Present‑day crustal deformation across the Daliang Shan, southeastern Tibetan Plateau: constrained by a dense GPS network

1. Intensive observations   

In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.

2. Data processing

We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series.

Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017).

The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).

References                                                                

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Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038.

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Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge.

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