EUDP-funded research project developing UHRS quantitative interpretation methods for integrated geophysical and geotechnical characterisation of offshore wind sites. Partners include GEUS, Qeye, and SolidGround.
WINDFARM is a three-year research project bringing together GEUS, Qeye, and SolidGround. Funded by Denmark's Energy Technology Development and Demonstration Programme (EUDP), the project launched January 2024 and runs through 2026.
The work develops cutting-edge quantitative interpretation methods for ultra-high-resolution seismic (UHRS) data, with the goal of delivering geotechnical insights at both large-scale screening and site-specific construction stages of offshore wind development. Key technical aspects include geostatistics, refinement of geological and geotechnical models, and both deterministic and probabilistic UHRS inversion.
For SolidGround, WINDFARM has deepened our soil-physics understanding and advanced our geodatabase solution. The project's industry advisory board — DTU, Ørsted, TotalEnergies, RWE, Skyborn, and DNV — ensures the research is shaped by, and feeds back into, operational offshore wind practice.
- Duration
- 3 years (Jan 2024 – Dec 2026)
- Methods
- Geostatistics · Deterministic UHRS inversion · Probabilistic UHRS inversion
- Deliverables
- Geotechnical insights at large-scale screening and site-specific construction
- UHRS inversion techniques at different stages of offshore wind farm developmentDalgaard, E. & Ross, R. · 3rd EAGE/SUT Workshop on Integrated Site Characterisation for Offshore Renewable Energy, Melbourne, Australia (2025)
- UHRS inversion across the development timeline of offshore wind farmsDalgaard, E. · Sixth EAGE Global Energy Transition Conference & Exhibition (GET 2025), Rotterdam, Netherlands (2025)
- UHRS pre-stack inversion — challenges and possibilitiesDalgaard, E. · 86th EAGE Annual Conference & Exhibition, Toulouse, France (2025)
- Limitations to geotechnical datasets for machine learning: the case of P-S loggingStuyts, B. & Dalgaard, E. · 3rd Workshop on the Future of Machine Learning in Geotechnics (3FOMLIG), Florence, Italy. Paper PS01-ID22 (2025)