海上风电场土体参数的空间变异性表征对海上风电工程具有重要意义，多源土体参数融合可降低表征结果的不确定性。然而，现有方法无法利用非同位多源土体参数数据，且不考虑统计不确定性对空间变异性表征的影响。为此，提出了基于贝叶斯理论的条件协同模拟方法，该方法根据非同位多源土体参数数据，利用贝叶斯理论估计交叉变异函数，再利用条件协同模拟方法生成大量土体参数空间分布的模拟样本，表征参数空间变异性，表征过程中合理地考虑模型参数的统计不确定性。以某海上风电场为工程背景，利用提出的方法融合无侧限抗压强度(q_u)和标准贯入试验(SPT)击数 N 值，表征 q_u 的空间变异性。结果表明：提出的方法可以根据有限非同位的 q_u 和 N 值数据，表征 q_u 的空间变异性，合理地反映了有限数据条件下统计不确定性的影响。此外，利用强信息先验分布或者融合标准贯入数据，可以降低变异函数模型参数统计不确定性和条件协同模拟结果的不确定性。

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.