Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

Abstract

Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

A High–Efficiency Side–Scan Sonar Simulator for High–Speed Seabed Mapping

“…In Figure 1 , a signal–beam side–scan sonar forms a narrow receiving beam of (in degrees) along the azimuth, which is determined by the length and operation frequency of the sensor as where is the sound speed in the water. is the angular beamwidth in elevation, which is often rather large to ensure strip coverage [ 20 ]. The receiving beam footprint has a width of at the maximum operation range along the navigation direction.…”

Open-source approach for reproducible substrate mapping using semantic segmentation on recreation-grade side scan sonar datasets

“…Supervised approaches, particularly deep learning for semantic segmentation, have recently been used to characterize SSS datasets collected with survey-grade and recreation-grade sonar instruments (Steiniger et al, 2022). Deep learning based approaches have included segmenting the water column (Zheng et al, 2021), prominent lines (Wu et al, 2019;Wang et al, 2020), discriminating objects from sonar shadows and background (Song et al, 2021), sand waves (Yu et al, 2019;Li et al, 2019;Nian et al, 2021), and seagrass and potholes (Rahnemoonfar and Dobbs, 2019). However, only four of these studies are focused on benthic habitat and primarily consider binary classifications, limiting their application to heterogeneous habitats.…”