海浪谱参数的估计
Estimating the parameters of ocean wave spectra
摘要：
风生波通常被视为随机过程。人们特别感兴趣的是它们的谱密度函数，这些函数通常以某种参数形式表示。当对结构响应或其他工程问题进行建模时，此类谱密度函数用作输入。因此，从观测到的波浪记录中准确和精确地恢复这种形式的参数是重要的。已知当前技术难以恢复某些参数，尤其是峰值增强因子和光谱尾部衰减。我们介绍了一种方法，从统计文献中，被称为去偏惠特尔可能性，并解决一些实际问题，其实施的背景下，风产生的波浪。我们证明，通过数值模拟，去偏惠特尔似然优于目前的技术，如最小二乘拟合，无论是在精度和精度的恢复参数。我们还提供了一种方法来估计参数估计的不确定性。我们执行一个例子分析记录的数据集新西兰海岸，以说明一些额外的实际问题时出现的参数估计光谱从观测数据。
英文摘要：
Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form. Such spectral density functions are used as inputs when modelling structural response or other engineering concerns. Therefore, accurate and precise recovery of the parameters of such a form, from observed wave records, is important. Current techniques are known to struggle with recovering certain parameters, especially the peak enhancement factor and spectral tail decay. We introduce an approach from the statistical literature, known as the de-biased Whittle likelihood, and address some practical concerns regarding its implementation in the context of wind-generated waves. We demonstrate, through numerical simulation, that the de-biased Whittle likelihood outperforms current techniques, such as least squares fitting, both in terms of accuracy and precision of the recovered parameters. We also provide a method for estimating the uncertainty of parameter estimates. We perform an example analysis on a data-set recorded off the coast of New Zealand, to illustrate some of the extra practical concerns that arise when estimating the parameters of spectra from observed data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
