Unsupervised Anomaly Detection Applied to Φ-OTDR

Abstract

Distributed acoustic sensors (DASs) based on direct-detection Φ-OTDR use the light–matter interaction between light pulses and optical fiber to detect mechanical events in the fiber environment. The signals received in Φ-OTDR come from the coherent interference of the portion of the fiber illuminated by the light pulse. Its high sensitivity to minute phase changes in the fiber results in a severe reduction in the signal to noise ratio in the intensity trace that demands processing techniques be able to isolate events. For this purpose, this paper proposes a method based on Unsupervised Anomaly Detection techniques which make use of concepts from the field of deep learning and allow the removal of much of the noise from the Φ-OTDR signals. The fact that this method is unsupervised means that no human-labeled data are needed for training and only event-free data are used for this purpose. Moreover, this method has been implemented and its performance has been tested with real data showing promising results.

Keywords

• distributed acoustic sensors
• Φ-OTDR
• deep learning
• autoencoder
• Unsupervised Anomaly Detection