Skip to main content

Research Repository

Advanced Search

A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies

Habboub, Mohammed; Psimoulis, Panos A.; Bingley, Richard; Rothacher, Markus

A Multiple Algorithm Approach to the Analysis of GNSS Coordinate Time Series for Detecting Geohazards and Anomalies Thumbnail


Authors

Mohammed Habboub

RICHARD BINGLEY RICHARD.BINGLEY@NOTTINGHAM.AC.UK
Professor of Geodetic Surveying

Markus Rothacher



Abstract

©2020. American Geophysical Union. All Rights Reserved. In this study, a multiple algorithm approach to the analysis of GNSS coordinate time series for detecting geohazards and anomalies is proposed. This multiple algorithm approach includes the novel use of spatial and temporal analyses. In the spatial analysis algorithm, the spatial autoregressive model was used, assuming that the GNSS coordinate time series from a network of stations are spatially dependent. Whereas in the temporal analysis algorithm, it is assumed that the GNSS coordinate time series of a single station is temporally dependent and an artificial neural network is used to extract this dependency as a nonparametric model. This multiple algorithm approach was examined using (i) the BIGF network of GNSS stations in the British Isles and (ii) the GNSS stations of the GEONET network in Japan for the Tohoku-Oki 2011 Mw9.0 earthquake. It was demonstrated in these case studies that this multiple algorithm approach can be used to detect the effect of a geohazard such as an earthquake on the GNSS network coordinate time series and to detect regional anomalies in the GNSS coordinate time series of a network. The spatial analysis algorithm seemed to be more suitable to detect coordinate offsets in the low-frequency component and/or variations in the long-term trends of the GNSS coordinate time series, while it is less reliable in detecting sudden large magnitude coordinate offsets due to earthquakes, as the effects at one station propagate to nearby stations. In contrast, the temporal analysis algorithm detects coordinate offsets in the high-frequency component which makes it effective in detecting sudden large coordinate offsets in the GNSS coordinate time series such as those due to earthquakes. Thus, it was shown the complementary of the temporal and spatial analysis algorithms and their successful application for the magnitude and frequency content of the anomalies in the two case studies.

Journal Article Type Article
Acceptance Date Jan 15, 2020
Online Publication Date Feb 11, 2020
Publication Date Feb 11, 2020
Deposit Date Mar 10, 2020
Publicly Available Date Aug 12, 2020
Journal Journal of Geophysical Research: Solid Earth
Electronic ISSN 2169-9356
Publisher American Geophysical Union
Peer Reviewed Peer Reviewed
Volume 125
Issue 2
Article Number e2019JB018104
DOI https://doi.org/10.1029/2019JB018104
Public URL https://nottingham-repository.worktribe.com/output/3794324
Publisher URL https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019JB018104
Additional Information ©2020 American Geophysical Union. All Rights Reserved.

Files





You might also like



Downloadable Citations