Abstract
We present a feature extraction method for a feedforward-type neural network (FNN) designed to realize a distributed acoustic sensing (DAS) technique that suits conventional optical fiber. This is, to the best of our knowledge, the first trial in which an FNN is used to interpret the field communication infrastructure type surrounding optical cables. Three classes are taken to represent the field environment: the cable tunnel (class 1), circular duct (class 2), and overhead area (class 3). We investigate and compare frequency- and time-domain feature extraction. We also show that the frequency-domain features yielded by spectral envelope shape (SES) processing have better performance than simple fast Fourier transform features. Two types of time-domain features are verified: one is the short-time maximum magnitude (STMM), which shows the largest value in the time frame, and the other is the short-time average magnitude (STAM), which indicates the average value in a time frame. Note that all features are optimized for multi-class classification. In this paper, we present the suitable number of both features and the number of training iterations. An accuracy rate of 79.0% is achieved using FNN analysis with the features studied here. Furthermore, by considering the similarity of neighboring classes, classes are refined into higher probability classes. As a result, accuracy is improved to 87.2%.
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