Artificial intelligence-driven distributed acoustic sensing technology and engineering application

The Distributed Acoustic Sensing (DAS) system based on Phase-Sensitive Optical Time Domain Reflectometry (Φ-OTDR) uses narrow-linewidth lasers as high-coherence light sources, utilizing Rayleigh scattering signals in the fiber for real-time monitoring. During the detection of pulse propagation, the scattered light interferes, and the detected signal is the coherent superposition of Rayleigh scattering signals within the pulse width. This technology offers advantages such as long measurement distances, high spatial resolution, and a large dynamic range, efficiently capturing acoustic wave propagation information in the environment.

AI in DAS mainly includes three stages: data acquisition, preprocessing, and machine learning model construction. Data is the foundation of artificial intelligence, facing challenges such as difficulties in data acquisition and large data volumes. The establishment of public DAS datasets and the development of data augmentation algorithms help promote further advancements in the AI+DAS field. Data preprocessing involves two steps: denoising and feature extraction. Denoising algorithms effectively reduce the impact of Gaussian noise, phase noise, and fading on the signal. Feature extraction improves the accuracy of classification models by selecting appropriate features. In model construction, traditional machine learning methods such as Support Vector Machines (SVM) and Hidden Markov Models (HMM) are still widely used. Deep learning models like Convolutional Neural Networks (CNN) are becoming mainstream algorithms for DAS pattern recognition. Advanced learning paradigms, including semi-supervised learning, unsupervised learning, and transfer learning, are also gradually being applied to DAS event recognition, aiming to enhance recognition accuracy and model robustness.

AI-driven DAS technology demonstrates broad application potential across multiple industries. In the transportation sector, DAS technology can be used for infrastructure monitoring and intelligent transportation systems. In the energy sector, DAS technology is applied to oil and gas pipeline monitoring and power system monitoring. In the security field, DAS technology provides early warning and protection for critical facilities by monitoring surrounding vibrations and acoustic signals.