基于贝叶斯推断和马尔可夫链蒙特卡罗的地学模型参数估计
Bayesian inference and Markov chain Monte Carlo based estimation of a geoscience model parameter
摘要：
在潜在地震研究中，断层摩擦速率和状态模型中的临界滑动距离可以根据临界应力断层的长度尺度从微米到几米变化很大。这使得建立一个反演框架变得非常重要，该框架纯粹基于在地震记录上观察到的加速度来提供对临界滑动距离的良好估计。该框架是基于贝叶斯推断和马尔可夫链蒙特卡罗。通过将噪声添加到弹簧-滑块-阻尼器的加速度输出来产生合成数据，将速率和状态模型理想化为正向模型。
英文摘要：
The critical slip distance in rate and state model for fault friction in the study of potential earthquakes can vary wildly from micrometers to few meters depending on the length scale of the critically stressed fault. This makes it incredibly important to construct an inversion framework that provides good estimates of the critical slip distance purely based on the observed acceleration at the seismogram. The framework is based on Bayesian inference and Markov chain Monte Carlo. The synthetic data is generated by adding noise to the acceleration output of spring-slider-damper idealization of the rate and state model as the forward model.
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