Abstract The difference in dynamic mechanical properties of different rock samples leads to a random process of deformation and stress. Uncertainties exist in the parameters of the rock material dynamic constitutive model. Unlike traditional inversion analysis methods, this paper treats model parameters as random variables. Bayesian theory and Differential Evolution Adaptive Metropolis algorithm (DREAM) are used to accurately quantify the uncertainty of the dynamic constitutive model parameters. The error function of the DREAM algorithm is divided into prediction error and model error to mitigate the effect of parameter compensation and improve the prediction ability. Peak stress error term is added to the DREAM prediction error to increase the peak stress fitting accuracy. Experiments of shock compression under freezing and high temperature cooling damage are presented to illustrate the proposed method. Different optimization algorithms such as Genetic Algorithm and Ant Colony Optimization are used to compare the accuracy of inversion parameters. The results show that the obtained peak stress fitting accuracy of the dynamic constitutive model is significantly improved. The 95% confidence interval considering model errors almost completely covers the experimental observations, indicating that the parameter probability distribution interval can accurately cover the true value while reflecting the model uncertainty. Using inversion parameters to predict other working conditions, 95% confidence interval considering model errors has high coverage. Thus, stochastic inversion method can accurately fit and predict the deformation and failure law of rock under impact load.

中文翻译：

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基于改进DREAM的岩石材料动态本构模型随机反演方法

摘要 不同岩样动态力学性质的差异导致变形和应力的随机过程。岩石材料动力本构模型参数存在不确定性。与传统的反演分析方法不同，本文将模型参数视为随机变量。贝叶斯理论和差分进化自适应大都会算法（DREAM）用于准确量化动态本构模型参数的不确定性。DREAM算法的误差函数分为预测误差和模型误差，以减轻参数补偿的影响，提高预测能力。峰值应力误差项被添加到 DREAM 预测误差中，以提高峰值应力拟合精度。提出了冷冻和高温冷却损伤下的冲击压缩实验来说明所提出的方法。使用遗传算法和蚁群优化等不同的优化算法来比较反演参数的准确性。结果表明，得到的动态本构模型峰值应力拟合精度显着提高。考虑模型误差的95%置信区间几乎完全覆盖了实验观测值，说明参数概率分布区间在反映模型不确定性的同时能够准确覆盖真实值。使用反演参数预测其他工况，考虑模型误差的95%置信区间覆盖率高。因此，