Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches

Gao, Wenzong; Li, Zhao; Chen, Qusen; Jiang, Weiping; Feng, Yanming

doi:10.1007/s00190-022-01662-5

Cited by 25 publications

(12 citation statements)

References 56 publications

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“…This problem is non‐trivial. It may be possible to separate the various spatial‐temporal processes using principal or independent component analysis, or other data separation approaches such as those based on machine learning (Dong et al., 2006; Gao et al., 2022; Liu et al., 2018; Mandler et al., 2021). A viscoelastic model that includes both earthquake‐related slip and a changing ice load would also be a step forward in modeling Antarctic deformation in a consistent way.…”

Section: Discussionmentioning

confidence: 99%

Postseismic Deformation in the Northern Antarctic Peninsula Following the 2003 and 2013 Scotia Sea Earthquakes

Nield,

King,

Koulali

et al. 2023

JGR Solid Earth

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Large earthquakes in the vicinity of Antarctica have the potential to cause postseismic viscoelastic deformation affecting measurements of displacement that are used to constrain models of glacial isostatic adjustment (GIA). In November 2013, a Mw 7.7 strike‐slip earthquake occurred in the Scotia Sea, 650 km from the Antarctic Peninsula. GPS time series from the northern Peninsula show a change in rate after this event, indicating a far‐field postseismic deformation signal is present. In this study, we use a finite element model with a suite of 1D and 3D Earth structures to investigate the extent of postseismic deformation in the Antarctic Peninsula. Model output is compared with GPS time series to place constraints on the Earth structure in this region. The preferred Earth structure has a thin lithosphere combined with a Burgers rheology with steady‐state viscosity of 4 × 1018 Pa s and transient viscosity one order of magnitude lower. Our study shows that including 3D Earth structure does not improve the fit. Using the best fitting Earth structure, we run a forward model of the nearby 2003 Mw 7.6 strike‐slip earthquake and combine the predictions for both earthquakes. We show that postseismic deformation is widespread across the northern Peninsula with rates of horizontal deformation up to 1.65 mm/yr for the period 2015–2020, a signal that persists for decades. These results suggest that much of Antarctica may be deforming due to recent postseismic deformation and this signal needs to be accounted for when using GPS observations to constrain geophysical models.

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Section: Discussionmentioning

confidence: 99%

Postseismic Deformation in the Northern Antarctic Peninsula Following the 2003 and 2013 Scotia Sea Earthquakes

Nield,

King,

Koulali

et al. 2023

JGR Solid Earth

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“…LSTM networks are extensions of recurrent neural networks (RNNs), not only able to learn temporal correlations in series prediction problems, but also able to memorize information for a long time. In recent studies, the LSTM has achieved good results in nonlinear time series prediction, such as voltage prediction [22], pressure prediction [23], and GNSS time series prediction [24]. The residual phase fluctuations of the system cannot be obtained in advance, but its changing trend is related to temperature variations and the uplink phase fluctuations, all three of which are typical nonlinear time series.…”

Section: Introduction Large Number Of Advanced Scientific and Industr...mentioning

confidence: 99%

A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System

Cheng,

Wang,

Qiao

et al. 2024

IEEE Photonics J.

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A convolutional neural network combined with long short-term memory (CNN-LSTM) phase compensation method (PCM) is proposed and demonstrated, where CNN is employed to extract spatial features, and LSTM is used to capture temporal features and realize the long-term predictions of residual phase fluctuations. This is the first-time machine learning (ML) has been used to mitigate the effects of optical path asymmetry caused by temperature variations on radio frequency (RF) transmission systems. The performance is verified by experiments on a unidirectional two-way RF transmission system, in which both the two 259-km-long separate fibers are coupled into one optical cable. The results demonstrate the CNN-LSTM model presents better prediction performance than the other eight previously proposed ML models. When the prediction duration is 40,000 s and the ambient temperature variation range is 14.38°C, the coefficient of determination (R Squared, R 2 ) between the predicted value and the actual value is higher than 0.99. In addition, compared to the phase locked loop (PLL) PCM, the proposed CNN-LSTM PCM can reduce the root-mean-square (RMS) phase jitter of the received signal from 219 ps to 19.72 ps, and improve the frequency stability of the system at 10,000 s by 84.5%. Overall, the proposed CNN-LSTM PCM can effectively compensate for residual phase fluctuations generated by the optical path asymmetry, providing a potential option for achieving stable RF transmission in telecommunication networks.

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“…Therefore, machine learning algorithms were used to model the GNSS position time series, and spatio-temporal correlations were taken into account in the modeling process. Gao et al [21] considered potential connections between GNSS vertical time series and multiple geophysical factors (polar motion, temperature, atmospheric pressure, etc. ), performed modeling of GNSS vertical time series by gradient boosting decision tree (GBDT), support vector machine (SVM) and long short-term memory (LSTM) algorithms, respectively, and compared with least squares fitting methods to verify the better performance and effectiveness of machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

Li,

Lu,

et al. 2023

Remote Sensing

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The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

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Modelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches

Cited by 25 publications

References 56 publications

Postseismic Deformation in the Northern Antarctic Peninsula Following the 2003 and 2013 Scotia Sea Earthquakes

Postseismic Deformation in the Northern Antarctic Peninsula Following the 2003 and 2013 Scotia Sea Earthquakes

A CNN-LSTM Phase Compensation Method for Unidirectional Two-way Radio Frequency Transmission System

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

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