Joint Wavelet Denoising for Raman DTS Signal Quality
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Solution Overview
Problem
Raman DTS systems face challenges in achieving high signal-to-noise ratio (SNR) over long fiber distances due to the weak power of Raman Stokes and anti-Stokes signals, leading to large measurement errors and unsuitable performance for long-range sensing applications, despite the use of high-gain avalanche photodetectors and existing signal denoising methods that either degrade spatial resolution or fail to remove all noise components.
Innovation Solution
The implementation of joint wavelet denoising (JWD) techniques, which jointly estimate noise levels and apply thresholding/shrinkage to both Raman Stokes and anti-Stokes signals, leveraging their common temperature dependence to enhance signal retention and suppress noise, thereby improving denoising performance and maintaining spatial resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If existing signal denoising methods are used to reduce noise in weak Raman signals, then noise reduction is achieved, but spatial resolution is degraded
Solution Approach 1:
The patent applies discrete wavelet transform to segment the Raman signal into different frequency components across multiple decomposition levels. This segmentation allows selective processing of noise components at different scales while preserving the spatial resolution of the original signal through localized wavelet basis functions.
Solution Approach 2:
The patent implements localized denoising by applying different thresholding strategies to different wavelet coefficients based on their local characteristics. The wavelet transform provides local time-frequency analysis, allowing the denoising operation to adapt to local signal features while maintaining spatial resolution through the localized support of wavelet basis functions.
2Object-affected harmful factors
If existing signal denoising methods are used to reduce noise in weak Raman signals, then noise reduction is achieved, but measurement errors remain large
Solution Approach 1:
The patent merges the denoising processing of both Stokes and anti-Stokes Raman signals through joint wavelet thresholding. By combining information from both signals and applying coordinated thresholding operations, the method achieves superior noise reduction while preserving the true signal components, thereby reducing measurement errors in temperature sensing.
Solution Approach 2:
The patent employs iterative threshold adjustment based on the statistical characteristics of the wavelet coefficients. The thresholding process uses feedback from the signal statistics to adaptively determine optimal threshold values, improving the accuracy of noise removal and reducing measurement errors through refined iterative processing.
3Power
If high-gain avalanche photodetectors are used to amplify weak Raman signals, then signal power is increased, but noise amplification and artifacts occur
Solution Approach 1:
The patent applies wavelet denoising as a preliminary processing step before temperature calculation and other subsequent operations. By removing noise components from the Raman signals prior to further processing, the method prevents noise amplification and artifact generation in downstream calculations, ensuring higher measurement accuracy.
Solution Approach 2:
The patent converts the harmful effect of noise in the amplified Raman signals into a benefit by using the wavelet transform to identify and remove noise components while preserving the true signal. The noise, which initially degrades the signal quality, becomes a distinguishable component that can be selectively eliminated through thresholding, turning the noise problem into an opportunity for enhanced signal purification.
Data Source
AI summary
Aspects of the present disclosure describe systems, methods, and structures for distributed temperature sensing that employ joint wavelet denoising to achieve desirable signal-to-noise ratio(s) over extended sensor fiber distances.