Typical fully distributed optical fiber sensors (DOFS) with dozens of kilometers are equivalent to tens of thousands of point sensors along the whole monitoring line, which means tens of thousands of data will be generated for one pulse launching period. Therefore, in an all-day nonstop monitoring, large volumes of data are created thereby triggering the demand for large storage space and high speed for data transmission. In addition, when the monitoring length and channel numbers increase, the data also increase extensively. The task of mitigating large volumes of data accumulation, large storage capacity, and high-speed data transmission is, therefore, the aim of this paper. To demonstrate our idea, we carried out a comparative study of two lossless methods, Huffman and Lempel Ziv Welch (LZW), with a lossy data compression algorithm, fast wavelet transform (FWT) based on three distinctive DOFS sensing data, such as Φ-OTDR, P-OTDR, and B-OTDA. Our results demonstrated that FWT yielded the best compression ratio with good consumption time, irrespective of errors in signal construction of the three DOFS data. Our outcomes indicate the promising potentials of FWT which makes it more suitable, reliable, and convenient for real-time compression of the DOFS data. Finally, it was observed that differences in the DOFS data structure have some influence on both the compression ratio and computational cost.
Multi-point disturbance detection is always challenging in polarization-sensitive optical time domain reflectometry (POTDR). In this paper, we propose a novel method to solve such a challenging problem by accumulating the temporally differentiated OTDR traces and building up a two-dimensional temporally-spatially evolving graph, and then using edge detection and automatic-clustering, adopted from imaging processing techniques, to discriminate different disturbance points and find out their locations. Many multi-point disturbance cases are tested and the results show that the method proposed has better performance than the conventional direct differentiation method and the Fast Fourier Transform (FFT) spectrum analysis. In particular, the location accuracy has been improved significantly.