断层滑动反演的不确定性量化
Uncertainty quantification for fault slip inversion
AI总结：
本文提出基于最优方向吉布斯的贝叶斯方法，从大地测量数据中高效反演慢滑移事件的断层滑移并量化不确定性，成功应用于2006年墨西哥格雷罗事件并估计其矩震级。
摘要：
我们提出一种高效的贝叶斯方法，用于从慢滑移事件中的大地测量数据推断断层位移。我们的滑移过程物理模型可简化为受约束的多元线性回归。假设大地测量数据服从高斯模型，并考虑未知断层滑移服从多元截断正态先验分布，所得后验分布也是多元截断正态分布。针对该后验分布，我们提出一种基于最优方向吉布斯（Optimal Directional Gibbs）的算法，使我们能够高效地从断层网格划分中沿倾向和沿走向运动的高维后验分布中采样。一个合成断层滑移示例展示了所提方法的灵活性和准确性。该方法还被应用于一个真实数据集，即2006年墨西哥格雷罗慢滑移事件，其目标是在已知界面上恢复断层滑移，该滑移产生了地面大地测量站观测到的位移。作为我们方法的一个副产品，我们能够对2006年格雷罗事件的矩震级进行带不确定性量化的估计。
英文摘要：
We propose an efficient Bayesian approach to infer a fault displacement from geodetic data in a slow slip event. Our physical model of the slip process reduces to a multiple linear regression subject to constraints. Assuming a Gaussian model for the geodetic data and considering a multivariate truncated normal prior distribution for the unknown fault slip, the resulting posterior distribution is also multivariate truncated normal. Regarding the posterior, we propose an algorithm based on Optimal Directional Gibbs that allows us to efficiently sample from the resulting high-dimensional posterior distribution of along dip and along strike movements of our fault grid division. A synthetic fault slip example illustrates the flexibility and accuracy of the proposed approach. The methodology is also applied to a real data set, for the 2006 Guerrero, Mexico, Slow Slip Event, where the objective is to recover the fault slip on a known interface that produces displacements observed at ground geodetic stations. As a by-product of our approach, we are able to estimate moment magnitude for the 2006 Guerrero Event with uncertainty quantification.
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