Abstract: A method for calculating channel-frequency feature for detecting multi channel (propagated spatially) activities in DAS sensor data is provided. This method can be generalized for linearly spaced sensor arrays. In the method, spectrograms of different channels are generated and frequency features are calculated for different time windows. In channel frequency image, frequency features are concatenated in spatial domain, so that horizontal axis represents the spatial dimension.

Abstract: A method for a multi-channel acoustic event detection and classification for weak signals, operates at two stages; a first stage detects a power and probability of events within a single channel, accumulated events in the single channel triggers a second stage, wherein the second stage is a power-probability image generation and classification using tokens of neighbouring channels.

Abstract: A method that detects the location of undesired levels of signal loss for DAS systems. In the method, raw data is received from a DAS system with dual photodetector. Statistics are obtained from the received raw data, then processed according to the dual photodetector. The obtained statistical data are reconstructed to remove noise and the reconstructed signal is used to form the power statistics of the DAS signal in each channel. The power statistics are expected to be linearly decreasing with the distance from the sensor. A change detection algorithm is developed to detect the possible undesired levels of signal loss and to find the location of the signal loss.

Abstract: A frequency response estimation method to compensate for channel differences in distributed acoustic sensing systems include two compensation algorithms, online and offline, and these two compensation algorithms are presented to generate standardized mel-frequency features, as an input to neural networks. By this scheme, the variance of mel-frequency feature space is decreased and normalized among different channels, which enables to use less training data and smaller architectures for classification and anomalous event detection tasks.

Abstract: A method for a multi-channel acoustic event detection and classification for weak signals, operates at two stages; a first stage detects a power and probability of events within a single channel, accumulated events in the single channel triggers a second stage, wherein the second stage is a power-probability image generation and classification using tokens of neighbouring channels.