Joint Inversion of Body-Wave Arrival Times and Surface-Wave Dispersion for Three-Dimensional Seismic Structure Around SAFOD

2014 ◽  
Vol 171 (11) ◽  
pp. 3013-3022 ◽  
Author(s):  
Haijiang Zhang ◽  
Monica Maceira ◽  
Philippe Roux ◽  
Clifford Thurber
Keyword(s):  
Surface Wave ◽  
Joint Inversion ◽  
Body Wave ◽  
Three Dimensional ◽  
Wave Dispersion ◽  
Seismic Structure ◽  
Surface Wave Dispersion ◽  
Arrival Times ◽  
Wave Arrival

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.

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.

Effects of thin soft layers on body waves

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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