Intelligent water perimeter security event recognition based on NAM-MAE and distributed optic fiber acoustic sensing system

Distributed optical acoustic sensing (DAS) based on phase-sensitive optical timedomain reflectometry can realize the distributed monitoring of multi-point disturbances along an optical fiber, thus making it suitable for water perimeter security applications. However, owing to the complex environment and the production of various noises by the system, continuous and effective recognition of disturbance signals becomes difficult. In this study, we propose a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on the novel mask mode of a Masked Autoencoders (MAE) and applies it to the intelligent event recognition in DAS. In this method, fewer but more accurate features are fed into the deep learning model for recognition by directly shielding the noise. Taking the fading noise generated by the system as an example, data on water perimeter security events collected in DAS underwater acoustic experiments are used. The NAM-MAE is compared with other models. The results indicate higher training accuracy and higher convergence speed of NAM-MAE than other models. Further, the final test accuracy reaches 96.6134%. It can be demonstrated that the proposed method has feasibility and superiority.

Automated summary

This study proposes a Noise Adaptive Mask-Masked Autoencoders (NAM-MAE) algorithm based on a novel mask mode of Masked Autoencoders (MAE) for intelligent event recognition in Distributed Optical Acoustic Sensing (DAS). The NAM-MAE algorithm achieves higher training accuracy and convergence speed compared to other models, with a final test accuracy of 96.6134%.