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

2021 ◽  
Author(s):  
Shoucheng Han ◽  
Haijiang Zhang ◽  
Hailiang Xin ◽  
Weisen Shen ◽  
Huajian Yao
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Body Wave ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Arrival Times ◽  
Velocity Models ◽  
Dispersion Data ◽  
Wave Travel ◽  
Wave Arrival

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.

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

Joint inversion of two-dimensional magnetotelluric and surface wave dispersion data with cross-gradient constraints

2020 ◽  
Vol 221 (2) ◽  
pp. 938-950
Author(s):  
Pingping Wu ◽  
Handong Tan ◽  
Changhong Lin ◽  
Miao Peng ◽  
Huan Ma ◽  
...  
Keyword(s):  
Surface Wave ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Uniqueness Problem ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Surface Wave Dispersion ◽  
Gradient Constraints ◽  
Dispersion Data

SUMMARY Multiphysics imaging for data inversion is of growing importance in many branches of science and engineering. Cross-gradient constraint has been considered as a feasible way to reduce the non-uniqueness problem inherent in inversion process by finding geometrically consistent images from multigeophysical data. Based on OCCAM inversion algorithm, a direct inversion method of 2-D profile velocity structure with surface wave dispersion data is proposed. Then we jointly invert the profiles of magnetotelluric and surface wave dispersion data with cross-gradient constraints. Three synthetic models, including block homogeneous or heterogeneous models with consistent or inconsistent discontinuities in velocity and resistivity, are presented to gauge the performance of the joint inversion scheme. We find that owning to the complementary advantages of the two geophysical data sets, the models recovered with structure coupling constraints exhibit higher resolution in the classification of complex geologic units and settle some imaging problems caused by the separate inversion methods. Finally, a realistic velocity model from the NE Tibetan Plateau and its corresponding resistivity model calculated by empirical law are used to test the effectiveness of the joint inversion scheme in the real geological environment.

3D Seismic Velocity Models for Alaska from Joint Tomographic Inversion of Body-Wave and Surface-Wave Data

2020 ◽  
Vol 91 (6) ◽  
pp. 3106-3119
Author(s):  
Avinash Nayak ◽  
Donna Eberhart-Phillips ◽  
Natalia A. Ruppert ◽  
Hongjian Fang ◽  
Melissa M. Moore ◽  
...  
Keyword(s):  
Surface Wave ◽  
High Velocity ◽  
Mantle Wedge ◽  
Seismic Velocity ◽  
Body Wave ◽  
Arrival Times ◽  
Velocity Models ◽  
Wave Data ◽  
Surface Wave Data ◽  
Wave Arrival

Abstract We present two new seismic velocity models for Alaska from joint inversions of body-wave and ambient-noise-derived surface-wave data, using two different methods. Our work takes advantage of data from many recent temporary seismic networks, including the Incorporated Research Institutions for Seismology Alaska Transportable Array, Southern Alaska Lithosphere and Mantle Observation Network, and onshore stations of the Alaska Amphibious Community Seismic Experiment. The first model primarily covers south-central Alaska and uses body-wave arrival times with Rayleigh-wave group-velocity maps accounting for their period-dependent lateral sensitivity. The second model results from direct inversion of body-wave arrival times and surface-wave phase travel times, and covers the entire state of Alaska. The two models provide 3D compressional- (VP) and shear-wave velocity (VS) information at depths ∼0–100  km. There are many similarities as well as differences between the two models. The first model provides a clear image of the high-velocity subducting plate and the low-velocity mantle wedge, in terms of the seismic velocities and the VP/VS ratio. The statewide model provides clearer images of many features such as sedimentary basins, a high-velocity anomaly in the mantle wedge under the Denali volcanic gap, low VP in the lower crust under Brooks Range, and low velocities at the eastern edge of Yakutat terrane under the Wrangell volcanic field. From simultaneously relocated earthquakes, we also find that the depth to the subducting Pacific plate beneath southern Alaska appears to be deeper than previous models.

