Secular crustal deformation characteristics prior to the 2011 Tohoku-Oki earthquake detected from GNSS array, 2003–2011

Xu, Kuiying; He, Rong; Li, Kezhao; Ren, Ankang; Shao, Zhenhua

doi:10.1016/j.asr.2021.10.036

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…Contemporary geodetic techniques such as Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) are the most direct and effective tools for observing ground displacement [42][43][44][45][46]. This paper obtained ascending and descending orbit data of the Maduo earthquake on 22 May 2021 using the Sentinel-1 IW mode.…”

Section: Inversion Datamentioning

confidence: 99%

Source Parameter Inversion and Century-Scale Stress Triggering Analysis of the 2021 Maduo MW7.4 Earthquake Using GNSS and InSAR Displacement Fields

Xu,

Wang,

Wan

2023

Remote Sensing

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To explore the degree of constraint by Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) data on the Maduo earthquake within a layered earth model structure and to gain an insight into the seismogenic mechanism and the seismic risk in the surrounding area, this study employs D-InSAR technology to acquire the InSAR co-seismic deformation field of the Maduo earthquake on 22 May 2021. Utilizing both GNSS and InSAR data, the inversions constrained by single and joint data are conducted and compared to determine the co-seismic slip model and fault plane stress distribution of the Maduo earthquake. Additionally, this paper calculates the Coulomb stress changes induced by 14 M ≥ 7 strong earthquakes, considering co-seismic effects, post-seismic viscoelastic relaxation, and inter-seismic tectonic stress loading, on 19 fault segments within the Bayan Har block research area (96°E~106°E, 29°N~36°N) since 1900. The findings are as follows: (1) The maximum line-of-sight (LOS) deformation was approximately 0.9 m. The joint inversion rupture was primarily located in the Dongcao Along Lake section (~98.6°E), aligning with previous research outcomes. (2) The cumulative Coulomb stress at the Maduo earthquake’s source location was −0.1333 MPa, while the inter-seismic stress loading amounted to 0.0745 MPa. The East Kunlun Fault, Maduo–Gande Fault, Ganzi–Yushu Fault, and Dari Fault C exhibited considerable stress loading, warranting attention due to heightened seismic risk. (3) Based on three different co-seismic slip models, the stress disturbance results caused by the Maduo earthquake to the surrounding area and fault did not differ significantly. After the earthquake, the seismogenic fault still has high seismic risk.

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Section: Inversion Datamentioning

confidence: 99%

Source Parameter Inversion and Century-Scale Stress Triggering Analysis of the 2021 Maduo MW7.4 Earthquake Using GNSS and InSAR Displacement Fields

Xu,

Wang,

Wan

2023

Remote Sensing

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“…In the last thirty years, more than 20,000 global navigation satellite system (GNSS) continuously operating reference stations (CORS) have been established worldwide, and the GNSS position time series observed by these CORSs can provide effective data support for geoscience research. By analyzing the GNSS position time series, researchers have studied crustal movement [1][2][3], the maintenance of regional or global geodetic reference frames [4][5][6], engineering deformation monitoring [7][8][9], and other geodynamic phenomena [10][11][12].…”

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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“…These data can effectively describe the long-term trend and the nonlinear changes caused by geophysical effects (Wang et al 2021). Analysing the GNSS position time series is helpful when monitoring crustal plate movements (Staller et al 2018;Hobbs and Ord 2018;Xu et al 2022), dams/bridge deformations (Xi et al 2018;Xiao et al 2019;Konakoglu et al 2020), global or regional reference frame estimation (Altamimi et al 2016;Lahtinen et al 2019;Li et al 2020), analysing regional vertical deformation (Maciuk and Szombara 2018;Yan et al 2019;Gobron et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Forecasting and analysing the GNSS vertical time series with an improved VMD-CXGBoost model

Li,

2023

Preprint

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Global Navigation Satellite System (GNSS) vertical time series studies can monitor crustal deformations and plate tectonics, contributing to the estimation of regional sea-level rise and detecting various geological hazards. This study proposes a new model to forecast and analyze the GNSS vertical time series. This model is based on a method to construct features using the variational mode decomposition (VMD) algorithm and includes a correction function to optimize the eXtreme Gradient Boosting (XGBoost) algorithm, called the VMD-CXGBoost model. To verify the validity of the VMD-CXGBoost model, six GNSS reference stations are selected within China. Compared with VMD-CNN-LSTM, the VMD-CXGBoost-derived forecasting RMSE and MAE are decreased by 20.76% and 23.23%, respectively. The flicker noise and white noise decrease by 15.43% and 25.65%, and the average trend difference is 1 mm/year, with a 15.14% reduction in uncertainty. Compared with the cubic spline interpolation method, the VMD-CXGBoost-derived interpolation RMSE is reduced by more than 40%. Therefore, the proposed VMD-CXGBoost model could be used as a powerful alternative tool to forecast GNSS vertical time series and will be of wide practical value in the fields of reference frame maintenance.

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Secular crustal deformation characteristics prior to the 2011 Tohoku-Oki earthquake detected from GNSS array, 2003–2011

Cited by 8 publications

References 31 publications

Source Parameter Inversion and Century-Scale Stress Triggering Analysis of the 2021 Maduo MW7.4 Earthquake Using GNSS and InSAR Displacement Fields

Source Parameter Inversion and Century-Scale Stress Triggering Analysis of the 2021 Maduo MW7.4 Earthquake Using GNSS and InSAR Displacement Fields

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

Forecasting and analysing the GNSS vertical time series with an improved VMD-CXGBoost model

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