Sea wave height analysis method

Abstract

The invention discloses a sea wave height analysis method. The sea wave height analysis method comprises a fast band-pass filtering method for signal mode decomposition; a model decomposition trend is used for substituting for a sectioned polynomial fitting trend in an MF-DFA (multifractal detrended fluctuation analysis) method. A partition function method for multifractal analysis and the MF-DFA method are applied to sea wave height testing data analysis to indicate that a wave height sequence has a weak multi-fractal characteristic. The sea wave height analysis method makes improvements aiming at the problems existing in the MF-DFA method and establishes an MF-DFA method based on signal mode decomposition; testing data verification indicates that the improved method has the capability of avoiding the defects of the original method and has certain advantages on the basis of well meeting an elimination trend of the original method.

Description

technical field [0001] The invention relates to a method for analyzing wave height of ocean waves based on multifractal MF-DFA. Background technique [0002] The so-called fractal, according to the definition given by Mandelbrot, the founder of fractal analysis, refers to "a fractal is a shape made of parts similar to the whole in some way (A fractal is a shape made of parts similar to the whole in some way)", here "a certain The "similar way" can be self-similarity, self-affine similarity or statistical similarity, etc. The similarity can be in time or in physical space. Fractal phenomena widely exist in nature (such as the shape of the coastline, the distribution of rivers, and the growth shape of trees), as well as in physics and chemistry (such as fractal noise, soil particle size distribution), and even in economics and finance. In learning (such as fluctuations in exchange rates, changes in stock prices). In fact, fractal analysis is also widely used in the above fie...

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Problems solved by technology

[0004] Eliminating trend fluctuations is an important part of the MF-DFA method, but there are currently the following defects in the calculation of this part: First, the fitted polynomials are discontinuous at the connection points of adjacent intervals, which will generate new pseudo-fluctuation errors [7] ;Secondly, the selection of the fitting polynomial order is very subjective, the low order cannot reflect the fluctuation trend of the data well, and the high order will produce overfitting phenomenon

This calculation method has three defects: first, it is believed that the annual extreme wave heights follow the same probability distribution no matter in the short term or in the long term; The quantities are strictly self-similar; 3. Only the annual extreme wave height (only one data per year) is used in the calculation, while most observation data do not use

Due to the existence of these flaws, the results of the extrapolation are unbelievable

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