Discontinuity Detection in GNSS Station Coordinate Time Series Using Machine Learning

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.

Supervised Machine Learning of High Rate GNSS Velocities for Earthquake Strong Motion Signals

“…Crocetti et al. (2021) used a random forest classifier for antenna offset detection, including due to earthquake offsets, from low‐rate, 24‐hr position solutions. Habboub et al.…”

“…Several geodetic applications of machine learning algorithms have demonstrated promising results with respect to seismic processes. Crocetti et al (2021) used a random forest classifier for antenna offset detection, including due to earthquake offsets, from low-rate, 24-hr position solutions. Habboub et al (2020) applied a neural network to coordinate time series anomaly detection applicable to specific regional datasets well above the noise floor.…”

Modelling the differences between ultra-rapid and final orbit products of GPS satellites using machine learning approaches

“…The first category of the tested ML methods are the tree-based models, from which many geodetic studies profited due to their reasonable performance and good interpretability (Crocetti et al, 2021;Jing et al, 2020;. In this study, we investigated Decision Tree (DT; Loh, 2011) and Random Forest (RF; Breiman, 2001).…”

Modeling the Differences between Ultra-Rapid and Final Orbit Products of GPS Satellites Using Machine-Learning Approaches

“…The products from January 2019 to April 2021 were used (205,281 valid samples), the sampling rate of which is 15 min. To monitor the training process and evaluate the model independently, the data samples were randomly divided into training, validation, and test sets with proportions of 70% (144,075), 15% (30,873) and 15% (30,873). All the proposed models were trained on the same training samples, and all the results reported were based on the same test set to obtain a fair comparison.…”