Intrusion-Localization Method Using Distributed Optical Sensing

Distributed Sagnac loop (SI) is a simple vibration-based sensing system, which is cost-effective and simple to implement. Because of its simplicity, its performance is low compared to other complex structure sensors. Recently, machine learning (ML) has gained significant popularity and found applications in almost each science discipline. ML is an artificial intelligence sub-field, which intimates human intelligence. Sensing is one area that has exploited ML techniques to improve and enhance the reliability of sensing systems. Using ML to enhance the reliability of location identification in SI sensing systems was proposed using frequency null as a feature for training the ML model. Extracting frequency null features for ML training degrades the system performance because of their lower power, especially under harsh environments that are very noisy. In this work, we propose using an ML model for improving event localization in SI sensing systems by exploiting time-based features. A training set containing 190 events is generated over 9.5 km of effective sensing fiber. Then, the training features are extracted using an auto-encoder neural network, which reduces dimensionality and performs denoising. After that, an ML model is developed using a random forest (RF) algorithm for predicting the event location. The obtained results indicate that the mean absolute error (MAE) of 13.4 m over 9.5 km effective sensing fiber. The percentage of samples with an MAE > 50 m is 0.52%, which is very low.