REGIONALIZED SHEAR-VELOCITY MODELS FOR THE UPPER MANTLE INFERRED FROM SURFACE-WAVE DISPERSION DATA

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.

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

Seismological Research Letters ◽

10.1785/0220210122 ◽

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.

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Effects of thin soft layers on body waves

Bulletin of the Seismological Society of America ◽

10.1785/bssa0590052071 ◽

1969 ◽

Vol 59 (5) ◽

pp. 2071-2078

Author(s):

Tom Landers ◽

Jon F. Claerbout

Keyword(s):

Surface Wave ◽

Upper Mantle ◽

Body Wave ◽

Wave Dispersion ◽

Soft Material ◽

Body Waves ◽

Surface Wave Dispersion ◽

Wave Data ◽

Use Of Time ◽

Surface Wave Data

abstract The inability of simple layered models to fit both Rayleigh wave and Love wave data has led to the proposal of an upper mantle interleaved with thin soft horizontal layers. Since surface-wave dispersion is not sensitive to the distribution of soft material but only to the fraction of soft material a variety of models is possible. The solution to this indeterminancy is found through body-wave analysis. It is shown that body waves are dispersed according to the thinness and softness of the layers. Three models, each of which satisfy all surface-wave data, are examined. Transmission seismograms calculated for these models show one to be impossible, one improbable and the other possible. Synthesis of the seismograms is accomplished through the use of time domain theory as the complicated frequency response of the models makes a frequency oriented Haskell-Thompson approach impractical.

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Crustal Velocity Models Retrieved from Surface Wave Dispersion Data for Gujarat Region, Western Peninsular India

Earth Sciences Research Journal ◽

10.15446/esrj.v23n2.58570 ◽

2019 ◽

Vol 23 (2) ◽

pp. 147-155

Author(s):

Vishwa Joshi

Keyword(s):

Wave Dispersion ◽

Data File ◽

Western India ◽

Nonlinear Inversion ◽

S Wave ◽

Surface Wave Dispersion ◽

Peninsular India ◽

Velocity Models ◽

Crustal Velocity ◽

Dispersion Data

The physiographic features of Gujarat state of western India are unique, as they behaved dynamically with several alterations and modifications throughout the geological timescale. It displays a remarkable example of a terrain bestowed with geological, physiographical and climatic diversities. The massive 2001 Bhuj earthquake (M 7.7) over the Kachchh region caused severe damage and devastation to the state of Gujarat and attracted the scientific community of the world to comprehend on its structure and tectonics for future hazard reduction. In the present study, three clusters of wave paths A, B1, and B2 have considered. In each cluster, dispersion data were measured station by station which collectively formed a dispersion data file for a nonlinear inversion through Genetic algorithm. In this way, three crustal velocity models were generated for entire Gujarat. These models are 1) Across Cambay Basin (Path A), 2) Along Saurashtra - Kathiawar Horst (Path B1) and 3) Along Narmada Basin (Path B2), which were formed at different times during the Mesozoic. The average thickness of the crust estimated in the present study for paths A, B1 and B2 are 38.2 km, 36.2 km, and 41.6 km respectively and the estimated S-wave velocity in the lower crust is ~ 3.9 km/s for all the paths. The present study will improve our knowledge about the structure of the seismogenic layer of this active intraplate region

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