Anisotropic tomography of eastern Tibet and its uncertainty from hypocentral errors

Summary The mechanism responsible for the lateral expansion and uplift of the eastern Tibetan Plateau remains a topic of ongoing debate, partly due to discrepancies in the results of seismic velocity and anisotropy. In local earthquake tomography, hypocentral uncertainties can cause significant errors in the tomographic model. However, this issue has received limited attention in previous studies. In this work, we employ the weighted least-squares (WLS) method to solve the tomographic inversion problem. A power exponent coefficient, which is called weighting level, is introduced into the weighting matrix to control the relative contribution of the data with different hypocentral errors to the final tomographic result. Our data set contains high-quality Pg, Pn and Sg arrival times of local earthquakes recorded by the dense Chinese seismic network in eastern Tibet during 2008 to 2022. We comprehensively analyze the inversion results derived from the WLS inversions with different weighting levels to evaluate the robustness of isotropic velocity anomalies and azimuthal anisotropy. The most robust feature of our results is a striking low-velocity (low-Vp) zone surrounded by high-velocity (high-Vp) anomalies and fault parallel fast-velocity directions (FVDs) of azimuthal anisotropy in the lower crust beneath the western side of the Longmenshan fault zone. Taking into account many previous results of the region, we deem that the low-Vp zone reflects hot and wet upwelling flow from the deep asthenosphere, which ascends to the lower crust along the fault zone. At the NE margin of the Tibetan Plateau, significant low-Vp anomalies exist in the lower crust and the FVDs are consistent with the motion direction of the Tibetan block revealed by GPS observations. We think that lower crustal flow exists beneath NE Tibet, which controls the plateau expansion toward the northeast. A low-Vp anomaly appears at 30 km depth beneath the Sichuan Basin. However, as the weighting level increases, the amplitude of this low-Vp anomaly decreases by more than 6%, suggesting that this low-Vp anomaly has a larger uncertainty than the other features.