论文标题
贝叶斯推断和马尔可夫链蒙特卡洛基于地球科学模型参数的估计
Bayesian inference and Markov chain Monte Carlo based estimation of a geoscience model parameter
论文作者
论文摘要
潜在地震研究中断层摩擦的速率和状态模型的临界滑移距离可能会因巨大的尺度的长度尺度而异，从毫米到几米。这使得构建一个反演框架非常重要，该框架纯粹基于地震图上观察到的加速度提供了良好的临界滑动距离估计。该框架基于贝叶斯推断和马尔可夫链蒙特卡洛。合成数据是通过将速度和状态模型作为正向模型的弹簧击挡仪理想化的加速度输出添加到正向模型的加速度输出来生成的。
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.
