| Project Detail |
Improved approach to modelling sediment transfer from shelf to basin
Shelf-to-basin sedimentary processes involve complex interactions between ocean currents and seafloor sediments, affecting ecology, marine geo-hazards, continental margins evolution and global climate. However, quantitative tools to model these processes are lacking. The EU-funded GEM-SBP project will use ultra-high-resolution seismic reflection data to develop modelling techniques that couple sediment and oceanographic properties. The southeastern Levant basin will be the study area. The research will create seismic processing workflows for imaging ocean and near-seafloor sediments. Then, researchers will use advanced statistical techniques to generate high-resolution models of ocean and seafloor properties. Using deep learning, they will build a 3D spatial regression model integrating spaceborne earth observation and geophysical data to predict these properties. The project aims to increase awareness of the global impact of seafloor processes.
Shelf-to-basin sedimentary processes entail intricate interactions between the ocean sub-mesoscale and the near-seafloor sediments. These processes hold significant multidisciplinary importance, given their ecological impact, relevance to marine geo-hazards, and role in shaping continental margins and global climate. However, theres a gap in quantitative tools to model and characterize these processes simultaneously considering oceanographic and near-seafloor sediments properties. In GEM-SBP I will use ultra-high-resolution seismic reflection data (UHRS) to create a set of modelling techniques able to couple both domains. The Southeastern Levant Basin (SLB) will serve as a natural laboratory for studying the interaction between the sedimentological and oceanographic domains. This semi-enclosed basin features a single sediment source and a multitude of morphologically complex features formed by sediment transport mechanisms. I will first develop novel seismic processing workflows tailored for ocean and near-seafloor sediments imaging that will consistently yield a seismic image depicting both domains and their interaction. I will then develop and implement an iterative geostatistical seismic inversion procedure able to invert the processed UHRS for high-resolution ocean (i.e. temperature and salinity) and near-seafloor geotechnical models. I intend to leverage the most recent advances in deep learning to build a spatial regression model and predict these properties in 3-D by integrating spaceborne earth observation data. Our models will provide insights into the sediment transport mechanisms dynamics and will pave the way for a new set of methodologies for studying these phenomena on a global scale. I intend to raise societys awareness about the global impact of oceanographic and seafloor processes, through outreach, communication and dissemination activities aiming to foster a multi-stakeholder environment (i.e. industry, academia and public) within the project. |
