Ocean wave spectrum is the key to the response estimation of seagoing vessel whose structural integrity is of utmost importance. Efforts have been made by researchers to correctly estimate the ocean wave spectrum using so called ‘wave-buoy analogy’ concept, where the vessel is considered to behave as a wave buoy. The aim of this study is to develop a methodology through which the directional wave spectrum can be estimated using the concept of ‘wave-buoy analogy’. To achieve the objective, ocean wave was modeled with 10-parameter bimodal wave spectrum combining long- and short-wave component. These 10 parameters of bimodal wave spectrum were targeted by solving non-linear least square problem, which is formulated by error function quantifying the difference between model prediction and onboard measurement data. Model prediction is based on the linear relationship between the wave spectrum and response spectra and measurement data are directly from the sensors installed on the vessel. To solve the non-linear least square problem, Bayesian statistics-based probabilistic approaches, Markov-Chain Monte Carlo simulation (MCMC), were utilized. Well-known adaptive Metropolis–Hastings algorithm which is one of the most popularly used MCMC techniques was utilized to derive the spectrum parameters that best describe the directional wave spectrum. To validate the proposed methodology, pseudo measurement data generated by numerical analysis with different loading conditions were used. The application of the proposed methodology to the numerical analysis data confirmed that it accurately estimates the response at locations where sensors are not installed.

海浪谱是海船响应估计的关键，其结构完整性至关重要。研究人员一直在努力使用所谓的“波浪浮标类比”概念来正确估计海浪频谱，其中船舶被认为是波浪浮标。本研究的目的是开发一种方法，通过该方法可以使用“波浪浮标类比”的概念来估计定向波谱。为了实现这一目标，利用结合长波和短波分量的 10 参数双峰波谱对海浪进行建模。通过求解非线性最小二乘问题来确定双峰波谱的这10个参数，该问题是通过量化模型预测与船上测量数据之间的差异的误差函数来制定的。模型预测基于波谱和响应谱之间的线性关系，测量数据直接来自安装在船上的传感器。为了解决非线性最小二乘问题，采用了基于贝叶斯统计的概率方法，即马尔可夫链蒙特卡罗模拟（MCMC）。著名的自适应 Metropolis-Hastings 算法是最常用的 MCMC 技术之一，用于导出最能描述方向波频谱的频谱参数。为了验证所提出的方法，使用了不同载荷条件下数值分析生成的伪测量数据。将所提出的方法应用于数值分析数据证实，它可以准确估计未安装传感器的位置的响应。