Geodetic inversion for space-time distribution of fault slip with time-varying smoothing regularization
Geodetic inversion for space-time distribution of fault slip with time-varying smoothing regularization
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DOI：
10.1111/j.1365-246x.2007.03722.x
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发表时间：
2008-04-01
影响因子：
2.8
通讯作者：
Kato, Teruyuki
中科院分区：
文献类型：
--
作者：
Fukuda, Jun'ichi;Miyazaki, Shin'ichi;Kato, Teruyuki
本文提出了一种基于时变平滑正则化的断层滑动速度时空分布大地测量反演方法，以重建地震断层滑动瞬变的精确时间历程。我们通过贝叶斯状态空间方法引入了滑动和滑动速度的时间平滑正则化，其中正则化的强度（滑动速度的时间平滑度）由超参数控制。将超参数作为时变随机变量，采用分层贝叶斯状态空间模型，在传统贝叶斯状态空间模型的基础上引入超参数的先验分布，实现了时变平滑正则化.我们已经测试了这种反演方法对两个合成数据集所产生的模拟地震滑动瞬变。结果表明，我们的方法再现了快速变化的滑移速度和稳态速度没有显着的过度平滑和欠平滑，这是很难克服的传统贝叶斯方法与时间无关的平滑正则化。2002年日本房总半岛外静震引起的瞬时形变的应用也显示了这种方法比传统方法的类似优点。
We have developed a new geodetic inversion method for space-time distribution of fault slip velocity with time-varying smoothing regularization in order to reconstruct accurate time histories of aseismic fault slip transients. We introduce a temporal smoothing regularization on slip and slip velocity through a Bayesian state space approach in which the strength of regularization (temporal smoothness of slip velocity) is controlled by a hyperparameter. The time-varying smoothing regularization is realized by treating the hyperparameter as a time-dependent stochastic variable and adopting a hierarchical Bayesian state space model, in which a prior distribution on the hyperparameter is introduced in addition to a conventional Bayesian state space model. We have tested this inversion method on two synthetic data sets generated by simulated aseismic slip transients. Results show that our method reproduces well both rapid changes of slip velocity and steady-state velocity without significant oversmoothing and undersmoothing, which has been hard to overcome by the conventional Bayesian approach with time-independent smoothing regularization. Application of this method to transient deformation in 2002 caused by a silent earthquake off the Boso peninsula, Japan, also shows similar advantages of this method over the conventional approach.