Estimating the parameters of ocean wave spectra

Estimating the parameters of ocean wave spectra
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估计海浪谱的参数

DOI:
10.1016/j.oceaneng.2021.108934
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发表时间:
2021
期刊:
影响因子:
5
通讯作者:
Grainger J
Grainger J
中科院分区:
工程技术2区
文献类型:
--
作者:
Grainger J

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风产生的波浪通常被视为随机过程。人们对它们的谱密度函数特别感兴趣,这些函数通常以某种参数形式表示。在对结构响应或其他工程问题进行建模时,此类谱密度函数可用作输入。因此,从观测到的波浪记录中准确且精确地恢复这种形式的参数非常重要。众所周知,当前的技术很难恢复某些参数,尤其是峰值增强因子和谱尾衰减。我们引入了统计文献中的一种方法,称为去偏惠特尔似然法,并解决了有关其在风生波浪背景下实施的一些实际问题。我们通过数值模拟证明,在恢复参数的准确性和精确度方面,去偏 Whittle 似然优于最小二乘拟合等现有技术。我们还提供了一种估计参数估计不确定性的方法。我们对新西兰海岸记录的数据集进行了示例分析,以说明根据观测数据估计光谱参数时出现的一些额外的实际问题。
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.
关于JONSWAP谱峰参数估计的不确定性
DOI: 10.1115/omae2018-78386
发表时间: 2018
期刊: Volume 3: Structures, Safety, and Reliability
影响因子: --
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时间序列分析中的频谱参数估计† †这项工作的部分内容得到了荷兰纯粹研究促进组织 (Z.W.O.) 资助 B62-165 的支持。
DOI: --
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影响因子: 2.7
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