Φ-OTDR瑞利背向散射信号压缩存储与衰落噪声抑制技术研究

随着智能化时代的推进，传感器技术作为连接现实物理世界与数字世界桥梁，其重要性日益凸显。分布式光纤传感技术是光纤传感领域的重要分支，它利用普通光纤作为传感介质，实现全链路的连续有效监测。其中，基于瑞利散射的相位敏感光时域反射仪（Φ-OTDR）备受瞩目。该技术利用瑞利散射现象，实现高灵敏度的振动测量和声波信号采集。Φ-OTDR具有散射功率大、响应速度快、定位精度高等优势。结合传感光纤低损耗、轻量化、耐腐蚀以及电绝缘等优点，它特别适用于地质勘探、能源线缆监测、设施安全防护等领域。

Φ-OTDR系统在部署后长期运行时，面临着如何高效存储所产生的海量数据的挑战。与此同时，随着以大语言模型为代表的人工智能技术的迅猛发展，将这些技术应用到Φ-OTDR中以增强其对扰动的监测、识别及预警能力，正逐渐成为研究领域的一个显著趋势。然而，传统Φ-OTDR仅存储解调相位信号，特征单一，限制了模式识别等技术的应用。Φ-OTDR瑞利背向散射（RBS）信号可以提供丰富的传感信息。然而，原始RBS信号数据量庞大，直接存储并不现实。本论文以获取高压缩比、无传感盲区的RBS信号为目标进行了深入研究，并取得以下创新性成果：

（1）探究了RBS信号精度对Φ-OTDR系统性能的影响，并提出单比特Φ-OTDR系统的概念。实验发现低精度RBS信号在通过滤波器后可以恢复时域波形，并且当数据精度超过一定阈值时，RBS信号精度的进一步提升为系统性能带来的收益将急剧减少。这一发现为Φ-OTDR系统数据采集设备采样精度的选择提供了指导。特别地，单比特RBS信号的扰动信噪比（SNR）可达58.03 dB，应变分辨率为0.64 nε，该结果为单比特Φ-OTDR系统的应用奠定了基础。

（2）针对Φ-OTDR系统RBS信号数据量庞大问题，提出基于超低精度与降采样的RBS信号时域波形压缩存储技术。通过降采样减少单位时间内需存储的样本数量，再结合超低精度技术压缩每个样本所需存储空间，从两个维度同时降低数据量。相比于存储原始RBS信号，该方法压缩比高达80，对应98.75%的节省空间率。在多扰动实验中，压缩数据解调结果能准确定位所有振动源；在单扰动场景下，压缩数据解调扰动SNR虽然比原始数据低9.82 dB，但仍高达56.13 dB，表现出良好性能。

（3）针对RBS信号时域波形压缩存储技术会影响系统SNR的问题，提出基于编码提取频谱的数据存储技术。通过分析RBS信号提取频谱的统计特性，发现其取值概率为非均匀分布。引入哈夫曼算法对量化提取频谱进行编码压缩，获得19.61的压缩比，对应节省空间率94.88%。不同于时域波形压缩存储技术，编码提取频谱解调得到的扰动SNR仅比原始RBS信号结果低0.3 dB，这表明该方法对系统SNR几乎没有影响。

（4）相干衰落噪声是Φ-OTDR中一个不可避免的问题。上述时域波形和频谱数据的压缩存储技术均属于有损压缩，可能使衰落噪声变得难以抑制，进而产生传感盲区。针对该问题，提出4种集成衰落抑制算法。该算法利用频域分集、相位分集以及空间域同步合成技术，在不同域产生多组子信号后进行同步并合成，从而有效抑制合成信号中的衰落噪声。集成衰落抑制算法在常规数据、单比特降采样数据以及编码提取频谱数据中均进行了验证，实验结果显示，该算法成功消除了实验数据中的衰落噪声，从而有效地解决了传感盲区的问题。

本文针对高压缩比、无传感盲区RBS信号的研究，为深度结合人工智能技术的Φ-OTDR系统在地质勘探、能源线缆监测等领域的实用化和智能化发展提供了一种可能的解决方案与技术思路。

