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The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site investigations because of the unsoundness in attribute calculation. To overcome this problem, a new reliable method combining VMD (variational mode decomposition) and WVD (Wigner–Ville distribution), as well as relaxation time, is presented. Since the number of modes in classical VMD should be provided in advance, a modified VMD algorithm, MVMD (modified variational mode decomposition), is proposed here, where the distribution of the frequency domain of modes is taken into account to automatically determine the number of modes. Through the relaxation time model, the IF data of a series of pings calculated through MVMD-WVD are transformed into a relaxation time map. A robust estimation algorithm is applied to the relaxation time map to reduce the effects of interferences and obtain robust relaxation times. The final relaxation time data are used to determine the sediment types. Real data from SBP experiments, as well as borehole sampling and geotechnical analysis results, verified the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

Traditional manual horizon picking is time-consuming and laborious, while automatic picking methods often suffer from the limited scope of their applications and the discontinuity of picked results. In this paper, we propose a novel method for automatic horizon picking from sub-bottom profiles (SBP) by an improved filtering algorithm. First, a clear and fine SBP image is formed using an intensity transformation method. On this basis, a novel filtering method is proposed by improving the multi-scale enhancement filtering algorithm to obtain clear horizons from an SBP image. The improvement is performed by applying a vertical suppression weighting term based on the form of logistic function, which is constructed by using the eigenvectors from the Hessian matrix. Then, the filtered image is segmented using a threshold method, and the horizon points in the SBP image are picked. After that, a horizon linking method is applied, which uses the horizon directions to refine the picked horizon points. The proposed method has been verified experimentally, and accurate and continuous horizons were obtained. Finally, the proposed method is discussed and some conclusions are drawn. [ABSTRACT FROM AUTHOR]