Assessing paleo channel probability for offshore wind farm ground modeling - comparison of multiple-point statistics and sequential indicator simulation
The presented study investigates the prediction of buried paleo-channels for probabilistic ground modeling of offshore windfarm development areas using geostatistical methods. These channels, common in glaciogenic regions like the North Sea, can pose significant geohazards affecting turbine foundati...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Applied Computing and Geosciences |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259019742500062X |
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| Summary: | The presented study investigates the prediction of buried paleo-channels for probabilistic ground modeling of offshore windfarm development areas using geostatistical methods. These channels, common in glaciogenic regions like the North Sea, can pose significant geohazards affecting turbine foundation stability. Conventional 2D seismic data interpretation provides the best estimate of the position but lacks probabilistic assessment, specifically at unexplored locations. Multiple-point statistics (MPS) and sequential indicator simulation (SIS) are applied to quantify the probability of channel features from seismic data, away from seismic lines. MPS utilizes training images to capture complex spatial structures, while SIS relies on variogram models for modeling spatial variability. Results demonstrate that denser seismic line spacing (150 m) yields higher accuracy compared to wider spacings (300 m and 600 m), underscoring the importance of data density in offshore subsurface site characterization. Additionally, the findings indicate that MPS provides lower errors, making it preferable for precise channel location prediction. The selected training image did not have a major impact on the outcome on the tested data. Conversely, SIS offers broader coverage of potential channel locations, which may be advantageous for further de-risking. This research contributes to more informed ground modeling by incorporating probabilistic approaches. Therefore, it supports in offshore wind farm site development by enhancing knowledge of the subsurface at an early stage of wind farm development to aid decisions in windfarm and further site investigation planning. |
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| ISSN: | 2590-1974 |