With the advancement of the intelligent era, sensing technology, as a bridge connecting the physical world with the digital world, has become increasingly significant. Distributed optical fiber sensing technology is an essential branch of optical fiber sensing, utilizing optical fibers as sensing media to achieve continuous and effective monitoring across the entire link. Notably, phase-sensitive optical time-domain reflectometry (Φ-OTDR) based on Rayleigh backscattering (RBS) light has garnered considerable attention. This technology exploits the Rayleigh scattering phenomenon to enable highly sensitive vibration measurements and acoustic signal acquisition. Φ-OTDR boasts advantages including high scattering power, rapid response, and precise positioning. Combined with the advantages of low loss, light weight, corrosion resistance and electrical insulation of sensing fiber, it is particularly well-suited for applications in geological exploration, energy cable monitoring, facility safety protection, and other related fields.

The challenge of storing vast amounts of sensing data poses a significant hurdle for the long-term monitoring Φ-OTDR. Meanwhile, the rapid development of artificial intelligence (AI) technologies and their application in Φ-OTDR to enhance its capabilities in disturbance monitoring, identification, and early warning have emerged as a prominent trend. However, conventional Φ-OTDR systems typically store demodulated phase traces with limited features, restricting the application of techniques like pattern recognition. Original RBS signal can provide rich sensing information. Nonetheless, the original RBS signal data volume is substantial, making direct storage impractical. This thesis delves into the acquisition of RBS signals with high compression ratio and no sensing dead zones, yielding the following innovative outcomes:

(1) We explore how the resolution of RBS signal impacts the system signal-to-noise ratio (SNR) and introduce the concept of a single-bit Φ-OTDR system. Through experiments, we discover that low-resolution RBS signal can regain its time-domain waveform after filtering. This revelation provides valuable guidance for selecting the data acquisition equipment in Φ-OTDR. Remarkably, the vibration SNR demodulated from single-bit RBS signal can achieve 58.03 dB, with a strain resolution of 0.64 nε. These results demonstrate the feasibility of the single-bit Φ-OTDR.

(2) To tackle the challenge of managing vast amounts of RBS signal data in Φ-OTDR system, we propose a time-domain waveform compressed storage solution that combines ultra-low resolution and undersampling techniques. This approach reduces the number of samples stored per second through undersampling, and further compresses the storage space required for each sample using ultra-low resolution technique. Compared to storing raw data, the proposed method boasts a compression ratio of up to 80, equating to a space saving ratio of 98.75%. In multi-vibration experiment, the demodulated results of compressed data can accurately locate all vibration sources. In single-vibration experiment, the vibration SNR of compressed data, while 9.82 dB lower than that of the original signal, still maintains a high value of 56.13 dB.

(3) A compression solution for RBS signal based on encoding the extracted spectrum data is also proposed. By analyzing the statistical properties of the extracted spectrum data from RBS signals, a non-uniform distribution in their value probabilities is observed. Based on this, the Huffman algorithm is introduced to encode the quantized spectrum data. A compression ratio of 19.61 is achieved in the experiment, corresponding to a space saving ratio of 94.88%. Unlike the time-domain waveform compression technique, the vibration SNR obtained from the encoded spectrum data is only 0.3 dB lower than that of the original signal, indicating that this method has almost no impact on the system SNR.

(4) Interference fading is an unavoidable issue in Φ-OTDR. The storage techniques mentioned above are both lossy compression methods, potentially complicating the suppression of fading noise. To address this issue, an integrated fading suppression algorithm set is proposed. The integrated algorithms combine frequency domain diversity, phase diversity, and synthesizing in spatial domain technique to eliminate the fading noise. Through experiments on conventional data, single-bit undersampled data, and encoded spectrum data, the performance of the integrated fading suppression algorithms are verified.

Overall, our research on RBS signal with high compression ratio and no sensing dead zones contributes to further promoting the practical and intelligent development of Φ-OTDR systems.

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