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

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

<p>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’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.</p><p>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.</p><p> </p><p><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=""></p><p>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.</p>

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

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.

Short-period surface-wave dispersion and shallow crustal structure of central and eastern Tennessee

1992 ◽  
Vol 82 (2) ◽  
pp. 962-979
Author(s):  
Paul C. Yao ◽  
James Dorman
Keyword(s):  
Surface Wave ◽  
Half Space ◽  
Crustal Structure ◽  
Group Velocity Dispersion ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Zero Crossing ◽  
Clastic Rocks ◽  
Dispersion Data ◽  
Short Period

Abstract Group velocity dispersion of explosion-generated seismic surface waves with periods ranging from 0.2 to 1.5 sec is used to investigate shallow crustal structure of eastern and central Tennessee. Several modes of both Rayleigh and Love waves can be identified and separated on the seismograms of seven SARSN regional network stations by zero-phase digital filtering. Dispersion data for sinusoidal wave motion were based on digitized zero-crossing times. By forward modeling, we find that a wave guide of at least two layers over a half-space can adequately represent our particular multi-mode, narrow-band observations. In a layered section about 3 km thick, lower velocities characterize outcropping clastic rocks of the Cumberland plateau, and higher velocities correspond to shallow carbonate rocks of the Nashville Dome. Half-space shear velocities of about 3.9 km/sec appear to represent lower Paleozoic carbonate lithology deeper than 2 to 4 km on most of the observed paths. Our best data, interpreted jointly with earlier data of Oliver and Ewing (1958) and of Chen et al. (1989), have a composite period range of 0.2 to 40 sec, but they represent different Appalachian paths. Group velocities over this broad spectrum are satisfied by a complex model with two low-velocity layers. The uniqueness of this model cannot be demonstrated, but it represents important hypotheses concerning regional geologic features that can be tested more rigorously by improved surface-wave dispersion data.

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.

scholarly journals Oceanic lithosphere-asthenosphere boundary from surface wave dispersion data

2014 ◽  
Vol 119 (2) ◽  
pp. 1079-1093 ◽  
Author(s):  
G. Burgos ◽  
J.-P. Montagner ◽  
E. Beucler ◽  
Y. Capdeville ◽  
A. Mocquet ◽  
...  
Keyword(s):  
Surface Wave ◽  
Wave Dispersion ◽  
Oceanic Lithosphere ◽  
Surface Wave Dispersion ◽  
Dispersion Data

scholarly journals Probabilistic neural network tomography across Grane field (North Sea) from surface wave dispersion data

2020 ◽  
Vol 223 (3) ◽  
pp. 1741-1757
Author(s):  
S Earp ◽  
A Curtis ◽  
X Zhang ◽  
F Hansteen
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Surface Wave ◽  
Shear Wave ◽  
Wave Velocity ◽  
North Sea ◽  
Shear Wave Velocity ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
Dispersion Data

SUMMARY Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network (NN) based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of NNs called mixture density networks (MDNs), to invert dispersion curves for shear wave velocity models and their non-linearized uncertainty. MDNs are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties as additional inputs to the MDN gives substantially more reliable velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26 772 locations to produce a 3-D velocity model in 21 s on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3-D subsurface imaging and monitoring.

scholarly journals SWinvert: a workflow for performing rigorous 1-D surface wave inversions

2020 ◽  
Vol 224 (2) ◽  
pp. 1141-1156
Author(s):  
Joseph P Vantassel ◽  
Brady R Cox
Keyword(s):  
Surface Wave ◽  
Open Source ◽  
High Performance ◽  
Broad Band ◽  
Wave Dispersion ◽  
Surface Wave Dispersion ◽  
University Of Texas ◽  
Wave Inversion ◽  
Dispersion Data ◽  
Band Limited

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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