Multi-dimensional Information Extraction and Utilization in Smart Fiber-optic Distributed Acoustic Sensor (sDAS)

Current fiber-optic distributed acoustic sensor (DAS) target recognition technologies continue to prioritize updating the feature learning tools while neglecting varying contributions of information from different dimensions. Few works focus on designing recognition algorithms from the perspective of multi-dimensional information extraction and utilization. In this paper, we introduce an end-to-end three-dimensional attention-assisted convolutional neural network (3-D ACNN) into DAS for the first time. This novel approach allows for automatic and concurrent extracttion of three-dimensional input in time, frequency, and space, aiming to cooperatively recognize sensing targets with higher accuracy. Our new scheme also includes comparative examination of 2-D and 3-D attention mechanisms to leverage the most efficient information in various dimensions, thereby enhancing recognition accuracy while maintaining computation efficiency. Field tests demonstrate that the 3-D CNN outperforms commonly used 2-D networks with only time-frequency (T-F), time-space (T-S), or space-frequency (S-F) inputs, improving recognition accuracy from 95.47% to 98.67%. Moreover, the proposed 3-D ACNN can further improve accuracy up to 99.33%, outperforming the basic 3-D CNN. It is also found that T-F information in DAS is richer and more recognized than T-S and S-F information. The use of 3-D attention yields better results than 2-D attention. Additionally, the processing time required for each spatial sensing point is only 0.22 ms on a commercial GPU (NVIDIA GeForce (R) GTX 1080 Ti), which is approximately 1/171 of the time required for the equivalent 2-D network with the same T-F input (38.52 ms). The 3-D ACNN enabled smart fiber-optic DAS (sDAS) represents a significant breakthrough, enabling fast and accurate multi-dimensional collaborative recognition. This advancement is expected to have a substantial impact for distributed sensing applications.