This work presents a framework to inversely quantify uncertainty in the model parameters of the friction model using earthquake data via the Bayesian inference. The forward model is the popular rate- and state- friction (RSF) model along with the spring slider damper idealization. The inverse model is to determine the model parameters using the earthquake data as the response of the RSF model. The conventional solution to the inverse problem is the deterministic parameter values, which may not represent the true value, and quantifying uncertainty in the model parameters increases confidence in the estimation. The uncertainty in the model parameters is estimated by the posterior distribution obtained through the Bayesian inversion.

翻译：这项工作提供了一个框架,用贝叶斯推论的地震数据对摩擦模型参数的不确定性进行反向量化。前方模型是流行速率和州摩擦模型,以及弹簧滑轮大坝理想化。反向模型是用地震数据确定模型参数,作为RSF模型的响应。反向模型是反向问题的常规解决办法是确定参数值,它可能不代表真实值,而模型参数不确定性的量化增加了对估计的信心。模型参数的不确定性通过巴伊西亚的反向分析获得的后方分布估计。