Abstract

We present a long distance distributed strain sensing in optical frequency domain reflectometry (OFDR) by shape-adaptive principal component analysis Block-Matching three-dimensional filter (BM3D-SAPCA) image denoising, which uses correlated patterns and high degree redundancy of sensing data for enhancing the performance of distributed sensing by image processing for removing noise and increasing the signal-to-noise ratio (SNR) of noisy measurements. Compared with 2D image denoising methods, BM3D-SAPCA method searches similar 2D image blocks and stacks them together in 3D arrays, which takes full advantage of the high level of similitude and redundancy contained on the multidimensional information to denoise. We find that the BM3D-SAPCA method can effectively suppresses noise aggravation along with an increasing of the sensing distance. Without modifying the hardware system of OFDR, we achieve a distributed strain sensing with a 5 cm spatial resolution, a 2 με strain resolution on a 200 m all grating fiber. We compare the performance of distributed strain sensing by BM3D-SAPCA with Gaussian filter, wavelet denoising (WD) and non-local mean filter (NLM) using the same data. The mean maximal strain measurement error at loaded strain areas is reduced from 2.3791 με to 0.6545 με by BM3D-SAPCA. These mean errors by Gaussian filter, NLM and WD are 1.1177 με, 1.6668 με and 1.9721 με, respectively. The mean standard deviations of strain measurement in eight repeat experiments is reduced from 1.5221 με to 0.3134 με after noise reduction by BM3D-SAPCA, which is 79.37% lower than the raw data. The mean standard deviations after noise reduction by Gaussian filter, NLM and WD are decreased by 68.85%, 64.24% and 14.41% respectively.

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