Seafloor classification based on deep-sea multibeam data—Application to the Southwest Indian Ridge at 50.47°E

Abstract

Seafloor classification based on multibeam data is an attractive approach due to its wide-coverage capability and relatively low cost. It has been successfully applied in shallow water, but may not work well in deep sea, particularly that with complex seafloor topography. In this paper, a method for deep-sea seafloor classification based on multibeam data is proposed. Firstly, bathymetry data are processed to obtain local slopes. Secondly, backscattering strength (BS) data from depth datagrams are corrected by calculated incident angles and insonified areas on the seafloor. Then BS data from several consecutive pings along the track are grouped into a set of small units in the across-track direction and the angular response curve of BS in each unit is extracted. Moreover, inversions of angular response curves are carried out to obtain the empirical parameter BSO based on a functional model. BSO values related to seafloor types and the standard deviations of inversion results are acquired at the same time. Finally, with the help of the prior information gained by analyzing the relationship between BSO values and ground truth, BSO values of all the units can be statistically analyzed and classified to generate a seafloor classification map. This method has been applied in a complex terrain area around the Duanqiao hydrothermal field on the Southwest Indian Ridge (SWIR) at 50.47°E, and six seafloor types have been classified according to the statistical analysis on BSO values and the visual ground truth.