Data‐adaptive detection of transient deformation in geodetic networks. Issue 3 (21st March 2016)

• Record Type: Journal Article
• Authors: Walwer, Damian; Calais, Eric; Ghil, Michael
• Is Part Of: Journal of geophysical research. Volume 121:Issue 3(2016:Mar.)
• Page Start: 2129
• Page End: 2152
• Publication Date: 2016-03-21
• Subjects: GPS time series -- transient deformation -- seasonal oscillations -- Multichannel Singular Spectrum Analysis -- data‐adaptive analysis of GPS time series; Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1002/2015JB012424
• Languages: English
• ISSNs: 2169-9313

Abstract

Abstract: The recent development of dense and continuously operating Global Navigation Satellite System (GNSS) networks worldwide has led to a significant increase in geodetic data sets that sometimes capture transient‐deformation signals. It is challenging, however, to extract such transients of geophysical origin from the background noise inherent to GNSS time series and, even more so, to separate them from other signals, such as seasonal redistributions of geophysical fluid mass loads. In addition, because of the very large number of continuously recording GNSS stations now available, it has become impossible to systematically inspect each time series and visually compare them at all neighboring sites. Here we show that Multichannel Singular Spectrum Analysis (M‐SSA), a method derived from the analysis of dynamical systems, can be used to extract transient deformations, seasonal oscillations, and background noise present in GNSS time series. M‐SSA is a multivariate, nonparametric, statistical method that simultaneously exploits the spatial and temporal correlations of geophysical fields. The method allows for the extraction of common modes of variability, such as trends with nonconstant slopes and oscillations shared across time series, without a priori hypotheses about their spatiotemporal structure or their noise characteristics. We illustrate this method using synthetic examples and show applications to actual GPS data from Alaska to detect seasonal signals and microdeformation at the Akutan active volcano. The geophysically coherent spatiotemporal patterns of uplift and subsidence thus detected are compared to the results of an idealized model of such processes in the presence of a magma chamber source. Key Points: Data‐adaptive analysis of GPS time series to detect transient deformation and seasonal oscillations Use of the M‐SSA to analyze GPS time series without using any a priori hypotheses Detection of two cycles of uplift and subsidence at Akutan volcano likely to be of magmatic origin