Application of Hybrid Techniques (Self-Organizing Map and Fuzzy Algorithm) Using Backscatter Data for Segmentation and Fine-Scale Roughness Characterization of Seepage-Related Seafloor Along the Western Continental Margin of India

In this paper, a seafloor characterization technique to unravel the number of data classes using multibeam echo-sounding backscatter data has been demonstrated. An application of self-organizing maps (SOMs) to backscatter profile data has been developed to determine the number of classes. Consequently, the fuzzy C-means (FCM) method is employed thereafter, using the number of class information of the backscatter profiles segmentation. The use of the soft-computational technique for seafloor backscatter data facilitates in achieving stationary profile data sets suitable for seafloor roughness model application. The power spectral density (PSD) function of the segmented profiles provides the power law parameters (
$\beta$
and
$a'$
) through curve fitting, using power law expression. The data acquired from western continental margin of India (WCMI), off Goa, reveal five distinct classes of backscatter strength having different segment lengths. The estimated roughness parameters (
$\beta$
and
$a'$
) of the segmented profiles provide quantitative information about the area seafloor roughness. A gridded map of the estimated roughness parameter
$(\beta)$
is generated using the “krigging” method. The gridded map and the class of the segmented profiles overlaid on the backscatter map have been presented, which is a first time application relevant for the understanding of the seafloor. The present study underscores a combination of soft-computational (SOM and FCM) and numerical techniques (power spectral density at short and fine scales) to effectively recognize the seafloor processes and the associated sedimentological dynamics in a complex geographical environment (including the pockmarks and faulted structures), which is subjected to strong bottom currents and seasonal upwelling.