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Rapid Response DAS Denoising Method Based on Deep Learning
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DOI：10.1109/JLT.2021.3052651.png)
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Abstract
En 中文
In most optical fiber distributed acoustic sensing (DAS) systems, to obtain the desired outcome, the sensing signal acquired by DAS systems normally needs to be denoised. In some applications, such as the identifiation of the position of fast-moving targets, we need DAS systems to respond with sufficient speed. However, most classical denoising algorithms do not work if the signals are insufficiently collected within a short period (called a short-time signal). To obtain ideal results within a short time window, we propose an attention-based convolutional neural network (CNN) structure with extremely short signal windows to learn and approximate the results of classical denoising methods. To evaluate the effectiveness of the proposed method, the experiment is conducted under a real field highway scenario where the desired signals are overwhelmed with noise. The results show that by using signals collected within extremely short time windows of 100 ms, an insufficient time for the processing of existing denoising algorithms, our structure yields a satisfactory denoising performance.
Keywords:
Road transportation
Vibrations
Target tracking
Noise reduction
Acoustics
Sensors
Signal to noise ratio
Deep learning
distributed optical fiber acoustic sensing (DAS)
signal denosing
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