Current State of Deep Ocean Bathymetric Exploration

The paper presents current state of bathymetric survey concerning deep ocean rather than shallow areas, which are better surveyed due to safety of navigation concerns. Rules and requirements of the new challenge, called the Shell Ocean Discovery XPRIZE, became a starting point for a discussion about the possibilities of mapping large areas of the ocean using up-to-date and new technology. The amount of bathymetric data available nowadays and the current state of ocean map compilations are also discussed in the paper as a motivation to inspire the new initiatives in the deep ocean

The Ocean-Going Autonomous Ship—Challenges and Threats

Unmanned vehicles have become a part of everyday life, not only in the air, but also at sea. In the case of sea, until now this usually meant small platforms operating near shores, usually for surveying or research purposes. However, experiments with larger cargo vessels, designed to operate on the high seas are already being carried out. In this context, there are questions about the threats that this solution may pose for other sea users, as well as the safety of the unmanned vehicle itself and the cargo or equipment on board. The problems can be considered in the context of system reliability as well as the resilience to interference or other intentional actions directed against these objects—for example, of a criminal nature. The paper describes the dangers that arise from the specificity of systems that can be used to solve navigational problems, as well as the analysis of the first experiences of the authors arising from the transit of an unmanned surface vessel (USV) from the United Kingdom to Belgium and back, crossing the busiest world shipping route—the English Channel

Automatic Identification of Internal Wave Characteristics Affecting Bathymetric Measurement Based on Multibeam Echosounder Water Column Data Analysis

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 Autonomous Underwater Vehicle Integrated with the Unmanned Surface Vessel Mapping the Southern Ionian Sea. The Winning Technology Solution of the Shell Ocean Discovery XPRIZE

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