PhD Candidate DeepNL project: Numerical modelling and geophysical inversion for characterization of the Dutch subsurface

The global initiative to complete the energy transition by 2040 puts tremendous pressure on governments and industries. The need for any nation to know the potential of its subsurface to meet the future demand for energy and raw materials, as well as its storage capacity CO2, H2) and associated hazards induced seismicity, subsidence), is more critical than ever for informing exploration efforts, strategic economic planning, environmental policies, and decision-makers. The Netherlands has invested a significant part of its GDP in data acquisition programs in the past 30 years. However, the formal integration and inversion of all available and complementary data seismic, satellite, borehole, gravity, magnetic, etc.) have not been yet fully exploited for obtaining unifying, multi-parameter and internally consistent models of the subsurface at scales relevant for informing regional and nation-wide exploration efforts and strategic environmental planning. This project will develop novel ways of characterizing the physical state of the subsurface beneath the Netherlands via the formal integration of multiple geophysical, geochemical/petrological and petrophysical data into a single physics-based framework. Specifically, you will adopt and expand a simulation-based, multi-observable probabilistic platform Afonso et al., 2016; 2022) capable of inverting all available datasets simultaneously. Advanced numerical FE methods, reduced order methods) and inversion AI-based, McMC-driven) techniques will be applied and expanded to create innovative multi-observable inversion platforms for subsurface characterization. You will collaborate closely with researchers at Delft University of Technology, Utrecht University, and the Polytechnic University of Catalonia. You will be a member of a large and dynamic geophysics/computational geodynamics team of PhD students and post-doctoral fellows.