Azimuthal anisotropy beneath southern Africa from very broad-band surface-wave dispersion measurements

SUMMARY SWinvert is a workflow developed at The University of Texas at Austin for the inversion of surface wave dispersion data. SWinvert encourages analysts to investigate inversion uncertainty and non-uniqueness in shear wave velocity (Vs) by providing a systematic procedure and specific actionable recommendations for surface wave inversion. In particular, the workflow encourages the use of multiple layering parametrizations to address the inversion's non-uniqueness, multiple global searches for each parametrization to address the inverse problem's non-linearity and quantification of Vs uncertainty in the resulting profiles. While the workflow uses the Dinver module of the popular open-source Geopsy software as its inversion engine, the principles presented are of relevance to analysts using other inversion programs. To illustrate the effectiveness of the SWinvert workflow and to develop a set of benchmarks for use in future surface wave inversion studies, synthetic experimental dispersion data for 12 subsurface models of varying complexity are inverted. While the effects of inversion uncertainty and non-uniqueness are shown to be minimal for simple subsurface models characterized by broad-band dispersion data, these effects cannot be ignored in the Vs profiles derived for more complex models with band-limited dispersion data. To encourage adoption of the SWinvert workflow, an open-source Python package (SWprepost), for pre- and post-processing of surface wave inversion data, and an application on the DesignSafe-Cyberinfrastructure (SWbatch), for performing batch-style surface wave inversions with Dinver using high-performance computing, have been developed and released in conjunction with this work. The SWinvert workflow is shown to provide a methodical procedure and a powerful set of tools for performing rigorous surface wave inversions and quantifying the uncertainty in the resulting Vs profiles.

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Layered anisotropy beneath the Japan Sea and NE China from inversion of surface wave dispersion using rj-MCMC method

10.5194/egusphere-egu2020-6381 ◽

2020 ◽

Author(s):

Yanzhe Zhao ◽

Zhen Guo ◽

Xingli Fan ◽

Yanbin Wang

Keyword(s):

Surface Wave ◽

Shear Wave ◽

Wave Velocity ◽

Shear Wave Velocity ◽

Japan Sea ◽

Wave Dispersion ◽

Azimuthal Anisotropy ◽

Ne China ◽

Surface Wave Dispersion ◽

The Japan Sea

The surface wave dispersion data with azimuthal anisotropy can be used to invert for the wavespeed azimuthal anisotropy, which provides essential dynamic information about depth-varying deformation of the Earth&#8217;s interior. In this study, we adopt an rj-MCMC (reversible jump Markov Chain Monte Carlo) technique to invert for crustal and upper mantle shear velocity and azimuthal anisotropy beneath the Japan Sea using Rayleigh wave azimuthally anisotropic phase velocity measurements from Fan et al. (2019). The rj-MCMC implements trans-dimensional sampling in the whole model space and derives the distribution for each model parameter (shear wave velocity and anisotropy parameters) directly from data. Without the prejudiced parameterization for model, the result can be more credible, from which some more reliable estimates for shear wave velocity and azimuthal anisotropy as well as their uncertainties can be acquired. Our preliminary results, together with shear wave splitting observations, show a layered anisotropy beneath the Japan Sea and NE China, suggesting the complicated mantle flow that is controlled by the subduction of the Pacific plate and the large-scale upwelling beneath the Changbaishan volcano.

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Azimuthal anisotropy in Bayesian surface wave tomography: application to northern Cascadia and Haida Gwaii, British Columbia

Geophysical Journal International ◽

10.1093/gji/ggaa561 ◽

2020 ◽

Vol 224 (3) ◽

pp. 1724-1741

Author(s):

Jeremy M Gosselin ◽

Pascal Audet ◽

Andrew J Schaeffer ◽

Fiona A Darbyshire ◽

Clément Estève

Keyword(s):

Surface Wave ◽

Group Velocity ◽

Wave Dispersion ◽

Azimuthal Anisotropy ◽

Surface Wave Tomography ◽

Haida Gwaii ◽

Surface Wave Dispersion ◽

Data Errors ◽

Dispersion Data ◽

Wave Tomography

SUMMARY Surface wave tomography is a valuable tool for constraining azimuthal anisotropy at regional scales. However, sparse and uneven coverage of dispersion measurements make meaningful uncertainty estimation challenging, especially when applying subjective model regularization. This paper considers azimuthal anisotropy constrained by measurements of surface wave dispersion data within a Bayesian trans-dimensional (trans-d) tomographic inversion. A recently proposed alternative model parametrization for trans-d inversion is implemented in order to produce more realistic models than previous studies considering trans-d surface wave tomography. The reversible-jump Markov chain Monte Carlo sampling technique is used to numerically estimate the posterior probability density of the model parameters. Isotropic and azimuthally anisotropic components of surface wave group velocity maps (and their associated uncertainties) are estimated while avoiding model regularization and allowing model complexity to be determined by the data information content. Furthermore, data errors are treated as unknown, and solved for within the inversion. The inversion method is applied to measurements of surface wave dispersion from regional earthquakes recorded over northern Cascadia and Haida Gwaii, a region of complex active tectonics but highly heterogeneous station coverage. Results for isotropic group velocity are consistent with previous studies that considered the southern part of the study region over Cascadia. Azimuthal anisotropic fast-axis directions are generally margin-parallel between Vancouver Island and Haida Gwaii, with a small change in direction and magnitude along the margin which may be attributed to the changing tectonic regime (from subduction to transform tectonics). Estimated errors on the dispersion data (solved for within the inversion) reveal a correlation between surface wave period and the dependence of data errors on travel path length. This paper demonstrates the value of considering azimuthal anisotropy within Bayesian tomographic inversions. Furthermore, this work provides structural context for future studies of tectonic structure and dynamics of northern Cascadia and Haida Gwaii, with the aim of improving our understanding of seismic and tsunami hazards.

