High-accuracy acoustic sediment classification using sub-bottom profile data

Abstract

Sub-bottom sediment classifications have been widely used in marine science and engineering to obtain high-resolution information on types of sediments; however, these are often plagued by inaccuracies. Classification difficulties arise from the inability to effectively filter multiple reflections, extract representative lithology characteristic parameters, identify sub-bottom layer interfaces, extract image samples, control sample quality, optimise characteristic parameters, etc. To generate a highly accurate sub-bottom profile sediment map, a five-step classification method that considers two key lithology characteristic parameters of sub-bottom profile acoustic data was proposed. First, multiple reflections were filtered from the sea surface and sub-bottom layer interfaces of the primary signal. Second, two key characteristic parameters (relative backscattering intensity difference and attenuation compensation residual) were calculated. These reflect the relative differences in backscattering intensity and the attenuation compensation between adjacent interfaces based on the sound intensity attenuation model of a sub-bottom profile. Third, a combined method based on the sediment quality factor and peak trough of the echo signal loss level curve was employed to identify the actual interfaces between layers. An additional technique was proposed to determine the image sample width and preferred characteristic parameters. The resulting high-quality image samples and preferred characteristic parameters not only resulted in a faster convergence rate and increased ability of self-aggregation and identification, but also ensured that the training results met the convergence accuracy requirement. Ultimately, the preferred image samples were trained to classify the overall sub-bottom map of a selected test area of approximately 36 km2 in Bahai Bay, China. Compared with traditional methods, a considerably higher sediment identification accuracy was obtained. The experimental results indicate that the contribution rate of the two key lithology characteristic parameters was 65.49% according to principal component analysis, and the internal and external compatibilities were 97.98% and 84.76% for the training image samples, respectively. The total identification accuracy for the sub-bottom profile map was 98.2%. The two key characteristic parameters accurately captured the acoustic characteristics of sub-bottom sediments, significantly improving sediment classification. These results show that this method could be used to help refine the distributional estimates of submarine mineral resources.