Aiming at the location problem of high-pressure pipeline leakage, this paper proposes a method for predicting the disturbed position of a Sagnac distributed optical fiber sensing system based on ensemble learning of convolutional neural networks (CNNs). A Stacking ensemble method is used to combine two different CNN models to improve the stability and location accuracy of the disturbance position prediction model. By training the prediction model using the spectrum features of interference signals generated by the disturbances at partial sensing positions, accurate prediction of an arbitrary disturbance position can be realized. The disturbance position prediction was carried out numerically on a sensing fiber with an effective length of 8.5 km. Simulation results show that a mean absolute error of no more than 14.6 m and a location resolution of 10 m are achieved. The method is insensitive to noise, low in system complexity, simple in data processing and accurate in location results.

针对高压管道泄漏的定位问题，提出了一种基于卷积神经网络集成学习的Sagnac分布式光纤传感系统受干扰位置的预测方法。为了解决扰动位置预测模型的稳定性和定位精度问题，采用了一种集成算法将两种不同的CNN模型进行组合。通过使用由局部感测位置处的干扰产生的干扰信号的频谱特征训练预测模型，可以实现任意干扰位置的准确预测。在有效长度为8.5 km的传感光纤上以数值方式进行了干扰位置预测。仿真结果表明，平均绝对误差不超过14。达到6 m和10 m的位置分辨率。该方法对噪声不敏感，系统复杂度低，数据处理简单，定位结果准确。