Draupner 파랑자료의 통계적 해석
Statistical Analysis of Draupner Wave Data
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초록
In this paper, the time history of the surface elevation measured at the Draupner platform in the North Sea in 1995 is used to examine the statistical characteristics of the wave data. The wave statistics for 48 surface measurements, which contain three freak wave occurrences, are summarized. The quartiles, boxplots, correlations, and pair plots of 15 variables, along with the abnormality index, are presented. The kurtosis and skewness of the surface elevation are two variables that are highly correlated with the abnormality index, which defines freak waves. Principal coordinate analysis showed that the direction of the changes in the abnormality index agreed with the changes in the kurtosis and skewness. In addition, various wave heights, except the maximum wave height, showed a similar direction for the height changes, and various wave periods showed a similar direction for the period changes. Based on the correlations and PCA analysis, the kurtosis and skewness of the surface profiles are the two most important variables to predict the abnormality index.
Abstract
In this paper, the time history of the surface elevation measured at the Draupner platform in the North Sea in 1995 is used to examine the statistical characteristics of the wave data. The wave statistics for 48 surface measurements, which contain three freak wave occurrences, are summarized. The quartiles, boxplots, correlations, and pair plots of 15 variables, along with the abnormality index, are presented. The kurtosis and skewness of the surface elevation are two variables that are highly correlated with the abnormality index, which defines freak waves. Principal coordinate analysis showed that the direction of the changes in the abnormality index agreed with the changes in the kurtosis and skewness. In addition, various wave heights, except the maximum wave height, showed a similar direction for the height changes, and various wave periods showed a similar direction for the period changes. Based on the correlations and PCA analysis, the kurtosis and skewness of the surface profiles are the two most important variables to predict the abnormality index.
참고문헌 (10)
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