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10 results on '"Xing, Guanglong"'

1. Electromagnetic Fields Computation of Planar-Distribution Sources in Anisotropic Planar-Stratified Media With an Equivalent Boundary Approach

Author

Liu, Tinglong, Xing, Guanglong, and Zhang, Xiaochong

Abstract

In this study, the equivalent boundary approach is applied to quickly compute the electromagnetic (EM) fields of planar-distribution sources in fully anisotropic planar-stratified media. In the derivation process, the spectral-domain EM field vector is represented as the product of the field coefficient matrix, the adjacency matrix of the source-field, and the source coefficient matrix. The adjacency matrix of the source-field is found to be independent of the source coefficient matrix and is related only to the stratigraphic structure and anisotropy parameters. Thus, the EM fields of multiple planar-distribution sources can be calculated simultaneously by extending the source coefficient matrix approach, thereby achieving fast calculation. The 2-D Fourier integral is calculated in a cylindrical coordinate system to reduce the double infinite zones to a single infinite zone. To avoid a singularity when the field locations are close to the source along the vertical direction, a double parabolic Sommerfeld integration path is constructed in the spectral complex plane to optimize the use of the properties of evanescent waves in the integration calculations and for fast decay of the integrand, thereby allowing for the robust calculation of EM fields. Numerical results using the finite element method validate the proposed method. Hence, the proposed approach significantly improves the computational efficiency of solving for the EM field of a planar-distribution source in fully anisotropic media, thus facilitating geophysical exploration.

Published
2024
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3. Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels

Author

LIU Shuai and XING Guanglong

Subjects
hyperspectral unmixing, abundance estimation, convolutional neural network, deep learning, Mathematical geography. Cartography, GA1-1776
Abstract

Influenced by the performance of imaging spectrometer and the distribution of complex ground objects, hyperspectral images have a large number of mixed pixels. Traditional learning-based unmixing methods are shallow models, or lack of comprehensive use of spatial and spectral information. This paper proposes a collaborative deep model with multi-dimensional convolutional network. Using multi-dimensional convolutional network can make full use of multi-dimensional semantic information, which is better to estimate hyperspectral mixed pixel abundance with small samples. The method augments training data, constructs three kinds of convolutional neural networks: spectral dimension, spatial dimension and cube dimension; the method designs fusion layer to concatenate features with three kinds of convolutional neural networks, and to “end-to-end” estimate of mixed pixel abundance; the model uses batch normalization, pooling and dropout to avoid over fitting phenomenon. The experimental results indicate that the introduction of our proposed method can extract spatial-spectral feature information more effectively. Compared with other convolutional network unmixing models, the accuracy of the estimated mixed pixel abundance is significantly improved.

Published
2020

4. A hybrid method for electromagnetic propagated resistivity logging data inversion

Author

Xing, Guanglong and Xue, Jishuang

Subjects
Electromagnetism -- Analysis, Algorithms -- Analysis, Algorithm, Business, Earth sciences, Electronics and electrical industries
Abstract

Using an inverse technique for the array electromagnetic propagated resistivity logging (EPRL) data, a fine interpretation can be obtained about the resistivity distribution of an invaded profile. Generally, the Gauss--Newton algorithm (GN) is an efficient technique for the inverse problems; however, as a gradient-type optimization method, its accuracy and convergence depend strongly on the initial value. Even though this problem can be avoided by using a differential evolutionary algorithm (DE) as a global search optimization, it is computationally less efficient. In this paper, a hybrid inversion method of differential evolution has been developed to remove the strong dependence of the accuracy and convergence on the initial value. In this new method, an additional operation, which is designed with GN, is performed only to the best individual with a delay in the evolution processes of DE. Hence, the GN operation is used for the improvement of the convergence speed without leading to any decrease of the robustness of DE. The hybrid method is then extended to apply the inversion of EPRL data. Our results demonstrate its speed, steadiness, and efficiency of this hybrid method. Index Terms--Differential evolutionary algorithm (DE), electromagnetic propagated resistivity logging (EPRL), Gauss-Newton method (GN), hybrid differential evolutionary algorithm (HDE), inverse problem.

Published
2007

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