One of the key technologies for optical fiber vibration pre-warning system (OFVWS) refers to identifying the vibration source accurately from the detected vibration signals. Because of many kinds of vibration sources and complex geological structures, the implement of identifying vibration sources presents some interesting challenges which need to be overcome in order to achieve acceptable performance. This paper mainly conducts on the time domain and frequency domain analysis of the vibration signals detected by the OFVWS and establishes attribute feature models including an energy information entropy model to identify raindrop vibration source and a fundamental frequency model to distinguish the construction machine and train or car passing by. Test results show that the design and selection of the feature model are reasonable, and the rate of identification is good.

通过将Loop 型谐振腔嵌入在具有正热光系数的MgF2中，设计了海水中温度弱敏感的盐度传感器来实现海水盐度测量。利用COMSOL 软件建立海水盐度传感模型，结合Matlab 对盐度传感器的特征参量进行数值研究。结果表明：传感器的温度特性依赖于微纳光纤的半径，温度弱敏感的光纤半径随探测波长的减小而减小；盐度灵敏度随镀膜厚度和光纤半径的增大而减小；探测极限随镀膜厚度的增加而增大，随波长的减小而减小。通过优化，传感器盐度灵敏度可达0.025 nm/(mg/mL)，探测极限可达到0. 15 mg/mL。