Spatial variability characterization of soil properties in offshore wind farms based on Bayesian theory and conditional co-simulation method

The spatial variability characterization of soil properties in offshore wind farms is essential for offshore engineering. The multi-source data fusion can reduce the uncertainty of characterization. However, the existing methods cannot simulate geotechnical properties based on the non-co-located multi-source data, and do not consider the effects of statistical uncertainty. To overcome these challenges, a conditional co-simulation method based on the Bayesian theory is proposed. The Bayesian theory is first used to estimate the cross-variogram model based on the non-co-located multi-source data. Then, the conditional co-simulation is used to generate realizations of spatially varied soil properties, which can characterize the spatial variability with consideration of statistical uncertainty. The proposed method is applied to an offshore wind farm to establish the spatial variability model for the unconfined compression strength (q_u) by integrating data on q_u and standard penetration test (SPT) N value. The results show that the proposed method can characterize the spatial variability of q_u from the non-co-located data on the values of q_u and N, and statistical uncertainty is properly taken into account. In addition, the uncertainties of the variogram models and the conditional co-simulation results can be reduced when the prior distribution with more information and/or SPT data is used.