基于神经网络的探地雷达数据去噪

doi:10.11720/wtyht.2023.1347

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Abstract

Ground-penetrating radar (GPR) data are often contaminated by random noise in the actual engineering inspection.The noise in data will reduce the signal-to-noise ratio and resolution of the data,adversely affecting the subsequent inversion and interpretation.Accordingly,this study proposed neural network-based denoising for GPR data.First,a multi-layer neural network model was constructed to integrate the data corrupted by white Gaussian noise into the noise-free data.Then,the corrupted data and their corresponding noise patches were built as training data.The weights of neurons in every layer of the model e updated using a back-propagation algorithm to minimize the model training loss.Finally,the two synthetic data and the measured radar data were input to the trained model,and the model's output was calculated using the noise characteristic weights acquired from the training.Compared with the curvelet transform,the numerical simulation test results verify the effectiveness and robustness of the method proposed in this study.Moreover,the proposed method can suppress the noise more thoroughly in areas with complex structures and weak amplitudes,and show effective signals more clearly.

Key words： ground penetrating radar(GPR) neural network multi-layer perceptron back propagation white Gaussian noise data denoising

Received: 26 July 2022 Published: 27 October 2023

ZTFLH:

P631.4

Corresponding Authors: NING Xiao-Bin E-mail: 262607406@qq.com;631118892@qq.com

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	Articles by authors
	Song WU
	Xiao-Bin NING
	Ting-Wei YANG
	Hong-Liang JIANG
	Chao-Bo LU
	Yu-Di SU

Cite this article:

Song WU,Xiao-Bin NING,Ting-Wei YANG, et al. Neural network-based denoising for ground-penetrating radar data[J]. Geophysical and Geochemical Exploration, 2023, 47(5): 1298-1306.

URL:

https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2023.1347 OR https://www.wutanyuhuatan.com/EN/Y2023/V47/I5/1298

Single neuron model

MLP with one hidden layer

Operation module

Sequence module

MLP network structure diagram

Denoising results

Comparison of denoising effects of neural network and curvelet denoising on different noises

Comparison of denoising effects of MLP and curvelet denoising on different noises

Complex data denoising results

Real data denoising results

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