U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
Road traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/8/4751 |
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| author | Zhaolin Zhu Xin Chen Danping Cao Mingxin Cheng Shuaimin Ding |
| author_facet | Zhaolin Zhu Xin Chen Danping Cao Mingxin Cheng Shuaimin Ding |
| author_sort | Zhaolin Zhu |
| collection | DOAJ |
| description | Road traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near the road will record the noise while receiving the seismic signal. The amplitude of the traffic noise is much larger than the signal, which masks the effective information and degrades the quality of acquired data. At the same time, the traffic noise is coupled with the effective signal, which makes it difficult to separate them. Therefore, attenuating traffic noise is the key to improving the quality of the final processing results. In recent years, denoising methods based on convolution neural networks (CNN) have shown good performance in noise attenuation. These denoising methods can learn the potential characteristics of acquired data, thus establishing the mapping relationship between the original data and the effective signal or noise. Here, we introduce a method combining UNet networks with asymmetric convolution blocks (ACBs) for traffic noise attenuation, and the network is called the ACB-UNet. The ACB-UNet is a supervised deep learning method, which can obtain the distribution characteristics of noise and effective signal through learning the training data and then effectively separate the two to achieve noise removal. To validate the performance of the proposed method, we apply it to synthetic and real data. The data tests show that the ACB-UNet can obtain good results for high amplitude noise attenuation and is practical and efficient. |
| first_indexed | 2024-03-11T05:16:40Z |
| format | Article |
| id | doaj.art-1f839faeb2734072adb3c91ed95f2882 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:16:40Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1f839faeb2734072adb3c91ed95f28822023-11-17T18:08:44ZengMDPI AGApplied Sciences2076-34172023-04-01138475110.3390/app13084751U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic DataZhaolin Zhu0Xin Chen1Danping Cao2Mingxin Cheng3Shuaimin Ding4Hainan Institute of Zhejiang University, Sanya 572024, ChinaSchool of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaSchool of Geosciences, China University of Petroleum (East China), Qingdao 266580, ChinaHainan Institute of Zhejiang University, Sanya 572024, ChinaHainan Institute of Zhejiang University, Sanya 572024, ChinaRoad traffic noise is a special kind of high amplitude noise in seismic or acoustic data acquisition around a road network. It is a mixture of several surface waves with different dispersion and harmonic waves. Road traffic noise is mainly generated by passing vehicles on a road. The geophones near the road will record the noise while receiving the seismic signal. The amplitude of the traffic noise is much larger than the signal, which masks the effective information and degrades the quality of acquired data. At the same time, the traffic noise is coupled with the effective signal, which makes it difficult to separate them. Therefore, attenuating traffic noise is the key to improving the quality of the final processing results. In recent years, denoising methods based on convolution neural networks (CNN) have shown good performance in noise attenuation. These denoising methods can learn the potential characteristics of acquired data, thus establishing the mapping relationship between the original data and the effective signal or noise. Here, we introduce a method combining UNet networks with asymmetric convolution blocks (ACBs) for traffic noise attenuation, and the network is called the ACB-UNet. The ACB-UNet is a supervised deep learning method, which can obtain the distribution characteristics of noise and effective signal through learning the training data and then effectively separate the two to achieve noise removal. To validate the performance of the proposed method, we apply it to synthetic and real data. The data tests show that the ACB-UNet can obtain good results for high amplitude noise attenuation and is practical and efficient.https://www.mdpi.com/2076-3417/13/8/4751road traffic noisehigh amplitudeUNetasymmetric convolution blocks |
| spellingShingle | Zhaolin Zhu Xin Chen Danping Cao Mingxin Cheng Shuaimin Ding U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data Applied Sciences road traffic noise high amplitude UNet asymmetric convolution blocks |
| title | U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data |
| title_full | U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data |
| title_fullStr | U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data |
| title_full_unstemmed | U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data |
| title_short | U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data |
| title_sort | u net with asymmetric convolution blocks for road traffic noise attenuation in seismic data |
| topic | road traffic noise high amplitude UNet asymmetric convolution blocks |
| url | https://www.mdpi.com/2076-3417/13/8/4751 |
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