BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals
Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferenc...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/77 |
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| author | Weijie Wang Xuben Wang Xiaodong Yu Debiao Luo Xinyue Liu Kai Yang Wen Yang Xiaolan Yang Ke Hu Wenyi Hu |
| author_facet | Weijie Wang Xuben Wang Xiaodong Yu Debiao Luo Xinyue Liu Kai Yang Wen Yang Xiaolan Yang Ke Hu Wenyi Hu |
| author_sort | Weijie Wang |
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| description | Airborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys. |
| format | Article |
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| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-2e4888e0c2cd436c8bbd9abe8d9c76352025-08-20T02:36:12ZengMDPI AGSensors1424-82202024-12-012517710.3390/s25010077BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic SignalsWeijie Wang0Xuben Wang1Xiaodong Yu2Debiao Luo3Xinyue Liu4Kai Yang5Wen Yang6Xiaolan Yang7Ke Hu8Wenyi Hu9School of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaSchool of Computer Science, Chengdu University, Chengdu 610106, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaSchool of Geophysics, Chengdu University of Technology, Chengdu 610059, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaInformation Network Center, Chengdu University, Chengdu 610106, ChinaAirborne transient electromagnetic (ATEM) surveys provide a fast, flexible approach for identifying conductive metal deposits across a variety of intricate terrains. Nonetheless, the secondary electromagnetic response signals captured by ATEM systems frequently suffer from numerous noise interferences, which impede effective data processing and interpretation. Traditional denoising methods often fall short in addressing these complex noise backgrounds, leading to less-than-optimal signal extraction. To tackle this issue, a deep learning-based denoising network, called BA-ATEMNet, is introduced, using Bayesian learning alongside a multi-head self-attention mechanism to effectively denoise ATEM signals. The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. Moreover, the combination of Bayesian learning with a weighted integration of prior knowledge and SNR enhances the model’s performance across varying noise levels, thereby increasing its adaptability to complex noise environments. Our experimental findings indicate that BA-ATEMNet surpasses other denoising models in both single and multiple noise conditions, achieving an average signal-to-noise ratio of 37.21 dB in multiple noise scenarios. This notable enhancement in SNR, compared to the next best model, which achieves an average SNR of 36.10 dB, holds substantial implications for ATEM-based mineral exploration and geological surveys.https://www.mdpi.com/1424-8220/25/1/77deep learningairborne transient electromagnetic methodmulti-head self-attention mechanismBayesian learningsignal denoising |
| spellingShingle | Weijie Wang Xuben Wang Xiaodong Yu Debiao Luo Xinyue Liu Kai Yang Wen Yang Xiaolan Yang Ke Hu Wenyi Hu BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals Sensors deep learning airborne transient electromagnetic method multi-head self-attention mechanism Bayesian learning signal denoising |
| title | BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals |
| title_full | BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals |
| title_fullStr | BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals |
| title_full_unstemmed | BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals |
| title_short | BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals |
| title_sort | ba atemnet bayesian learning and multi head self attention for theoretical denoising of airborne transient electromagnetic signals |
| topic | deep learning airborne transient electromagnetic method multi-head self-attention mechanism Bayesian learning signal denoising |
| url | https://www.mdpi.com/1424-8220/25/1/77 |
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