Classification of images derived from submarine fibre optic sensing: detecting broadband seismic activity from hydroacoustic signals

SUMMARY Distributed acoustic sensing (DAS) is an optoelectronic technology that utilizes fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognize seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2-D convolutional neural network architecture. The 55-km-long ocean-bottom fibre optic cable, located off Cape Muroto in southwest of Japan, was interrogated using DAS. Data were collected during two different monitoring time periods. Optimization of the model’s hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 labeled images, the highest-performing model achieved an average classification accuracy of 92 per cent, correctly classifying 100 per cent of instances in the geophysical class, 80 per cent in the non-geophysical class and 96 per cent in ambient noise class. This performance demonstrates the model's effectiveness in distinguishing between geophysical data, various sources of hydroacoustic energy, and ambient noise.