In order to select sections for analysis, two classifying parameters
were implemented. Every measurement on a bathymetric profile could become an Initial Profile Point (IPP) for the analysis on condition that there was an End Profile Point (EPP) on the profile 256 m distant along the measuring route. The first parameter was calculated by finding the average deviation of the records between IPP and EPP from a linear fit between them. The lower the value of this parameter, the closer the location of a depth measurement to the straight segment. The other parameter was the real distance between IPP and EPP; this was used if measurements were being made while sailing haphazardly in the vicinity of a specific point. It was assumed that when the average deviation from the linear fit MS-275 solubility dmso was more than 2% of its length or the distance between IPP and EPP was less than 98% of its length, the profiles did not fulfil the straightness requirement. The following data analysis scheme was employed to characterise morphological seabed differences: – calculation of mathematical parameters describing bathymetric section diversification;
The paper describes all these steps in detail. Statistical, spectral and wavelet transformations, as well as fractal and median filtration parameters were used in this work. These parameters were determined not for the depth profiles, but for the deviations from the mean value (MV), linear trend (LT) and square trend (ST) of all straight segments of profiles with a length of 256 m selected by the method Antiinfection Compound Library research buy described above (Figure 2).
The usefulness of statistical parameters for describing morphological diversification was shown in topographical analyses of a whole planet (Aharonson et al., 2001, Nikora and Goring, 2004 and Nikora and Goring, 2005) but also of smaller regions (Moskalik & Bialik 2011). The following statistical parameters were determined: – the average absolute value of deviations (DeMV, DeLT, DeST); and parameters based on semivariograms of deviations: – linear regressions (SLRMV, SLRLT, SLRST); The range of interaction is the limit of increase in value of semivariograms (ωMV, ωLT, ωST), with its imposed limit of half of the length of the segments analysed. The usefulness of spectral analysis for describing morphological features was also demonstrated for planet topography (Nikora & Goring 2006) and also for smaller Atezolizumab cost regions like bathymetric maps (Lefebvre & Lyons 2011) and linear profiles (Goff et al., 1999, Goff, 2000 and Tęgowski and Łubniewski, 2002). The following parameters were determined for the bathymetric profiles collected at Brepollen: – the total spectral energies in the form of integrals of power spectral density deviations from the bathymetric profile (SMVk1,SLTk1,SSTk1): equation(1) Sk1=∫0kNyCkdk, Additional analysis involved the use of wavelet transforms, also used in the analysis of bathymetric measurements (Little et al., 1993, Little, 1994, Little and Smith, 1996 and Wilson et al.
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