用机器学习精准预测深海浮式平台运动响应，实时性与精度远超传统方法。
An application of machine learning to the motion response prediction of floating assets
- 采用梯度提升集成模型结合自研风向调整求解器，处理多变量非线性响应。
- 预测误差低于5%，航向精度达2.5度，优于传统频域方法。
- 已部署于实际海上设施，适合实时监控与工程决策场景。
在随机海洋气象条件下，实时预测浮动海上资产的运动行为仍是海上工程的重大挑战。尽管传统经验法和频域方法在良好工况下表现良好，但在极端海况和非线性响应下表现不佳。本研究提出一种监督式机器学习方法，利用多变量回归预测水深400米的转塔系泊船舶的非线性运动响应。构建了包含梯度提升集成方法与定制被动风向调整求解器的机器学习流程，基于约10⁶个样本、涵盖100个特征的数据集进行训练。模型在各类海洋气象条件下，对关键系泊参数的平均预测误差小于5%，船体航向精度达到2.5度以内，显著优于传统频域方法。该框架已成功应用于实际作业设施，验证了其在海上环境中实时监测与运营决策中的有效性。
原文摘要 · Abstract (English)
The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.
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