Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo

被引:0
作者
Piriyasatit, Suchanun [1 ,2 ]
Kuruoglu, Ercan Engin [1 ,2 ]
Ozeren, Mehmet Sinan [3 ]
机构
[1] Tsinghua Univ, Tsinghua Berkeley Shenzhen Inst, Shenzhen 518055, Peoples R China
[2] Tsinghua Shenzhen Int Grad Sch, Inst Data & Informat Sci, Shenzhen 518055, Peoples R China
[3] Istanbul Tech Univ, Eurasia Inst Earth Sci, TR-34469 Istanbul, Turkiye
关键词
sequential Monte Carlo; particle filtering; GPS time-series analysis; spatiotemporal analysis; geodetics; JAPAN; FIELD;
D O I
10.3390/e26040342
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Geodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications, including to determine crustal velocity fields and to detect significant displacement from earthquakes. The relationships inherent in these GPS displacement observations have not been fully explored. This study employs the sequential Monte Carlo method, specifically particle filtering (PF), to develop a time-varying analysis of the relationships among GPS displacement time-series within a network, with the aim of uncovering network dynamics. Additionally, we introduce a proposed graph representation to enhance the understanding of these relationships. Using the 1-Hz GEONET GNSS network data of the Tohoku-Oki Mw9.0 2011 as a demonstration, the results demonstrate successful parameter tracking that clarifies the observations' underlying dynamics. These findings have potential applications in detecting anomalous displacements in the future.
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页数:13
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