Distributed acoustic sensors (DAS) are effective apparatus which are widely used in many application areas for recording signals of various events with very high spatial resolution along the optical fiber. To detect and recognize the recorded events properly, advanced signal processing algorithms with high computational demands are crucial. Convolutional neural networks are highly capable tools for extracting spatial information and very suitable for event recognition applications in DAS. Long-short term memory (LSTM) is an effective instrument for processing sequential data. In this study, we proposed a multi-input multi-output, two stage feature extraction methodology that combines the capabilities of these neural network architectures with transfer learning to classify vibrations applied to an optical fiber by a piezo transducer. First, we extracted the differential amplitude and phase information from the Phase-OTDR recordings and stored them in a temporal-spatial data matrix. Then, we used a state-of-the-art pre-trained CNN without dense layers as a feature extractor in the first stage. In the second stage, we used LSTMs to further analyze the features extracted by the CNN. Finally, we used a dense layer to classify the extracted features. To observe the effect of the utilized CNN architecture, we tested our model with five state-of-the art pre-trained models (VGG-16, ResNet-50, DenseNet-121, MobileNet and Inception-v3). The results show that using the VGG-16 architecture in our framework manages to obtain 100% classification accuracy in 50 trainings and got the best results on our Phase-OTDR dataset. Outcomes of this study indicate that the pre-trained CNNs combined with LSTM are very suitable for the analysis of differential amplitude and phase information, represented in a temporal spatial data matrix which is promising for event recognition operations in DAS applications.
翻译:分布式传声器(DAS)是有效的装置,在许多应用领域广泛用于记录光纤上高度空间分辨率的各种活动信号。为了正确检测和识别所记录的事件,高级信号处理算法至关重要。进化神经网络是极能提取空间信息的工具,非常适合DAS中的事件识别应用。长短术语内存(LSTM)是处理连续数据的有效工具。在本研究中,我们建议采用多投入多输出多输出,两个阶段特征提取方法,将神经网络结构的能力与传输学习对由派索传感器应用到光纤的50-16移动式网络震动进行分类。首先,我们从阶段-ODR录音中提取了不同的振动和阶段性信息,并将其储存在时间-空间数据矩阵中。然后,我们使用了一种最先进的前训练型CNNISDMD数据采集器,在第一阶段中,我们用LSTMMS进一步分析所提取的功能。最后,我们用最精确的 RDRAF 模型来测量了我们所测试的 RDR 5级结构分析结果 。我们用了一个升级的 RDR 。在模型中,我们所测试的 RDFAFADF 的模型中,我们用了一个最深层数据级数据级的模型来进行了该模型 。