Event detection method comparison for distributed acoustic sensors using φ-OTDR

We investigate two kinds of event detection methods, support vector machine (SVM) and extreme learning machine (ELM), for distributed acoustic sensors (DAS) using phase-sensitive optical time domain reflectometer (phi-OTDR). We propose a heterodyne coherent phi-OTDR platform using an electric mixer, select spectrogram image feature (SIF) as a feature vector, and make comparison of the performance of 4 event classifiers. Theoretical and experimental analysis point out that an ELM classifier with RBF kernel is optimal in the detection of 5 sound events that has high identification accuracy, less training time, less testing time and strong noise immunity, which promises to be the first choice of event detection method in phi-OTDR systems in industrial applications.