Abstract
Extracting more and more accurate information to understand the detected vibration or acoustic targets better, has always been an important goal in signal recognition for Distributed Acoustic Sensor(DAS) with optical fiber. In this paper, we use one-dimensional Convolution Neural Networks(1D-CNNs) to extract the detailed temporal structure information at each signal node and utilize a bidirectional Long Short Term Memory(BiLSTM) network to dig out the spatial relationship among the different signal nodes, and then propose a novel identification method by treating the spatial- and temporal- information in a different way, which is denoted as the 1DCNNs-BiLSTM model. The experimental results on the field data show better recognition performance can be achieved in the safety monitoring of the buried optical communication cable in urban with DAS. It helps to improve the recognition rate further compared with the other deep-learning methods frequently or possibly used for DAS signal recognition, such as the 1D-CNNs with a single temporal feature extraction, and 1DCNN-CNN and 2D-CNN models with simultaneous spatiotemporal feature learning. To the best of our knowledge, it is the first time to simultaneously extract and utilize the detailed temporal structure feature and the overall spatial connection through a customized deep learning network.
Highlights
Distributed Acoustic Sensor (DAS) based on the phasesensitive optical time-domain reflectometry ( -OTDR) technology [1]–[4], provides a highly sensitive and cost-effective vibration or sound sensing way for our environment in long distance or wide range
We proposed a knowledge mining method based on the hidden Markov models (HMMs) [33] to extract the dynamic time sequence feature and its evolution information, and identify the sequential state process of typical events
It shows that the proposed 1DCNNs-bidirectional LSTM (BiLSTM) network behaves steadily the best in this field data test for DAS, which generally reveals that the 1DCNNs-BiLSTM has the best learning performance for the spatiotemporal information extraction in this application
Summary
Distributed Acoustic Sensor (DAS) based on the phasesensitive optical time-domain reflectometry ( -OTDR) technology [1]–[4], provides a highly sensitive and cost-effective vibration or sound sensing way for our environment in long distance or wide range. To improve the recognition ability in the safety monitoring of the buried optical communication cable, it is explored a novel identification method by using a deep learning structure with an array of 1-D Convolution Neural Networks(CNNs) combined with the Bi-directional Long Short-Term Memory(BiLSTM) model, which is shortened as 1DCNNs-BiLSTM In this algorithm, CNN is used to automatically extract the temporal structure feature of the signals at each acquisition node on fiber, and the BiLSTM network is composed of forward and backward LSTMs, which are designed to mine the internal spatial relationship among the temporal signals at different nodes from right to left and from left to right in succeed.
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