Application of simulated annealing inversion on high-frequency fundamental-mode Rayleigh wave dispersion curves

Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. R77-R85 ◽  
Author(s):  
Donghong Pei ◽  
John N. Louie ◽  
Satish K. Pullammanappallil
Keyword(s):  
Simulated Annealing ◽  
Wave Velocity ◽  
Fundamental Mode ◽  
High Frequency ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Model Parameters ◽  
S Wave ◽  
Velocity Inversion ◽  
Wave Velocities

The simulated annealing (SA) inversion technique has been successfully applied for solving various nonlinear geophysical problems. Following previous developments, we modified the SA inversion, yielding 1D shallow S-wave velocity profiles from high frequency fundamental-mode Rayleigh dispersion curves, and validated the inversion with blind tests. Unlike previous applications of SA, this study draws random numbers from a standard Gaussian distribution. The numbers simultaneously perturb both S-wave velocities and the layer thickness of models. The annealing temperature is gradually decreased following a polynomial-time cooling schedule. Phase velocities are calculated using the reflectivity-transmission coefficient method. The reliability of the model resulting from our implementation is evaluated by statistically calculating the expected values of model parameters and their covariance matrices. Blind tests on two field and 12 synthetic Rayleigh dispersion data sets show that our SA implementation works well for S-wave velocity inversion of dispersion curves from high-frequency fundamental-mode Rayleigh waves. Blind estimates of layer S-wave velocities fall within one standard deviation of the velocities of the original synthetic models in 78% of cases.

P- and S-wave velocity models from surface wave dispersion curves data transform

2017 ◽  
Author(s):  
Valentina Socco ◽  
Farbod Khosro Anjom ◽  
Cesare Comina ◽  
Daniela Teodor
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Velocity Models

Wave-equation dispersion inversion of Love waves

Geophysics ◽  
2019 ◽  
Vol 84 (5) ◽  
pp. R693-R705 ◽  
Author(s):  
Jing Li ◽  
Sherif Hanafy ◽  
Zhaolun Liu ◽  
Gerard T. Schuster
Keyword(s):  
Wave Equation ◽  
Wave Velocity ◽  
Rayleigh Waves ◽  
Love Wave ◽  
Field Data ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
Love Waves ◽  
S Wave ◽  
Love Wave Dispersion

We present a theory for wave-equation inversion of Love-wave dispersion curves, in which the misfit function is the sum of the squared differences between the wavenumbers along the predicted and observed dispersion curves. Similar to inversion of Rayleigh-wave dispersion curves, the complicated Love-wave arrivals in traces are skeletonized as simpler data, namely, the picked dispersion curves in the [Formula: see text] domain. Numerical solutions to the SH-wave equation and an iterative optimization method are then used to invert these dispersion curves for the S-wave velocity model. This procedure, denoted as wave-equation dispersion inversion of Love waves (LWD), does not require the assumption of a layered model or smooth velocity variations, and it is less prone to the cycle-skipping problems of full-waveform inversion. We demonstrate with synthetic and field data examples that LWD can accurately reconstruct the S-wave velocity distribution in a laterally heterogeneous medium. Compared with Rayleigh waves, inversion of the Love-wave dispersion curves empirically exhibits better convergence properties because they are completely insensitive to the P-velocity variations. In addition, Love-wave dispersion curves for our examples are simpler than those for Rayleigh waves, and they are easier to pick in our field data with a low signal-to-noise ratio.

Joint inversion of Rayleigh and Love wave dispersion curves for improving the accuracy of near-surface S-wave velocities

2020 ◽  
Vol 176 ◽  
pp. 103939
Author(s):  
Xiaofei Yin ◽  
Hongrui Xu ◽  
Binbin Mi ◽  
Xiaohan Hao ◽  
Peng Wang ◽  
...  
Keyword(s):  
Love Wave ◽  
Joint Inversion ◽  
Wave Dispersion ◽  
Dispersion Curves ◽  
S Wave ◽  
Near Surface ◽  
Wave Velocities ◽  
Love Wave Dispersion

