Machine Learning For Distributed Acoustic Sensors, Classic versus Image and Deep Neural Networks Approach
This work addresses pipeline monitoring and protection using DAS, presenting incremental improvements in detection speed and efficiency.
The paper tackled event detection in Distributed Acoustic Sensing (DAS) for pipeline monitoring by comparing classic machine learning with image-based deep learning, finding that the deep learning approach achieved six times lower detection delay and twelve times lower execution time.
Distributed Acoustic Sensing (DAS) using fiber optic cables is a promising new technology for pipeline monitoring and protection. In this work, we applied and compared two approaches for event detection using DAS: Classic machine learning approach and the approach based on image processing and deep learning. Although with both approaches acceptable performance can be achieved, the preliminary results show that image based deep learning is more promising approach, offering six times lower event detection delay and twelve times lower execution time.