Delft University of Technology (TUDelft)
Delft University of technology (TUDelft) is the largest technical university in the Netherlands and usually has a position between 50-70 in the worldwide university rankings. At TUDelft an industry consortium called Delphi has been established since the early 1980’s. It carries out research in the field of geo-imaging applications and is based at the faculty of Applied Sciences and the faculty of Civil Engineering & Geosciences. Most companies are from the geo-energy sector (traditionally the oil&gas related companies), although nowadays a shift towards other geo-imaging applications is established. The main emphasis has been the seismic imaging process, although we extended our activities to other geophysical imaging methods, like electromagnetic imaging, and involve the geologic community in Delft in order to extract meaningful information from these seismic measurements. Furthermore, since the last years, Machine Learning (ML) plays a more prominent role to support and augment the imaging, inversion and interpretation processes. Traditionally, TUDelft has put less emphasis on optimizing the implementation aspects of the developed methodologies, but mainly focused on deriving new algorithms and acquisition methodologies. But because the inversion methodologies become more computational intensive and realistic-scale demonstrations become more important, a collaboration with Cyprus Institute (CyI) has been established for developing and analyzing HPC implementation of the codes.
This resulted into a joint Ph.D. project, where the Ph.D student is based in CyI and co-supervised from TUDelft. Furthermore, TUDelft has been involved as third party to CyI in the RAISE project. The Delphi-developed seismic imaging and inversion approach is brought in as use-case in task 4.2, where both large-scale implementation aspects and ML applications are involved. This task will be carried out at CyI and will involve 19 PMs via a PostDoc position.
WP 4 - Data-Driven Use-Cases towards Exascale (Contributor)
Seismic imaging with remote sensing for energy applications