Layered Media Parameter Inversion Method Based on Deconvolution Autoencoder and Self-Attention Mechanism Using GPR Data

Layered medium parameter inversion is a crucial technique in ground-penetrating radar (GPR) data processing and has wide application in civil engineering and geological exploration. In response to the issues of high computational complexity and low accuracy associated with existing methods, a novel...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 14
Main Authors: Yang, Xiaopeng, Sun, Haoran, Guo, Conglong, Li, Yixuan, Gong, Junbo, Qu, Xiaodong, Lan, Tian
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Subjects:
Accuracy
Adaptability
Autocorrelation
Civil engineering
Computational efficiency
Computer applications
Computing time
Data acquisition
Data analysis
Data processing
Deconvolution
Feature extraction
Geological surveys
Ground penetrating radar
Inversions
Mathematical models
Modules
Multilayer perceptrons
Parameters
Radar
Robustness (mathematics)
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Layered medium parameter inversion is a crucial technique in ground-penetrating radar (GPR) data processing and has wide application in civil engineering and geological exploration. In response to the issues of high computational complexity and low accuracy associated with existing methods, a novel layered medium parameter inversion approach is proposed, comprising the deconvolution autoencoder and the parameter inversion network. First, the deconvolution autoencoder is introduced to solve the pulse response of layered medium systems in an unsupervised manner, which enhances the computational efficiency of deconvolution and decouples the data acquisition system from the supervised model. Subsequently, a parameter inversion network, including a self-attention module and a residual multilayer perceptron (MLP), is proposed to address the challenge posed by the excessively sparse pulse responses. The self-attention module calculates the autocorrelation of the pulse sequence, providing temporal delay information between pulses and reducing the sparsity of the pulse response to facilitate feature extraction. Meanwhile, the residual MLP, known for its low information loss and adaptability to different output dimensions, is employed for model-based and pixel-based inversions in situations with and without prior knowledge of the layer number, respectively. Finally, simulated and measured datasets are constructed to comprehensively train and evaluate the proposed method. The results demonstrate that the proposed method exhibits better performance of inversion accuracy, computational efficiency, robustness, generalization capability, and noise resistance. In addition, it remains applicable even when prior knowledge of the layer number is unknown.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3351894