Data Driven Ship Motion Prediction

Abstract

Short-term forecasting of six-degree-of-freedom (6-DoF) motions of a semi-submersible vessel supports the safe execution of motion-restricted offshore operations such as ROV launch and recovery, crew transfers, and crane lifts.
Industry-standard systems reconstruct the surrounding wave field from X-band navigation radar images, propagate it forward using linear wave theory, and convert it to motion through pre-computed Response Amplitude Operators (RAOs).
Existing machine learning approaches to vessel motion forecasting typically rely on past motion alone, sometimes augmented with scalar wave parameters or sparse wave measurements, and therefore do not exploit the full spatial structure of the surrounding wave field.
No prior study has demonstrated direct prediction of 6-DoF vessel motions from raw X-band radar image sequences within a single end-to-end learnable model.

This thesis addresses that gap by proposing the Spatiotemporal Cross-Attention Transformer (SCAT), a neural architecture that takes sequences of X-band radar images together with 45~seconds of past 6-DoF motion and predicts the next 60~seconds of motion.
A convolutional radar encoder produces a tokenised spatiotemporal representation of the radar history, which is queried through cross-attention by learned future motion tokens.
Both radar and motion representations are conditioned on the relative wave--vessel heading through a learned embedding.
Four model configurations are studied by varying the radar input representation and training loss in order to identify the most effective design.
All variants are pre-trained on synthetic data spanning five JONSWAP sea states and eight wave directions, and then fine-tuned on 20~minutes of operational data recorded aboard a semi-submersible rig in the Norwegian Sea.
Their performance is compared against state-of-the-art phase-resolved predictions provided alongside the same recordings and evaluated on four five-hour blocks covering representative wave headings.

On synthetic data, all four variants achieve heave correlations between 0.89 and 0.94 and 6-DoF mean correlations between 0.87 and 0.91 at the 60-second horizon, showing that the architecture can learn wave-to-motion relationships directly from radar imagery.
After fine-tuning, the selected variant reaches heave correlations of 0.69 to 0.76 across all four real-data blocks and reproduces per-DOF directional patterns consistent with the underlying wave physics.
Joint training across all blocks further improves correlation in three of the four blocks.
The state-of-the-art baseline still leads the 6-DoF mean correlation at 60~seconds by 0.14 to 0.31, indicating that synthetic-to-real domain mismatch remains the main limitation.

SCAT is therefore presented as a proof of concept showing that an attention-based model operating on raw radar imagery and motion history can learn physically meaningful 6-DoF motion predictions without hard-coded wave physics, dispersion relations, or RAOs.
These results establish a viable and complementary path toward data-driven maritime motion prediction and suggest that reducing the synthetic-to-real gap through higher-fidelity pre-training data is the most direct route toward operational deployment.