RESUMO
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.
RESUMO
Using offshore geodetic observations, we show that a segment of the North Anatolian Fault in the central Sea of Marmara is locked and therefore accumulating strain. The strain accumulation along this fault segment was previously extrapolated from onshore observations or inferred from the absence of seismicity, but both methods could not distinguish between fully locked or fully creeping fault behavior. A network of acoustic transponders measured crustal deformation with mm-precision on the seafloor for 2.5 years and did not detect any significant fault displacement. Absence of deformation together with sparse seismicity monitored by ocean bottom seismometers indicates complete fault locking to at least 3 km depth and presumably into the crystalline basement. The slip-deficit of at least 4 m since the last known rupture in 1766 is equivalent to an earthquake of magnitude 7.1 to 7.4 in the Sea of Marmara offshore metropolitan Istanbul.