Automatic Marine Sub-Bottom Sediment Classification Using Feature Clustering and Quality Factor

Abstract

It has been proven that the quality factor (Q) is important for marine sediment attenuation attribute representation and is helpful for sediment classification. However, the traditional spectral-ratio (SR) method is affected by the interference effect caused by thin interbeds, which seriously degrade the performance of the SR method. Aimed at this problem, a novel method based on variational mode decomposition (VMD) correlation analysis is presented in this paper, which realizes the separation between interference reflections and effective signals. After obtaining the effective signals, a frequency band selection method is employed to weaken the influence of background noise. To better apply the proposed method to large-area sediment classification, a sediment clustering method based on texture features is introduced. Experiments on real data validate the effectiveness of the proposed method. The accuracy of the correlation analysis method using the modified parameters is 94 percent. The stability improvement in the standard deviation of the Q calculation can reach more than 90 percent. Moreover, the interpretation of sediment categories using the mean value of Q fits the drilling data well. It is believed that the proposed method has huge potential for the engineering applications in sub-bottom sediment classification.