Transformers and neural networks for estimation of parameters of multi-directional waves from rich statistics of FPSO motion signals

This study presents a machine learning (ML) framework for inverse estimation of parameters of multi-directional waves from moored FPSO (floating production storage offloading) motion-sensor synthetic data. A time-domain hull/mooring/riser coupled dynamics numerical simulation program was used to generate realistic vessel-response time series under varying wind–wave–current conditions, from which motion-statistical features up to 122 were extracted. These statistical features served as inputs to two different ML models, artificial neural networks (ANNs) and transformer-based ensemble (TBE) model. Then, different combinations of motion-statistical features were selected as inputs to the ML models to estimate key spectral parameters of multi-directional-waves including significant wave height, peak period, mean wave direction, spectral enhancement (peakedness) factor, and directional spreading, and the results were systematically compared. A more advanced ML method, the transformer architecture, combined with an ensemble approach, demonstrated improved robustness and generality across complex sea states. The systematic comparisons of ML performances with measured wave parameters versus artificially-generated wave parameters provided insights into how the hidden intrinsic correlations among wave parameters can improve the overall performance of the ML-based inverse wave estimation. The results highlight the potential of FPSOs as near-real-time wave-sensing devices. The estimated parameters can serve as crucial inputs for optimizing dynamic positioning (DP) systems and other active controls, as well as for digital-twin and smart-ship/platform applications, reducing reliance on external measurement systems.