Mixed event separation and identification based on a convolutional neural network trained with the domain transfer method for a φ-OTDR sensing system

Abstract

In phase-sensitive optical time domain reflectometer (φ-OTDR) based distributed acoustic sensing (DAS), correct identification of event types is challenging in complex environments where multiple events happen simultaneously. In this study, we have proposed a convolutional neural network (CNN) with a separation module and an identification module to simultaneously separate a mixed event into individual single-event components and identify each type of component contained in the mixed event. The domain transfer method is used in the training, fine-tuning, and testing of the proposed CNN, which saves 94% of the workload for massive DAS data collection and signal demodulation. A fine-tuning stage is added to minimize the impact of the dataset shift between the audio data and DAS data, hence enhancing the separation and identification performance. The model has good noise tolerance and achieves nearly 90% identification accuracy even at a relatively low signal-to-noise ratio (SNR). Compared with the conventional method using DAS data for training, domain transfer using a large amount of diverse audio data for training well generalizes the model to the target domain and hence provides more stable performance with only little degradation of identification accuracy.