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Processing seismic ambient noise data to obtain reliable broad-band surface wave dispersion measurements

Geophysical Journal International ◽

10.1111/j.1365-246x.2007.03374.x ◽

2007 ◽

Vol 169 (3) ◽

pp. 1239-1260 ◽

Cited By ~ 987

Author(s):

G. D. Bensen ◽

M. H. Ritzwoller ◽

M. P. Barmin ◽

A. L. Levshin ◽

F. Lin ◽

...

Keyword(s):

Surface Wave ◽

Ambient Noise ◽

Broad Band ◽

Wave Dispersion ◽

Surface Wave Dispersion ◽

Seismic Ambient Noise ◽

Noise Data

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Layered mantle flow beneath the Japan Sea and NE China from inversion of surface wave dispersion using rj-MCMC method

10.5194/egusphere-egu21-9806 ◽

2021 ◽

Author(s):

Yanzhe Zhao ◽

Zhen Guo ◽

Yanbin Wang ◽

Xingli Fan

Keyword(s):

Surface Wave ◽

Sea Of Japan ◽

Korean Peninsula ◽

Wave Dispersion ◽

Azimuthal Anisotropy ◽

Japan Trench ◽

East China ◽

Surface Wave Dispersion ◽

Philippine Sea ◽

Dispersion Data

The surface wave dispersion data with azimuthal anisotropy can be used to invert for the wavespeed azimuthal anisotropy, which provides essential dynamic information about depth-varying deformation of the Earth&#8217;s interior. The traditional method to slove this inversion problem is a two-step process, i.e. inverting the isotropic wavespeed first, based on which the anisotropic part is solved successively. In this study, we try to simultaneously invert both the isotropic and anisotropic shear wave velocity using the rj-MCMC (reversible jump Markov Monte Carlo) algorithm, which allows sampling the model space in a transdimensional way.Our resarch is conducted in the Northeast Aisa, including the East and Northeast China (EC and NEC), Korean Peninsula and the sea of Japan (see Fig. 1). The previous anisotropic and tomographic studies were mainly conducted on separated continents, lacking a panoramic view of geodynamics across the entire region. In this study, we construct a crustal and uppermantle model of the whole ragion based on the Rayleigh wave dispersion data collected by Fan et al. (2020, GRL), and acquire high-resolution patterns reflecting valuable geodynamic characteristics.&#160;<img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.b0e3c3a9850061565790161/sdaolpUECMynit/12UGE&app=m&a=0&c=795fbbfedd6847e1e6ec5631f617bb03&ct=x&pn=gepj.elif&d=1" alt="">Figure 1. Map of the NE Asia showing the main tectonic features. Major blocks: NEC = north-east China; EC = East China; KP = Korean Peninsula; KS = Korea Strait; SoJ = Sea of Japan; JI = Japanese Island. The gray area in the background delineates the major sedimentary basins with thickness no less than 1.5 km. Red volcano symbols denote the Late Cenozoic intraplate volcanoes, including: CBV = Changbaishan volcano; JPHV = Jingpohu volcano; LGV = Longgang volcano; XJDV = Xianjingdao volcano; CRV = ChugaRyong volcano; ULV = Ulleung volcano; HLV = Halla volcano; FJV = FukueJima volcano. Small red triangles show the locations of island arc volca-noes. The Japan Trench where the western Pacific Plate subducts, and the Ryukyu Trench where the Philippine Sea Plate subducts are outlined by black lines with white sawtooth. Interface depths of the subducting Pacific slab and Philippine Sea slab are marked by white and purple dashed lines, respectively, with depth annotation. The Tanlu fault zone (TLFZ) is represented by thin black lines.

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Seismic structure of Precambrian lithosphere: New constraints from broad-band surface-wave dispersion

Lithos ◽

10.1016/j.lithos.2008.06.010 ◽

2009 ◽

Vol 109 (1-2) ◽

pp. 96-111 ◽

Cited By ~ 88

Author(s):

Sergei Lebedev ◽

Jan Boonen ◽

Jeannot Trampert

Keyword(s):

Surface Wave ◽

Broad Band ◽

Wave Dispersion ◽

Seismic Structure ◽

Surface Wave Dispersion

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Improved Surface Wave Dispersion Models, Amplitude Measurements and Azimuth Estimates

10.21236/ada438946 ◽

2005 ◽

Cited By ~ 2

Author(s):

Jeffry L. Stevens ◽

David A. Adams ◽

G. E. Baker ◽

Mariana G. Eneva ◽

Heming Xu

Keyword(s):

Surface Wave ◽

Wave Dispersion ◽

Dispersion Models ◽

Surface Wave Dispersion

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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

Geophysical Journal International ◽

10.1093/gji/ggaa230 ◽

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.

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

Journal of Seismology ◽

10.1007/s10950-019-09888-1 ◽

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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