In the ocean, light from the surface dissipates quickly leaving sound the only way to see at a distance. Different sediment types on the ocean floor and water properties like salinity, temperature, and ocean depth all change how sound travels across long distances. Hard sediment types, such as sand and bedrock, are highly reflective while softer sediment types, such as mud, are more absorptive and change the received sound upon arrival. Unfortunately, the vast majority of the ocean floor is not mapped and the expenses involved in creating such a map are far too great. Traditional signal processing methods in underwater acoustics attempt to localize sources and estimate seabed properties, but require a priori decisions and fall victim to ill conditioning and non-linear relationships between the unknowns and are computationally expensive. To address these problems, a deep learning method is proposed to distinguish between seabed types while also predicting source parameters such as source-receiver range from simulated training data. In this thesis, several studies are presented that explore the effectiveness of convolutional neural networks to make predictions from two types of sounds that propagated through the ocean: impulsive explosions and ship noise. These studies show that time-series signals and spectrograms contain sufficient information for deep learning, and additional preprocessing for feature extraction is not necessary. Training data considerations, such as randomness in the network weights and inclusion of representative variability are also explored. In all, this study shows that deep learning is a useful tool in underwater acoustics and has significant potential for seabed parameter estimation.
College and Department
Physical and Mathematical Sciences; Physics and Astronomy
BYU ScholarsArchive Citation
Van Komen, David Franklin, "Deep Learning to Predict Ocean Seabed Type and Source Parameters" (2020). Theses and Dissertations. 9213.
underwater acoustics, source localization, seabed sediment prediction, ocean modeling, machine learning, deep learning