The slope of the curve is almost the same in every case. The range of values of parameters σLT and σST or DeLT and DeST might suggest

the erroneous conclusion that they too are correlated, but the evidence for the non-dependence of these parameters is the quantitative distribution of all possible pairs of σLT and σST ( Figure 4d). Pairs of these parameters lie within almost the whole area below the linear relation describing the equivalence of σST and σLT. A similar click here analysis was conducted for the relationship between σ and SLR ( Figures 4e, 4f, 4g): this is exponential. A negative linear relationship was also found between CMV0 and SLRMV ( Figure 4h). The unequivocal inference from the Proteasome purification foregoing is that for every deviation only one of these parameters contributes clearly independent information on the morphological diversity of the seabed. Spectral moments (Mi) and spectral skewness (γ) were found to be the most significant spectral parameters. The higher the order of a spectral moment, the lower the difference between the values. These features are highlighted by the correlation coefficients for Mi and Mj pairs for each deviation ( Figure 5). There is also a correlation between the spectral moments for LT and ST ( Figure 5);

the coefficient of this correlation, of the 2nd order, is close to 1. In view of the above, it was decided that only 0 to 3rd order spectral moments would be used for every deviation. The similarities between σ and M0 were also investigated. Detailed analysis showed that for every type of deviation there exists a linear dependence between σ2 and M0. It is clear from the above relationship that when spectral analysis was used, the addition of characteristics emerging from statistical parameters did not contribute any new knowledge regarding the sea bottom morphology in Brepollen. Determination

of the wavelet energy for successive scaling parameters is an excellent method for isolating morphological forms on a bathymetric profile, as it takes the magnitude of forms into consideration CYTH4 on the basis of the scaling parameter’s size. To verify the applicability of wavelet energies, the correlations between them were calculated (Figure 6). The correlation for every type of wavelet was much less than 1, even in the case of adjacent scaling parameters for the same type of wave. Analysis of the wavelet energies calculated for the example profile showed that the wavelet energy determined using the mexh wavelet for the scaling parameter a = 2i resembled that of the db7 wavelet for the scaling parameter a = 2i + 2 when i = 1,…,5. This observation was confirmed by wavelet correlation analysis ( Figure 6). The final point in the discussion of the application of wavelets to bathymetric profile analysis is the possible use of asymmetric wavelets, such as db7.