Wave-equation dispersion inversion of Love waves

Li, Jing; Hanafy, Sherif M.; Liu, Zhaolun; Schuster, Gerard T.

doi:10.1190/geo2018-0039.1

Cited by 21 publications

(10 citation statements)

References 58 publications

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“…These curves are then inverted by the wave‐equation dispersion inversion method (Li et al . ) and the inverted S‐velocity tomogram is shown in Fig. .…”

Section: Numerical Testsmentioning

confidence: 99%

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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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“…These curves are then inverted by the wave‐equation dispersion inversion method (Li et al . ) and the inverted S‐velocity tomogram is shown in Fig. .…”

Section: Numerical Testsmentioning

confidence: 99%

“…Li et al . () used the WD S‐velocity tomogram to infer a LVZ in this area. The seismic data are recorded over the Qademah fault, where the shot spacing is 10 m, the vertical‐component receiver spacing is 5 m, and there are 120 traces per shot gather to give a total of 60 shot gathers.…”

Section: Numerical Testsmentioning

confidence: 99%

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

Chen

Schuster

2020

Geophysical Prospecting

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“…A 90 kg weight drop is used as the seismic source, with 10 to 15 stacks at each shot location. Inverting the Love waves gives a low-S-velocity zone (LVZ) above 20 m in the middle of site 2 (Li et al, 2017b).…”

Section: Qademah Field Datamentioning

confidence: 99%

Dispersion inversion of guided P-waves in a waveguide of arbitrary geometry

Hanafy

Schuster

2018

SEG Technical Program Expanded Abstracts 2018

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“…Xia et al (2012) demonstrated that higher signal to noise in dispersion images of Love-wave energy is the main advantage of using the multichannel analysis of Love-waves to obtain S-wave velocity profiles. Li et al (2019) found Love-wave dispersion curve produced better convergence properties than that of Rayleigh waves in their presented wave-equation-based inversion method.…”

Section: Introductionmentioning

confidence: 96%

2D time‐domain full‐waveform inversion of SH‐ and Love‐waves for geotechnical site characterization

Chen

Tran

Wang

2020

Near Surface Geophysics

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Full-waveform inversion has proved to be an effective and robust approach for near-surface site characterization. Past full-waveform inversion studies have mostly focused on Rayleigh wave, with only a few studies focussing on SH-and Love-waves. Compared with Rayleigh waves, the main advantages of using SH-and Love-waves are that they are more sensitive to mass density of materials and require much less computing time for simulation. In this study, we present an efficient SH-and Love-wave full-waveform inversion method to extract both S-wave velocity and mass density of soil and rock. The method is based on the solution of 2D elastic SH-wave equations and the adjoint-state gradient approach with an implementation of Tikhonov regularization. We use synthetic and field experiments to test the capability of the method. The synthetic experiment indicates that the presented method can accurately characterize a challenging soil profiel represented by the presence of a velocity reversal along with variable layers of high and low S-wave velocity and density. Variable layer interfaces, true S-wave velocity and density values are well recovered. The field experiment successfully characterizes the subsurface structure to a depth of 18 m, that includes a soil layer underlain by limestone bedrock. Two invasive standard penetration tests were conducted to verify the inverted seismic results. The trend in depth of the standard penetration test N-values generally agrees with the inverted results, including identification of a soft soil layer.

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Wave-equation dispersion inversion of Love waves

Cited by 21 publications

References 58 publications

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

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

Dispersion inversion of guided P-waves in a waveguide of arbitrary geometry

2D time‐domain full‐waveform inversion of SH‐ and Love‐waves for geotechnical site characterization

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