Convolutional neural networks for signal identification and extraction and seabed classification

Sound underwater source (SUS) charges can be used for seabed characterization experiments. Many SUS were deployed during experiments in the New England MudPatch during 2017 and 2022. The goal of this work is to automatically detect and extract SUS signals in the dataset and then perform seabed classification on the extracted SUS signals. A binary classifier CNN is trained on simulated SUS charges and measured ambient noise to detect if a SUS signal is present in one minute pressure waveforms. The trained CNN model is then used to identify and extract the SUS signals from the measured data on the 52-channel PROTEUS L-array deployed by Applied Research Laboratories, University of Texas at Austin. This automated extraction process expedites the identification and time alignment of hundreds of SUS signals. The extracted signals will then be input to a ResNet-18 network trained on synthetic signals to perform seabed classification using a catalog of 34 seabeds. Lessons learned from the automated identification and extraction process will be presented as well as a statistical analysis of the seabed classification results. Work supported by the Office of Naval Research, Grant N00014-22-12402.