De-Noising Method for Seismic Data via Improved Convolution Neural Network

Fig. 3. Denoising results of different algorithms for pre-stack seismic data. (a) Original seismic data; (b) noisy seismic data; (c) denoising results of SVD algorithm; (d) denoising results of BP algorithm; (e) denoising results of algorithm proposed in Ref. [9]; (f) denoising results of proposed algorithm

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Fig. 4. Denoising results of different algorithms for post-stack seismic data. (a) Original seismic data; (b) noisy seismic data; (c) denoising results of SVD; (d) denoising results of BP; (e) denoising results of algorithm proposed in Ref. [9]; (f) denoising results of proposed algorithm

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Table 1. M_SE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

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Table 1. M_SE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

Number of feature maps	3×3	5×5	7×7	9×9
1	0.0475	0.0520	0.0585	0.0680
2	0.0360	0.0245	0.0555	0.0445
3	0.0340	0.0275	0.0365	0.0355
4	0.0370	0.0250	0.0315	0.0300
5	0.0312	0.0265	0.0345	0.0330
6	0.0420	0.0520	0.0315	0.5000
7	0.5000	0.0335	0.5000	0.0285
8	0.5000	0.5000	0.0305	0.0285
9	0.5000	0.0235	0.0385	0.5000
10	0.5000	0.5000	0.0327	0.0290
11	0.5000	0.5000	0.5000	0.0312
12	0.5000	0.5000	0.5000	0.0441
13	0.5000	0.5000	0.5000	0.5000
14	0.5000	0.5000	0.5000	0.5000
15	0.5000	0.5000	0.5000	0.5000
16	0.5000	0.5000	0.5000	0.5000

Table 2. M_SE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for

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Table 2. M_SE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for

Number of feature maps	1×1	3×3	5×5
1	0.0620	0.0460	0.0510
2	0.0565	0.0425	0.0435
3	0.0570	0.0545	0.0435
4	0.0405	0.0410	0.0430
5	0.0415	0.0340	0.0345
6	0.0535	0.0190	0.0330
7	0.0495	0.0370	0.0295
8	0.0395	0.0325	0.0340
9	0.0480	0.0395	0.0260

Table 3. Results of different algorithms for pre-stack and post-stack seismic data

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Table 3. Results of different algorithms for pre-stack and post-stack seismic data

Seismic data	Algorithm	P_SNR /dB	S_NR /dB	M_SE
	SVD	62.9160	25.7189	0.0095
Pre-stack data	BP	61.0352	24.4899	0.0156
	Algorithm in Ref. [9]	64.6129	27.3903	0.0080
	Proposed algorithm	64.6236	28.1604	0.0067
	SVD	61.2323	27.4449	0.0079
Post-stack data	BP	60.3104	24.6603	0.0150
	Algorithm in Ref. [9]	64.9023	29.6088	0.0048
	Proposed algorithm	66.6712	31.9728	0.0022

Table 4. Denoising results of proposed algorithm at different noise levels
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Table 4. Denoising results of proposed algorithm at different noise levels
Noise level /% P_SNR /dB S_NR /dB M_SE
5 80.9861 38.3296 0.0005
10 71.0896 34.3800 0.0016
15 66.6712 31.9728 0.0022
20 65.5404 26.8308 0.0091
25 64.3720 23.7155 0.0150
30 63.7596 22.1031 0.0217

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Shaohua Cui, Suwen Li, Xude Wang. De-Noising Method for Seismic Data via Improved Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061501

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Paper Information

Category: Machine Vision

Received: Jun. 12, 2019

Accepted: Aug. 28, 2019

Published Online: Mar. 6, 2020

The Author Email: Shaohua Cui (flower0804@126.com)

DOI:10.3788/LOP57.061501

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. MSE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

Table 1. MSE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

Table 2. MSE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for

Table 2. MSE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for

Table 3. Results of different algorithms for pre-stack and post-stack seismic data

Table 3. Results of different algorithms for pre-stack and post-stack seismic data

Table 4. Denoising results of proposed algorithm at different noise levels

Table 4. Denoising results of proposed algorithm at different noise levels

Table 1. M_SE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

Table 1. M_SE values corresponding to different sizes of convolution kernels and numbers of feature maps in C1 layer

Table 2. M_SE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for

Table 2. M_SE values corresponding to different sizes of convolution kernels and feature map numbers of C3 layer when the convolution kernel size is 5×5 and the number of feature maps is 9 for