A CNN for Range and Seabed Estimation on Normalized and Extracted Time-Series Impulses

Abstract

Deep learning techniques have the potential to address real-time source localization and seabed characterization from passive ocean acoustic signals. The challenge grows when considering single-sensor cases that lack absolute travel time with no known source or environmental parameters. To address this, convolutional neural networks (CNNs) are trained to predict the source–receiver range and the seabed type on extracted and normalized 1-s pressure time series from explosive charges measured during the 2017 Seabed Characterization Experiment. To investigate the impact of ocean variability, two training data sets containing different sets of water sound-speed profiles (SSPs) are used. CNNs trained on the first synthetic data set, which contains a static ocean depth but significant variability in water SSPs, predict seabed class more accurately than the second data set from the measured data. Conversely, CNNs trained on the second synthetic data set, which contains fewer SSPs (similar to those measured during the experiment) and multiple ocean depths, predict the source–receiver range more accurately than the first data set from the measured data. The CNNs, with a degree of uncertainty, predict the seabed type and the source range when trained on synthetic data that have been stripped of source–receiver information, such as arrival time and amplitude. The CNNs trained to only predict the seabed type also make more accurate predictions on the seabed type when compared to those for multiple prediction types. This study highlights the importance of accounting for ocean variability and prediction uncertainties in training deep learning algorithms for marine applications.