Masanao Sumiyoshi

The accuracy of multibeam echosounder bathymetric measurement depends on the accuracy of the data of the sound speed layers within the water column. This is necessary for the correct modeling of ray bending. It is assumed that the sound speed layers are horizontal and static, according to the sound speed profile traditionally used in the depth calculation. In fact, the boundaries between varying water masses can be curved and oscillate. It is difficult to assess the parameters of these movements based on the sparse sampling of sound velocity profiles (SVP) collected through a survey; thus, alternative or augmented methods are needed to obtain information about water mass stratification for the time of a particular ping or a series of pings. The process of water column data collection and analysis is presented in this paper. The proposed method updates the sound speed profile by the automated detection of varying water mass boundaries, giving the option to adjust the SVP for each beam separately. This can increase the overall accuracy of a bathymetric survey and provide additional oceanographic data about the study area.

The methods of data collection, processing, and assessment of the quality of the results of a survey conducted at the Southern Ionian Sea off the Messinian Peninsula, Greece are presented. Data were collected by the GEBCO-Nippon Foundation Alumni Team, competing in the Shell Ocean Discovery XPRIZE, during the Final Round of the competition. Data acquisition was conducted by the means of unmanned vehicles only. The mapping system was composed of a single deep water AUV (Autonomous Underwater Vehicle), equipped with a high-resolution synthetic aperture sonar HISAS 1032 and multibeam echosounder EM 2040, partnered with a USV (Unmanned Surface Vessel). The USV provided positioning data as well as mapping the seafloor from the surface, using a hull-mounted multibeam echosounder EM 304. Bathymetry and imagery data were collected for 24 h and then processed for 48 h, with the extensive use of cloud technology and automatic data processing. Finally, all datasets were combined to generate a 5-m resolution bathymetric surface, as an example of the deep-water mapping capabilities of the unmanned vehicles’ cooperation and their sensors’ integration.