USTClitho2.0: Updated Unified Seismic Tomography Models for Continental China Lithosphere from Joint Inversion of Body-Wave Arrival Times and Surface-Wave Dispersion Data

Abstract

Xin et al. (2019) presented 3D seismic velocity models (VP and VS) of crust and uppermost mantle of continental China using seismic body-wave travel-time tomography, which are referred to as Unified Seismic Tomography Models for Continental China Lithosphere 1.0 (USTClitho1.0). Compared with previous models of continental China, the VP and VS models of USTClitho1.0 have the highest spatial resolution of 0.5°–1.0° in the horizontal direction and are useful for better understanding the complex tectonics of continental China. Although USTClitho1.0 is implicitly constrained by surface-wave data by using the VS model from surface-wave tomography and the converted VP model as initial models for body-wave travel-time tomography, the predicted surface-wave dispersion curves from USTClitho1.0 do not fit the observed data well. Here, we present updated 3D VP and VS models of the continental China lithosphere (USTClitho2.0) by joint inversion of body-wave arrival times and surface-wave dispersion data. Compared with the previous joint inversion scheme of Zhang et al. (2014), similar to Fang et al. (2016), it is further improved by including the sensitivity of surface-wave dispersion data to VP in the new joint inversion system. As a result, the shallow VP structure is also better imaged. In addition, the new joint inversion scheme considers the large topography variations between the eastern and western parts of China. Thus, USTClitho2.0 better resolves the upper-crustal structure of the Tibetan plateau. Compared with USTClitho1.0, USTClitho2.0 fits both body-wave arrival times and surface-wave dispersion data. Thus, the new velocity models are more accurate and can serve as a better reference model for regional-scale tomography and geodynamic studies in continental China.

Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning

“…At present, the commonly used methods include body wave tomography, surface wave tomography, receiver function, joint inversion, etc. With the significant improvement of the density and quality of the regional seismic network, many velocity structure models have been produced [8][9][10][11][12][13][14][15][16]. However, due to the inherent uncertainty of the model and the different sensitivity of different data types and inversion methods, the inversion results also have some differences.…”

“…The research on fault activity has been greatly improved [3][4][5]. At the same time, fruitful scientific research achievements have been produced, such as precise location data, focal mechanism solutions [6,7], and velocity structure models based on different methods and data sources [8][9][10][11][12][13][14][15][16]. In addition, there has been a good attempt to study local engineering geological conditions by integrating multi-source geophysical data [17].…”

Feasibility of Estimating Parameters and Enhancing Reflections of Dipping Moho From Teleseismic P Wave Coda Autocorrelation

“…The surface topography data is extracted from earth _ relief _01 m (shown in Figure 4a; Tozer et al., 2019). The velocity model is extracted from USTClitho2.0 (Han et al., 2022). We take the P ‐wave velocity isosurface of 7.3 km/s in the velocity model as the Moho (shown in Figure 10).…”

Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions

“…However, the above assumptions cannot be satisfied under complex geological settings, so the results of the H‐κ method may be inaccurate. The Chinese continent is known to have highly heterogeneous crustal structure and extremely variable Moho depth, as revealed for example, by seismic tomography (Bao et al., 2015; Han et al., 2021; M. Li et al., 2022; Shen et al., 2016; X. Zhang et al., 2022) and receiver functions (C. Li et al., 2014; J. Li et al., 2017; Y. Li et al., 2014; X. Wang et al., 2017).…”