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| Titel |
A Bayesian approach to offset detection in GPS coordinate time series |
| VerfasserIn |
Julien Gazeaux, Matt King, Simon Williams |
| Konferenz |
EGU General Assembly 2013
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 15 (2013) |
| Datensatznummer |
250074542
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| Zusammenfassung |
| The accuracy of Global Positioning System (GPS) time series is degraded by the presence of
offsets. Offsets may be caused by different phenomena (antenna swap, seismic event, ...) but
some of them can not be explained and remain unpredictable. DOGEx (Detection of Offsets
in GPS Experiment) results show that, at present, manual detection methods (where offsets
are hand picked) almost always give better velocity estimates than automated or
semi-automated methods. For instance, the 5th percentile range (5% to 95%) in velocity bias
for automated approaches is equal to 4.2mm/year (most commonly ±0.4mm/yr from the
truth), whereas it is equal to 1.8mm/yr for the manual solutions (most commonly ±0.2mm/yr
from the truth). Moreover, the magnitude of offsets detectable by manual solutions is smaller
than for automated solutions, with the smallest detectable offset for the two best
manual and automatic solutions equal to 5mm and 7mm and to 8mm and 10mm,
respectively.
Assuming the time series noise levels simulated for DOGEx are representative of real
GPS time series, robust geophysical interpretation of individual site velocities lower than
0.2-0.4mm/yr is therefore certainly not robust although a limit of nearer 1mm/yr would be a
more conservative choice.
In this work, the results of DOGEx are briefly discussed in order to motivate the
introduction of an automatic detection procedure to estimate offset epochs and
velocities from GPS time series. The detection algorithm based on a Bayesian approach
handles the overall known properties of GPS time series, such as flicker noise,
velocities, periodic signals and randomly occurring offsets. It not only provides
an objective estimation of GPS velocities, but at the same time it allows for the
consideration of a-priori information such as epochs of events which could cause
offsets, or a-priori knowledge on the velocity of each sites. The flexibility, robustness
and limitations of the approach are discussed by applying the method to simulated
time series and to the Monte-Carlo method to test the accuracy of the procedure. |
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