Rapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 10⁶ samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

中大型地震同震断层模型的快速估计是灾害响应的关键。为了估计大地震的同震断层模型，日本地理空间信息局和东北大学联合开发了用于快速变形监测的实时 GEONET 分析系统（REGARD）。REGARD 可以估计单个矩形断层模型和沿假定板块界面的滑动分布。单个矩形断层模型可用作中到大地震的一阶近似。然而，在其估计中，由于初始值的强烈影响，很难得到模型参数的准确结果。为了解决这个问题，本研究基于使用马尔可夫链蒙特卡罗 (MCMC) 方法的贝叶斯反演方法，提出了一种实时估计同震断层模型和模型不确定性的新方法。MCMC 方法在计算上很昂贵，并且应该通过反复试验提前定义超参数。使用并行回火方法提高了采样效率，并开发了超参数的自动定义方法以供实时使用。1 × 10 计算时间在 30 s 以内 并开发了一种用于实时使用的超参数自动定义方法。1 × 10 计算时间在 30 s 以内 并开发了一种用于实时使用的超参数自动定义方法。1 × 10 计算时间在 30 s 以内⁶示例使用典型的单个LINUX服务器，可以实现实时分析，类似于REGARD。使用近期地震（2016 年熊本地震和 2019 年山形冲地震）的数据评估了所开发方法的可靠性。日本海地震的模拟也进行了详尽的模拟。结果表明，与具有先验信息的最大似然方法相比具有优势，后者在非线性问题中具有初始值依赖性。在应用于具有小信噪比的数据方面，结果表明使用几种共轭故障模型的可能性。断层面积和滑移量之间存在权衡，特别是对于海上地震，