Crustal deformation before the 2008 Wenchuan MS8.0 earthquake studied using GPS data

We installed 10 continuous Global Positioning System (GPS) stations on the northeast margin of the Tibetan Plateau at the end of 2012, in order to qualitatively investigate strain accumulation across the Liupanshan Fault (LPSF). We integrated our newly built stations with 48 other existing GPS stations to provide new insights into three-dimensional tectonic deformation. We employed white plus flicker noise model as a statistical model to obtain realistic velocities and corresponding uncertainties in the ITRF2014 and Ordos-fixed reference frame. The total velocity decrease from northwest to southeast in the Longxi Block (LXB) was 5.3 mm/yr within the range of 200 km west of the LPSF on the horizontal component. The first-order characteristic of the vertical crustal deformation was uplift for the northeastern margin of the Tibetan Plateau. The uplift rates in the LXB and the Ordos Block (ORB) were 1.0 and 2.0 mm/yr, respectively. We adopted an improved spherical wavelet algorithm to invert for multiscale strain rates and rotation rates. Multiscale strain rates showed a complex crustal deformation pattern. A significant clockwise rotation of about 30 nradians/yr (10−9 radians/year) was identified around the Dingxi. Localized strain accumulation was determined around the intersectional region between the Haiyuan Fault (HYF) and the LPSF. The deformation pattern across the LFPS was similar to that of the Longmengshan Fault (LMSF) before the 2008 Wenchuan MS 8.0 earthquake. Furthermore, according to the distributed second invariant of strain rates at different spatial scale, strain partitioning has already spatially localized along the Xiaokou–Liupanshan–Longxian–Baoji fault belt (XLLBF). The tectonic deformation and localized strain buildup together with seismicity imply a high probability for a potential earthquake in this zone.

Section: Gps-derived Three-dimensional Velocitiessupporting

confidence: 78%

Section: Gps-derived Strain Ratessupporting

confidence: 63%

Section: Gps-derived Three-dimensional Velocitiesmentioning

confidence: 52%

See 1 more Smart Citation

Crustal Deformation on the Northeastern Margin of the Tibetan Plateau from Continuous GPS Observations

Yao

et al. 2018

“…Secondly, the observation data for a given day were combined to produce daily solutions for loosely constrained station coordinates and satellite orbits using the release 10.40 of GAMIT software [25]. Lastly, the station coordinates and velocity were estimated using the above daily solutions in the International Terrestrial Reference Frame 2008 (ITRF2008 reference) with the GLOBK software [26,27]; see Wu et al (2015) [28] for some more detail about the solving strategy. In Figure 1a, the interseismic velocity is presented, and Figure 1b shows the co-seismic displacement of the Wenchuan earthquake [15].…”

Section: Gps Campaign Datamentioning

confidence: 99%

Deformation Response of Seismogenic Faults to the Wenchuan MS 8.0 Earthquake: A Case Study for the Southern Segment of the Longmenshan Fault Zone

Jiang

Liang

et al. 2018

Self Cite

“…The LSC can weaken the influence of points position distribution on a deformation field by establishing the covariance matrix between the estimated and observation points and can accurately describe the regional crustal movement trend [6]. The LSC algorithm has been used to estimate crustal movement signals from GPS velocity fields [9][10][11][12][13][14][15]. It has also been used in various earth science fields to control systematic and anomaly errors in GIS spatial data [16], detect outliers in multibeam bathymetric data [17], solve common point coordinate errors in 3D coordinate transformation [18], improve the accuracy of mobile light detection and ranging (LiDAR) systems in hostile environments [19], improve the results of downward continuation values of airborne gravity data [20], and refine the local covariance model of gravity anomalies [21].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Least-Squares Collocation Algorithm Considering Distance Scale Factor for GPS Crustal Velocity Field Fitting and Estimation

Chen

Liang

et al. 2019

High-precision, high-reliability, and high-density GPS crustal velocity are extremely important requirements for geodynamic analysis. The least-squares collocation algorithm (LSC) has unique advantages over crustal movement models to overcome observation errors in GPS data and the sparseness and poor geometric distribution in GPS observations. However, traditional LSC algorithms often encounter negative covariance statistics, and thus, calculating statistical Gaussian covariance function based on the selected distance interval leads to inaccurate estimation of the correlation between the random signals. An unreliable Gaussian statistical covariance function also leads to inconsistency in observation noise and signal variance. In this study, we present an improved LSC algorithm that takes into account the combination of distance scale factor and adaptive adjustment to overcome these problems. The rationality and practicability of the new algorithm was verified by using GPS observations. Results show that the new algorithm introduces the distance scale factor, which effectively weakens the influence of systematic errors by improving the function model. The new algorithm can better reflect the characteristics of GPS crustal movement, which can provide valuable basic data for use in the analysis of regional tectonic dynamics using GPS observations.