Wave-equation dispersion inversion of surface waves recorded on irregular topography

Li, Jing; Schuster, Gerard T.; Lin, Fan‐Chi; Alam, A. M. Z.

doi:10.1190/segam2017-17663038.1

Cited by 12 publications

(17 citation statements)

References 23 publications

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“…We use the multioffset WDG strategy to invert for the P-velocity model using the conjugate-gradient method (J. Li, Lin, et al, 2017). Figure 7f shows the WDG tomogram after 25 iterations, and the predicted dispersion curves shown in Figure 8a (black solid lines) resemble the observed values (black dashed lines).…”

Section: Two-layer Model With An Irregular Interfacementioning

confidence: 92%

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Wave‐Equation Dispersion Inversion of Guided P Waves in a Waveguide of Arbitrary Geometry

Hanafy

Schuster

2018

JGR Solid Earth

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We present a dispersion‐inversion method which inverts for the P‐velocity model from guided waves propagating in wave guides of arbitrary geometry. Its misfit function is the squared summation of differences between the predicted and observed dispersion curves of guided P waves, and the inverted result is a high‐resolution estimate of the near‐surface P‐velocity model. We denote this procedure as wave‐equation dispersion inversion of guided P waves (WDG), which is valid for near‐surface waveguides with irregular layers and does not require a high‐frequency approximation. It is more robust than full waveform inversion and can sometimes provide velocity models with higher resolution than wave‐equation traveltime tomography. Both the synthetic‐data and field data results demonstrate that WDG for guided P waves can accurately invert for complex P‐velocity models at the near surface of the Earth.

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Section: Two-layer Model With An Irregular Interfacementioning

confidence: 92%

“…Inverting the Love waves gives a low-S-velocity tomogram in the middle of site 2 (J. Li, Lin, et al, 2017).…”

Section: Qademah Field Datamentioning

confidence: 99%

Wave‐Equation Dispersion Inversion of Guided P Waves in a Waveguide of Arbitrary Geometry

Hanafy

Schuster

2018

JGR Solid Earth

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“…At local (exploration) scales, Nuber et al (2016) showed that neglecting topography with an amplitude fluctuation greater than half the minimum seismic wavelength leads to appreciable inversion artifacts. Similarly, Li et al (2017) demonstrated the importance of including topography when inverting for S-wave velocity from Rayleigh wave dispersion curves. Although these studies were done for smaller scales and shorter wavelengths where the effect of topography is clearly larger than in our case, we will find that topography may need to be included for regional wave propagation at intermediate periods.…”

Section: Modelling Wave Propagation In Ice-covered Regionsmentioning

confidence: 93%

Numerical wave propagation through ice-covered regions

Thrastarson¹,

Driel²,

Afanasiev³

et al. 2019

Preprint

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In recent years, seismic station coverage in polar regions has been increasing steadily, providing new insight into the deep structure and dynamics of these remote parts of the globe. Numerical seismic wave propagation through polar regions is complicated by the presence of ice sheets. At periods relevant for regional-scale waveform tomography, the ice acts as a thin layer that, for an exact solution, needs to be meshed by elements that are small compared to the minimum wavelength. As a consequence, computational costs may become prohibitively high.In this study, we investigate the effects of various simplifications of the mesh using spectral-element simulations of regional seismic wave propagation, with propagation distances of several hundred kilometres. Our goals are to quantify errors in phase and amplitude, and to identify simplifications that significantly reduce computational costs while producing numerical errors that are tolerable given a concrete application, dataset and frequency band. Using the example of western Greenland, we specifically consider meshes where 1) the ice sheet is replaced by bedrock, 2) where the ice sheet is removed but bedrock topography is honored, and 3) where any topography and the ice sheet are ignored.For periods of 10 s we find that the group velocity of fundamental-mode surface waves can be off by more than 2 % when the ice sheet topography is ignored (2 and 3). The actual elastic properties of the ice play a comparatively smaller role. Furthermore, the ice sheet-related traveltime errors diminish with increasing period, whereas the topography-related errors are more frequency-independent. This suggests (1) that regional-scale topography with height much less than a wavelength should be modelled also in ice-free regions, and (2) that computational-costs may be reduced substantially by replacing ice with bedrock in the simulations, while fully honoring topography.Amplitude errors in the fundamental-mode surface waves are equivalent to variations in attenuation that easily exceed 100 %, regardless of the type of simplification. It follows that the ice sheet should be meshed accurately when amplitude information is used to infer regional-scale Earth structure.

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“…; Li et al . ). Such information is important for earthquake research, seismology and near‐surface engineering studies (Xia, Miller and Park ; Lin, Moschetti and Ritzwoller ; Li and Hanafy ; Liu et al .…”

Section: Introductionmentioning

confidence: 97%

“…To mitigate this problem, Li et al . () and Li, Feng and Schuster () presented the theory for inverting dispersion curves associated with lateral and vertical variations in the velocity model. The misfit function is the sum of the squared wavenumber differences between the predicted and observed dispersion curves, and the inverted result is a moderate‐to‐high‐resolution estimate of the near‐surface S‐velocity model.…”

Section: Introductionmentioning

confidence: 99%

Separation of multi‐mode surface waves by supervised machine learning methods

Chen

Schuster

2020

Geophysical Prospecting

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A B S T R A C TLogistic regression, neural networks and support vector machines are tested for their effectiveness in isolating surface waves in seismic shot records. To distinguish surface waves from other arrivals, we train the algorithms on three distinguishing features of surface-wave dispersion curves in the k − ω domain: spectrum coherency of the trace's magnitude spectrum, local dip and the frequency range for a fixed wavenumber k in the spectrum. Numerical tests on synthetic data show that the kernel-based support vector machines algorithm gives the highest accuracy in predicting the surface-wave window in the k − ω domain compared to neural networks and logistic regression. This window is also used to automatically pick the fundamental dispersion curve. The other two methods correctly pick the low-frequency part of the dispersion curve but fail at higher frequencies where there is interference with higher-order modes.

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Wave-equation dispersion inversion of surface waves recorded on irregular topography

Cited by 12 publications

References 23 publications

Wave‐Equation Dispersion Inversion of Guided P Waves in a Waveguide of Arbitrary Geometry

Wave‐Equation Dispersion Inversion of Guided P Waves in a Waveguide of Arbitrary Geometry

Numerical wave propagation through ice-covered regions

Separation of multi‐mode surface waves by supervised machine learning methods

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