In the "Seafloor Modeling" group, we use computer simulations to investigate the formation and evolution of the ocean floor, its role in the earth system, and the responsible use of energetic and mineral resources on the seabed. We follow an interdisciplinary approach that links modern modeling techniques with different observations and data sets. We follow an open source approach and live the concept of "open science". We are also increasingly integrating machine learning and AI into our research.
Our focus is:
We combine models with data to study the geodynamic evolution of the ocean floor. For this purpose, we link findings from computer simulations with different geological, geochemical and geophysical data sets. Examples of our work are studies on the interactions between hydrothermalism and crust formation at mid-ocean ridges, the tectonic and petrological evolution of transform faults, and the formation of passive continental margins.
We investigate magmatic hydrothermal systems and their role in the earth system. For this we use various in-house computer models, which are based on the complex equation of state of sea water and can also resolve multi-phase phenomena. From these model results, we gain knowledge about mass and energy fluxes, which allow us to make quantitative estimates of the formation of seafloor ore deposits and the role of hydrothermal discharge fluxes in global biogeochemical cycles. We are also increasingly relying on fluid dynamic simulations on the pore scale to better understand fluid-rockinteractions at high and lowtemperature.
The migration of fluids and gases in marine sediments is another important building block for a better understanding of the connections between the solid earth and the global ocean in the earth system. Our work focuses on the synthesis of models and data to estimate the global and regional distribution of gas hydrate and associated biogeochemical turnover rates. Another focus is the deep biosphere and so-called cold seeps on the sea floor. We are also increasingly using machine learning and AI approaches to estimate global seabed accumulation and turnover rates.