Ship As a Wave Buoy - Estimating Full Direction Wave Spectra From In-service Ship Motion Measurements Using Deep Learning

The ocean wave directional spectrum is an important wave characteristic for maritime safety and navigation. Accurate estimation of directional spectra in real-time is a challenge. In this study we aim to reconstruct the directional spectra from ship motions using a deep convolutional encoding-decoding neural network. In-service measurements of ship motions and wave spectra from a WAMOS II wave scanning radar were used to train the neural network. The data was collected from a frigate type ship for a period of two years. We demonstrate that the deep convolutional encoding-decoding neural network is successful in predicting the directional spectra in real-time. At the same time, we conclude that more data is needed for a better prediction performance , including a more complete coverage of operational conditions.