ASSESSMENT OF SEA STATE ESTIMATION WITH CONVOLUTIONAL NEURAL NETWORKS BASED ON THE MOTION OF A MOORED FPSO SUBJECTED TO HIGH-FREQUENCY WAVE EXCITATION

Motion-based wave inference has been extensively discussed over the past years to estimate sea state parameters from the measured motions of a vessel. Most of those methods rely on the linearity assumption between waves and ship response and present a limitation related to high-frequency waves, whose first-order excitation is mostly filtered by the vessel. In a previous study in this project, the motion of a spread-moored FPSO platform, associated with a dataset of environmental conditions, was used to train convolutional neural networks models so as to estimate sea state parameters, displaying good results, even for high-frequency waves. This paper further explores this supervised learning inference method, focusing on the estimation of unimodal high-frequency waves along with an evaluation of particular features related to the approach. The analysis is performed by training estimation models under different circumstances. First, models are obtained from the simulated platform response out of a dataset with synthetic sea state parameters, that are uniformly distributed. Then, a second dataset of metocean conditions, with unimodal waves observed at a Brazilian Offshore Basin, is considered to verify the behavior of the models with data that have different distributions of wave parameters. Next, the input time series are filtered to separate first-order response and slow drift motion, allowing the derivation of distinct models and the determination of the contribution of each motion component to the estimation. Finally, a comparison among the outcomes of the approach based on neural networks evaluated under those conditions and the results obtained by the traditional Bayesian modeling is carried out, to assess the performance presented by the proposed models and their applicability to face one of the classical issues on motion-based wave inference.