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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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 14
Hlavní autoři:	Yang, Xiaopeng, Sun, Haoran, Guo, Conglong, Li, Yixuan, Gong, Junbo, Qu, Xiaodong, Lan, Tian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Témata:	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
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Abstract	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.
AbstractList	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.
Author	Yang, Xiaopeng Li, Yixuan Qu, Xiaodong Guo, Conglong Gong, Junbo Sun, Haoran Lan, Tian
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References	ref13 ref12 ref15 ref30 ref11 ref33 ref10 ref32 ref2 ref1 ref17 Vaswani (ref31); 30 ref19 ref18 ref24 ref26 ref25 ref20 Al-Qadi (ref16) 2004; 73 ref22 ref21 ref28 ref27 ref29 ref8 ref7 Li (ref23) 2019 ref9 ref4 ref3 ref6 ref5 Sakamoto (ref14) 1986; 81
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SubjectTerms	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)
Title	Layered Media Parameter Inversion Method Based on Deconvolution Autoencoder and Self-Attention Mechanism Using GPR Data
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