Surface Wave Tomography of Northeastern Tibetan Plateau Using Beamforming of Seismic Noise at a Dense Array

Abstract

In traditional surface wave tomography based on seismic noise, 2D phase or group velocity distribution is obtained by performing pure-path inversion after extracting interstation velocities based on the noise cross-correlation function. In this paper, we show that 2D surface wave phase velocity maps of adequate quality can be obtained directly, without interferometry, by beamforming the ambient noise recorded at array of stations. This method does not require a good azimuthal distribution of the noise sources. The 2D surface wave phase velocity map is obtained by moving the subarrays within a larger dense network of stations. The method is illustrated with seismic noise recorded by over 600 stations of the ChinArray (Phase II). We obtain 2D Rayleigh wave phase velocity maps between 7 and 35 s in Northeastern (NE) Tibetan Plateau and adjacent regions that compare well with results obtained with other methods. The shear wave velocity model is then derived by inverting the phase velocity with depth. The model correlates well with geology and tectonics in NE Tibet. Two clear mid-to-low crustal low-velocity zones are observed at 15-to 35-km depth beneath the Songpan-Ganzi terrane and Northwestern Qilian Orogen, possibly facilitating lower crustal flow in this key region for the tectonic evolution of NE Tibet.

High‐Resolution 3D Shallow S Wave Velocity Structure of Tongzhou, Subcenter of Beijing, Inferred From Multimode Rayleigh Waves by Beamforming Seismic Noise at a Dense Array

“…The azimuth‐averaged phase velocity under the array can be estimated with minimal dependence on the distribution of noise sources and array geometry using BF if the azimuthal anisotropy is not concerned (Lu et al., 2018; Wang et al., 2020). For the case only the fundamental Rayleigh mode is dominated, BF has already been successfully used in the Californian (Roux & Ben‐Zion, 2017) and China array (Wang et al., 2020) with moving subarrays. If more than one mode dominates the noise record, the phase velocity of multimodes can in theory be measured by BF, as long as their velocities differ significantly.…”

A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint inversion of 3D MT and Surface-Wave Data

“…the misfit function is given by Eq. 3 ) with a representative standard deviation of 1% of the velocity in meters per second, comparable to the data errors expected for real SW data in dense arrays (Moorkamp et al, 2010;Yang & Forsyth, 2006;Wang et al, 2020). We have added random noise to all the data (see example in Figs.…”

A Reduced Order Approach for Probabilistic Inversions of 3D Magnetotelluric Data II: Joint inversion of MT and Surface-Wave Data

“…We assume normally distributed data errors with a representative standard deviation (std) of 20% of the period for Example 1 (that is, 2 * std = 10m/s for 25s and 2 * std = 80m/s for 200s). For the second example, we consider a standard deviation of 1% of the velocity in meters, which is comparable to the data errors expected for real SW data in dense arrays (Moorkamp et al, 2010;Yang & Forsyth, 2006;Wang et al, 2020).…”