基于贝叶斯定理的Gibbs随机采样方法，结合现有异重流水卷吸实验数据，探讨了异重流水卷吸经验式中经验系数E1和E2的不确定性，获取的E1和E2样本总数为2×105.统计发现，其中概率最大的(E1,E2)样本取值与原式取值相近.将样本值输入异重流层平均水沙耦合数学模型，模拟陡坡上异重流演化过程.结果表明：计算异重流厚度、速度、泥沙体积分数、底床形变区间范围随运动距离的增加逐渐增大；采用概率最大经验系数值可能低估异重流厚度和泥沙体积分数，高估异重流速度和底床形变；95%样本输入异重流模型所得计算水力参数范围远大于25%样本所得计算水力参数范围，意味着模型对(E1,E2)的取值十分敏感.
Combining with the existing experimental data of water entrainment for turbidity currents, uncertainties of empirical coefficients, E1 and E2, in empirical formula were investigated using the method of Gibbs sampling based on Bayesian theory. A total of 2×105 sample values for E1 and E2 were obtained. Through statistics, the maximum probability (E1,E2) values are similar to the original formula. By inputting these sample values of (E1,E2) into a fully coupled mathematical model, the evolution of the turbidity currents over a steep slope is simulated. The results indicates that the range of the computed current thickness, velocity, sediment’s volume fraction and bottom deformation increases with distance. If the sample values of (E1,E2) with the highest frequency were used, the model may underestimate the current thickness and sediment volume fraction, and over-estimate the current velocity and bottom deformation. The range of hydraulic parameters calculated with 95% (E1,E2) sample values inputted into turbidity current models is much larger than that with 25% (E1,E2) sample values, which indicates that the model is very sensitive to the value of (E1,E2).
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