An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing Article Swipe
Tianwei Lan
,
Liguo Han
,
Zhaofa Zeng
,
Jingwen Zeng
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095262
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13095262
The incorporation of effective denoising techniques is a crucial requirement for seismic data processing during the acquisition phase due to the inherent susceptibility of the seismic data acquisition process to various forms of interference, such as random and coherent noise. For random noise, the Residual Neural Network (Resnet), with its notable ability to effectively suppress noise in seismic data, has garnered widespread utilization in removing unwanted disturbances or interference due to its elegant simplicity and outstanding performance. Despite the considerable advancements achieved by conventional Resnet in the field of suppressing noise, it is irrefutable that there is still room for amelioration in their ability to filter out unwanted disturbances. As a result, this paper puts forth a novel attention-based methodology for Resnet, intended to overcome the present constraints and attain an optimal seismic signal enhancement. Specifically, we add the convolutional block attention module (CBAM) after the convolutional layer of the residual module and add channel attention on the shortcut connections to filter out the disturbance. We replace the commonly used ReLU activation function in the network with ELU, which is better suited for suppressing seismic noise. Empirical assessments conducted on both synthetic and authentic datasets have demonstrated the efficacy of the proposed methodology in amplifying the denoising prowess of Resnet. Our proposed method remains stable even when dealing with seismic data that has complex waveforms. The findings of this investigation evince that the recommended approach furnishes a substantial augmentation in the signal-to-noise ratio (SNR), thereby facilitating the efficient and robust extraction of the underlying signal from the noisy observations.
Related Topics
Concepts
Computer science
Residual
Noise reduction
Noise (video)
Interference (communication)
Filter (signal processing)
SIGNAL (programming language)
Convolutional neural network
Pattern recognition (psychology)
Artificial intelligence
Electronic engineering
Algorithm
Channel (broadcasting)
Engineering
Telecommunications
Computer vision
Programming language
Image (mathematics)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13095262
- https://www.mdpi.com/2076-3417/13/9/5262/pdf?version=1682237245
- OA Status
- gold
- Cited By
- 7
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366826945
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366826945Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13095262Digital Object Identifier
- Title
-
An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal ProcessingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-23Full publication date if available
- Authors
-
Tianwei Lan, Liguo Han, Zhaofa Zeng, Jingwen ZengList of authors in order
- Landing page
-
https://doi.org/10.3390/app13095262Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/9/5262/pdf?version=1682237245Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/13/9/5262/pdf?version=1682237245Direct OA link when available
- Concepts
-
Computer science, Residual, Noise reduction, Noise (video), Interference (communication), Filter (signal processing), SIGNAL (programming language), Convolutional neural network, Pattern recognition (psychology), Artificial intelligence, Electronic engineering, Algorithm, Channel (broadcasting), Engineering, Telecommunications, Computer vision, Programming language, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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