Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach
Inversion of Surface Deformation Data for Rapid Estimates of Source Parameters and Uncertainties: A Bayesian Approach
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DOI：
10.1029/2018gc007585
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发表时间：
2018-07-01
影响因子：
3.5
通讯作者：
Hooper, Andrew
中科院分区：
文献类型：
--
作者：
Bagnardi, Marco;Hooper, Andrew
新的卫星飞行任务（例如，欧洲航天局的Sentinel-1星座）、数据下行链路的进步以及快速产品生成现在使我们能够在获取后数小时内访问空间大地测量数据。为了真正利用这一机会，我们需要能够以迅速和可靠的方式解释大地测量数据。在这里，我们提出了一个贝叶斯方法反演的多个大地测量数据集，允许源模型参数的后验概率密度函数（PDF）的快速表征。反演算法有效地采样后PDF通过马尔可夫链蒙特卡罗方法，结合大都会黑斯廷斯算法，自动步长选择。我们将我们的方法应用于模拟岩浆起源变形的综合大地测量数据，并证明了它能够检索已知的源参数。我们还将反演算法应用于测量逆冲断层地震（2015年M-w 6.4皮山地震，中国）同震位移的干涉合成孔径雷达数据，并检索最佳震源参数和相关的不确定性。由于其鲁棒性和快速估计变形源参数和不确定性，我们的贝叶斯框架是能够利用实时大地测量。因此，我们的方法可以应用于大地测量数据，以研究岩浆，构造和其他地球物理过程，特别是在快速响应的操作设置（例如，火山观测站）。我们的算法完全在基于MATLAB（R）的软件包（大地贝叶斯反演软件）中实现，我们免费向科学界提供。
New satellite missions (e.g., the European Space Agency's Sentinel-1 constellation), advances in data downlinking, and rapid product generation now provide us with the ability to access space-geodetic data within hours of their acquisition. To truly take advantage of this opportunity, we need to be able to interpret geodetic data in a prompt and robust manner. Here we present a Bayesian approach for the inversion of multiple geodetic data sets that allows a rapid characterization of posterior probability density functions (PDFs) of source model parameters. The inversion algorithm efficiently samples posterior PDFs through a Markov chain Monte Carlo method, incorporating the Metropolis-Hastings algorithm, with automatic step size selection. We apply our approach to synthetic geodetic data simulating deformation of magmatic origin and demonstrate its ability to retrieve known source parameters. We also apply the inversion algorithm to interferometric synthetic aperture radar data measuring co-seismic displacements for a thrust-faulting earthquake (2015 M-w 6.4 Pishan earthquake, China) and retrieve optimal source parameters and associated uncertainties. Given its robustness and rapidity in estimating deformation source parameters and uncertainties, our Bayesian framework is capable of taking advantage of real-time geodetic measurements. Thus, our approach can be applied to geodetic data to study magmatic, tectonic, and other geophysical processes, especially in rapid-response operational settings (e.g., volcano observatories). Our algorithm is fully implemented in a MATLAB (R)-based software package (Geodetic Bayesian Inversion Software) that we make freely available to the scientific community.