The Community Velocity Model V.1.0 of Southwest China, Constructed from Joint Body- and Surface-Wave Travel-Time Tomography

2021 ◽  
Author(s):  
Ying Liu ◽  
Huajian Yao ◽  
Haijiang Zhang ◽  
Hongjian Fang
Keyword(s):  
Surface Wave ◽  
Rayleigh Wave ◽  
Travel Time ◽  
Southwest China ◽  
Joint Inversion ◽  
Velocity Model ◽  
Arrival Times ◽  
Velocity Models ◽  
Wave Travel ◽  
Grid Interval

Abstract Southwest China, located at the southeastern margin of the Tibetan plateau, plays an important role for the plateau growth and its material extrusion. It has complicated tectonic environment and strong seismic activities including the 2008 Wenchuan great earthquake. Numerous geophysical studies have been conducted in southwest China. However, a community velocity model (CVM) in this region is still not available, which makes it difficult to have a consistent catalog of earthquake locations and focal mechanisms and a consistent velocity model for simulating strong ground motions and evaluating earthquake hazards. In this study, we aim at building a high-resolution CVM (both VP and VS) of the crust and uppermost mantle in southwest China along with earthquake locations by joint inversion of body- and surface-wave travel-time data. In total, we have assembled 386,958 P- and 372,662 S-wave first arrival times and nearly 8100 Rayleigh-wave dispersion curves in the period band of 5–50 s. A multigrid strategy is adopted in the joint inversion. A coarser horizontal grid interval of 0.5° is first used and then a finer grid interval of 0.25° is used with initial models interpolated from the coarser-grid inverted velocity models. The spatial resolution of both VP and VS models can reach up to 0.5° horizontally and 10 km vertically according to the checkerboard tests. The comparisons of our inverted VP and VS models with those from other studies show general consistency in large-scale features. The inverted models are further validated by P-wave arrival times from active sources and Rayleigh-wave data. In general, our velocity models show two low-velocity zones in the middle-lower crust and a prominent high-velocity region in between them. Our new models have been served as the first version of the CVM in southwest China (SWChinaCVM-1.0) for future studies.

Using Deep Learning to Derive Shear-Wave Velocity Models from Surface-Wave Dispersion Data

2020 ◽  
Vol 91 (3) ◽  
pp. 1738-1751
Author(s):  
Jing Hu ◽  
Hongrui Qiu ◽  
Haijiang Zhang ◽  
Yehuda Ben-Zion
Keyword(s):  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Southern California ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Velocity Models ◽  
Dispersion Data ◽  
Phase Group

Abstract We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase–group images for continental China and 4160 phase–group images for southern California. The conversions of these images to velocity profiles take ∼23  s for continental China and ∼30  s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.

scholarly journals Reliable workflow for inversion of seismic receiver function and surface wave dispersion data: a “13 BB Star” case study

2019 ◽  
Vol 24 (1) ◽  
pp. 101-120
Author(s):  
Kajetan Chrapkiewicz ◽  
Monika Wilde-Piórko ◽  
Marcin Polkowski ◽  
Marek Grad
Keyword(s):  
Surface Wave ◽  
Inverse Problems ◽  
Receiver Function ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Receiver Functions ◽  
P Wave ◽  
Surface Wave Dispersion ◽  
Phase Velocity Dispersion ◽  
Wide Range

AbstractNon-linear inverse problems arising in seismology are usually addressed either by linearization or by Monte Carlo methods. Neither approach is flawless. The former needs an accurate starting model; the latter is computationally intensive. Both require careful tuning of inversion parameters. An additional challenge is posed by joint inversion of data of different sensitivities and noise levels such as receiver functions and surface wave dispersion curves. We propose a generic workflow that combines advantages of both methods by endowing the linearized approach with an ensemble of homogeneous starting models. It successfully addresses several fundamental issues inherent in a wide range of inverse problems, such as trapping by local minima, exploitation of a priori knowledge, choice of a model depth, proper weighting of data sets characterized by different uncertainties, and credibility of final models. Some of them are tackled with the aid of novel 1D checkerboard tests—an intuitive and feasible addition to the resolution matrix. We applied our workflow to study the south-western margin of the East European Craton. Rayleigh wave phase velocity dispersion and P-wave receiver function data were gathered in the passive seismic experiment “13 BB Star” (2013–2016) in the area of the crust recognized by previous borehole and refraction surveys. Final models of S-wave velocity down to 300 km depth beneath the array are characterized by proximity in the parameter space and very good data fit. The maximum value in the mantle is higher by 0.1–0.2 km/s than reported for other cratons.

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