional methods often demonstrate scattered structures in slices. In contrast, our new method recovers the model in a smooth way and the convergence speed is largely improved. Finally, we adopt the USArray data for further testing and find that comparing to conventional inversion methods, our new strategy can provide more reliable underground structures with better data fitting.

Smoothing

Regularization

Tetrahedron

Transpose

10.1093/gji/ggad209

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Citations (6)

Approximation solver of Integral Equation for Airborne EM Modeling

International Geophysical Conference, Qingdao, China, 17-20 April 2017 (2017)

Yongchao Lu Changchun Yin Xin Huang Yunhe Liu Jing Cai

Summary Storage of Green's coefficient matrix and solution of linear equations system are always challenges limiting the development and application of Integral equations (IE) method. Multigrid Quasi-linear (MGQL) approximation is a fast method for 3D airborne electromagnetic modeling based on IE and Quasi-linear approximation (QL), which has advantage of both. In this paper, after optimizing IE algorithm in MGQL, the algorithm has been applied for the AEM. We have tested the new method adopting Spectral Element method (SE) for 3D AEM modeling. Utilizing different grids, compared with the traditional IE method. MGQL guarantees the modeling accuracy as well as efficiency can be greatly improved. This method substantially speeds up the modeling of multiple-source.

Solver

10.1190/igc2017-148

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Impact of Satellite Orbit Altitudes on the Global Induced Magnetic Fields

IEEE Transactions on Geoscience and Remote Sensing (2024)

Yinglin Wang Xiuyan Ren Changchun Yin Mingquan Lai Yang Su

Orbit (dynamics)

Polar orbit

Sun-synchronous orbit

Satellite broadcasting

Geocentric orbit

10.1109/tgrs.2024.3476442

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Effect of observation parameters on 3D controlled-source electromagnetic inversion

Near-Surface Asia Pacific Conference, Waikoloa, Hawaii, 7-10 July 2015 (2015)

Yunhe Liu Changchun Yin Weng Ai-hua Jing Cai

Summary In this paper, we use controlled-source audio-frequency magnetotelluric method (CSAMT) as a controlled-source electromagnetic (CSEM) survey example, to discuss how to choose observation parameters in order to obtain optimal exploration results. The staggered finite-difference method is used to solve the three-dimensional (3D) scattered electrical fields governed by Helmholtz equation with primary field as source term at the right hand side. Limited memory quasi-Newton method is used to invert the synthetic data. On each observing site, we will observe Ex, Ey and Hy, from which we use Ex itself, amplitude of Ex, the combination by Ex and Ey, or by Ex and Hy, and impedance element Zxy derived from Ex and Hy as inversing parameters. The numerical results show that for controlled-source EM, we suggest that Ex and Hy, are observed and the derived parameter Zxy be used as inversion parameters. If the magnetic field component Hy is not available, Ey can be used to compensate the Hy component; if only Ex component is available, then its amplitude and phase need be observed. If only the amplitude is used for inversion, there exist very large uncertainties in the deep area.

10.1190/nsapc2015-097

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DecNet: Decomposition network for 3D gravity inversion

Geophysics (2022)

Shuang Zhang Changchun Yin Xiaoyue Cao Siyuan Sun Yunhe Liu

Three dimensional gravity inversion is an effective way to extract subsurface density distribution from gravity data. Different from the conventional geophysics-based inversions, machine-learning-based inversion is a data-driven method mapping the observed data to a 3D model. We have developed a new machine-learning-based inversion method by establishing a decomposition network (DecNet). Unlike existing machine-learning-based inversion methods, the proposed DecNet method is a mapping from 2D to 2D, which requires less training time and memory space. Instead of learning the density information of each grid point, this network learns the boundary position, vertical center, thickness, and density distribution by 2D-to-2D mapping and reconstructs the 3D model by using these predicted parameters. Furthermore, by using the highly accurate boundary information learned from this network as supplement information, the DecNet method is optimized into a DecNet B method. By comparing the least-squares inversion and U-Net inversion on synthetic and real survey data, the DecNet and DecNet B methods have shown the advantage in dealing with inverse problems for targets with boundaries.

Inverse transform sampling

Prior information

10.1190/geo2021-0744.1

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Citations (14)