Wind Farm
Site Characterization
Subsurface interpretation for offshore and onshore wind farms — foundation design, cable routing, and geohazard identification powered by machine learning.
Shallow Hazard Assessment
Identify boulders, buried channels, gas pockets, and other geohazards that could impact foundations using seismic attributes and AI.
Foundation Site Characterization
Map soil layers, bedrock depth, and sediment properties to inform monopile, jacket, or gravity-based foundation design.
Cable Route Planning
Interpret sub-bottom profiles along proposed cable corridors to identify obstacles and optimize routing.
GPR Interpretation
OpendTect's full suite of visualization and attribute tools supports Ground-Penetrating Radar data for onshore site investigations.
ML-Powered Automation
Train machine learning models to automate classification of soil types, detect anomalies, and predict subsurface conditions (synthetic CPTs) across large survey areas.
3D Visualization
Integrate bathymetry, seismic, well, and geotechnical data in OpendTect's powerful 3D visualization environment.
Successful offshore and onshore wind farm development requires a thorough understanding of the subsurface. Foundation design, cable routing, and long-term structural integrity all depend on accurate characterization of the shallow geology.
OpendTect provides the tools to interpret High-Resolution seismic, Sub-Bottom Profiler, Side Scan Sonar and other measurements for site assessment, geohazard identification, and shallow geological mapping — with machine learning to automate and accelerate the process.
Integrated Ground Models from UHRS, CPTs & Wells
OpendTect is used by service companies and windfarm developers to interpret UHRS data and to build Integrated Ground Models (IGM) from CPTs, wells, and seismic — linking geophysics, geology, and geotechnics into a single cost-optimized foundation design across the windfarm zone.
dGB is currently advising the Dutch government on geophysical aspects in the development of the offshore Door De Wind windfarm project, including:
- —2D and 3D UHRS acquisition, processing & interpretation
- —Quantitative Interpretation and IGM model building
Neural Network Predicted Synthetic CPTs
A cross-validated ANN regression trained on 2D seismic, acoustic impedance inversion and CPT parameters predicts synthetic CPTs along seismic lines, then interpolates them across the full 3D volume using stratigraphic 3D Kriging — with confidence intervals calibrated against decimated seismic volume predictions.
- —GA acoustic impedance inversion & probability distributions (AI, Vp, Qp)
- —Cross-validated ANN training with grid search for best hyper-parameters
- —2D & 3D synthetic CPTs with quantified uncertainty