Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning
Open access
Date
2021-09-29Type
- Journal Article
Abstract
Global navigation satellite systems (GNSS) provide globally distributed station coordinate
time series that can be used for a variety of applications such as the definition of a terrestrial reference frame. A reliable estimation of the coordinate time series trends gives valuable information about station movements during the measured time period. Detecting discontinuities of various origins
in such time series is crucial for accurate and robust velocity estimation. At present, there is no fully automated standard method for detecting discontinuities. Instead, discontinuity-catalogues are frequently used, which provide information about when a device was changed or an earthquake occurred. However, it is known that these catalogues suffer from incompleteness. This study investigates the suitability of machine learning classification algorithms that are fully data-driven
to detect discontinuities caused by earthquakes in station coordinate time series without the need
for external information. For this study, Japan was selected as a testing area. Ten different machine
learning algorithms have been tested. It is found that Random Forest achieves the best performance
with an F1 score of 0.77, a recall of 0.78, and a precision of 0.76. Overall, 525 of 565 recorded
earthquakes in the test data were correctly classified. It is further highlighted that splitting the time
series into chunks of 21 days leads to the best performance. Furthermore, it is beneficial to combine
the three (normalized) components of the GNSS solution into one sample, and that adding the value
range as an additional feature improves the result. Thus, this work demonstrates how it is possible
to use machine learning algorithms to detect discontinuities in GNSS time series. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000509293Publication status
publishedExternal links
Journal / series
Remote SensingVolume
Pages / Article No.
Publisher
MDPISubject
GNSS; Discontinuities; Earthquakes; Machine learningOrganisational unit
09707 - Soja, Benedikt / Soja, Benedikt
More
Show all metadata