Abstract

Presently, it is still challenging to obtain satisfied identification results for long-distance safety monitoring with fiber distributed acoustic sensor (DAS) in practical complicated burying environments. Thus, extracting increasingly abundant features has always been the direction of DAS signal recognition. This paper proposes a new recognition method using an end-to-end mCNN-HMM combined model, which can identify the vibration sources more correctly by simultaneously extracting multi-scale structural features and the sequential information of the DAS signals. A modified multi-scale convolution neural network (mCNN) is designed to automatically extract the DAS signals' local structural features from a multilevel perspective and their relationship in the proposed model. A hidden Markov model (HMM) is then used to mine the sequential information of the whole sample's previously extracted features. The test results based on real field data show that it outperforms the HMM model with the all-around hand-crafted features, the CNN-HMM model, and the MS-CNN-HMM model in both the feature extraction ability and the recognition accuracy in the case of little increase in time consumption. Moreover, the Euclidean distance between the posterior probabilities classified correctly and incorrectly is proposed to evaluate the test samples' feature distinguishability for different recognition models. Then the feature extraction capabilities of the models can be measured in an objective parameter.

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