A hierarchical elastic full-waveform inversion scheme based on wavefield separation and the multistep-length approach

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. R99-R123 ◽  
Author(s):  
Zhiming Ren ◽  
Yang Liu
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
P Wave ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Wave Velocities ◽  
Full Waveform ◽  
Wavefield Separation ◽  
P Wave Velocity

Elastic full-waveform inversion (FWI) updates model parameters by minimizing the residuals of the P- and S-wavefields, resulting in more local minima and serious nonlinearity. In addition, the coupling of different parameters degrades the inversion results. To address these problems, we have developed a hierarchical elastic FWI scheme based on wavefield separation and a multistep-length gradient approach. First, we have derived the gradients expressed by different wave modes; analyzed the crosstalk between various parameters; and evaluated the sensitivity of separated P-wave, separated S-wave, and P- and S-wave misfit functions. Then, a practical four-stage inversion workflow was developed. In the first stage, conventional FWI is used to achieve rough estimates of the P- and S-wave velocities. In the second stage, we only invert the P-wave velocity applying the separated P-wavefields when strong S-wave energy is involved, or we merely update the S-wave velocity by matching the separated S-wavefields for the weak S-wave case. The PP and PS gradient formulas are used in these two cases, respectively. Therefore, the nonlinearity of inversion and the crosstalk between parameters are greatly reduced. In the third stage, the multistep-length gradient scheme is adopted. The density structure can be improved owing to the use of individual step lengths for different parameters. In the fourth stage, we make minor adjustments to the recovered P- and S-wave velocities and density by implementing conventional FWI again. Synthetic examples have determined that our hierarchical FWI scheme with the aforementioned steps obtains more plausible models than the conventional method. Inversion results of each stage and any three stages reveal that wavefield decomposition and the multistep-length approach are helpful to improve the accuracy of velocities and density, respectively, and all the stages of our hierarchical FWI method are necessary to give a good recovery of P- and S-wave velocities and density.

Retrieval of shallow S-wave profiles from seismic reflection surveying and traffic-induced noise

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. EN105-EN117
Author(s):  
Kai Zhang ◽  
Hongyi Li ◽  
Xiaojiang Wang ◽  
Kai Wang
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Rayleigh Waves ◽  
Seismic Reflection ◽  
Seismic Energy ◽  
Dispersion Curves ◽  
S Wave ◽  
Near Surface ◽  
Wave Velocities

In urban subsurface exploration, seismic surveys are mostly conducted along roads where seismic vibrators can be extensively used to generate strong seismic energy due to economic and environmental constraints. Generally, Rayleigh waves also are excited by the compressional wave profiling process. Shear-wave (S-wave) velocities can be inferred using Rayleigh waves to complement near-surface characterization. Most vibrators cannot excite seismic energy at lower frequencies (<5 Hz) to map greater depths during surface-wave analysis in areas with low S-wave velocities, but low-frequency surface waves ([Formula: see text]) can be extracted from traffic-induced noise, which can be easily obtained at marginal additional cost. We have implemented synthetic tests to evaluate the velocity deviation caused by offline sources, finding a reasonably small relative bias of surface-wave dispersion curves due to vehicle sources on roads. Using a 2D reflection survey and traffic-induced noise from the central North China Plain, we apply seismic interferometry to a series of 10.0 s segments of passive data. Then, each segment is selectively stacked on the acausal-to-causal ratio of the mean signal-to-noise ratio to generate virtual shot gathers with better dispersion energy images. We next use the dispersion curves derived by combining controlled source surveying with vehicle noise to retrieve the shallow S-wave velocity structure. A maximum exploration depth of 90 m is achieved, and the inverted S-wave profile and interval S-wave velocity model obtained from reflection processing appear consistent. The data set demonstrates that using surface waves derived from seismic reflection surveying and traffic-induced noise provides an efficient supplementary technique for delineating shallow structures in areas featuring thick Quaternary overburden. Additionally, the field test indicates that traffic noise can be created using vehicles or vibrators to capture surface waves within a reliable frequency band of 2–25 Hz if no vehicles are moving along the survey line.

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