气候变化领域集成服务门户(CCPortal): Variational Bayesian Independent Component Analysis for InSAR Displacement Time-Series With Application to Central California, USA

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DOI	10.1029/2020JB020845
	Variational Bayesian Independent Component Analysis for InSAR Displacement Time-Series With Application to Central California, USA
	Gualandi A.; Liu Z.
发表日期	2021
ISSN	21699313
卷号	126 期号:4
英文摘要	The exploitation of ever increasing Interferometric Synthetic Aperture Radar (InSAR) data sets to monitor the Earth's surface deformation is an important goal of today's geodesy. In this study our observations consist of deformations along the Line-Of-Sight direction of the satellite. Our observations are the result of the combination of a multitude of sources (either volcano-tectonic or nontectonic deformation). In most cases, we are facing a Blind source separation (BSS) problem. Natural approaches to tackle BSS problems are multivariate statistical techniques that attempt to decompose the data set into a limited number of statistically independent sources, under the assumption that the different physical mechanisms contributing to the observations have independent footprints in space and/or time. We show the capabilities of a variational Bayesian independent component analysis (vbICA) algorithm in dealing with synthetic InSAR time series and compare it to the commonly used FastICA algorithm. We explore the effectiveness of the spatial and temporal mode decompositions. We apply vbICA on data relative to the San Joaquin Valley and the Central San Andreas fault (CSAF), California, spanning the time range 2015/03/01–2019/07/14. The proposed approach likely isolates the contribution of shallow and deep aquifers to the surface deformation as well as the elastic and inelastic deformation. We present a 1-dimensional compaction estimation of the elastic and inelastic storage coefficients adopting a formalism that takes into account the last century water level history. Concerning the CSAF, the algorithm helps separating tectonic loading from seasonal behavior concentrated in the Quaternary sediments of the Salinas Valley. © 2021. Jet Propulsion Laboratory. California Institute of Technology. Government sponsorship acknowledged.
英文关键词	Central San Andreas fault; elastic and inelastic compaction; InSAR; multivariate time series analysis; San Joaquin Valley; variational Bayesian independent component analysis
语种	英语
来源期刊	Journal of Geophysical Research: Solid Earth
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/187216
作者单位	National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
推荐引用方式 GB/T 7714	Gualandi A.,Liu Z.. Variational Bayesian Independent Component Analysis for InSAR Displacement Time-Series With Application to Central California, USA[J],2021,126(4).
APA	Gualandi A.,&Liu Z..(2021).Variational Bayesian Independent Component Analysis for InSAR Displacement Time-Series With Application to Central California, USA.Journal of Geophysical Research: Solid Earth,126(4).
MLA	Gualandi A.,et al."Variational Bayesian Independent Component Analysis for InSAR Displacement Time-Series With Application to Central California, USA".Journal of Geophysical Research: Solid Earth 126.4(2021).