Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo

TL;DR

This paper uses particle filtering to model GPS displacement networks over time, revealing hidden dynamics in geodetic data.

Contribution

The study introduces a novel time-varying network modeling approach using sequential Monte Carlo and a graph representation for GPS displacement data.

Findings

Particle filtering successfully tracks parameters in GPS displacement time-series.

The graph representation improves understanding of network relationships.

Results show potential for detecting anomalous displacements in geodetic data.

Abstract

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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