S-wave velocity inversion and prediction using a deep hybrid neural network

Author(s):  
Jun Wang ◽  
Junxing Cao ◽  
Shuang Zhao ◽  
Qiaomu Qi
Geophysics ◽  
1993 ◽  
Vol 58 (5) ◽  
pp. 713-719 ◽  
Author(s):  
Ghassan I. Al‐Eqabi ◽  
Robert B. Herrmann

The objective of this study is to demonstrate that a laterally varying shallow S‐wave structure, derived from the dispersion of the ground roll, can explain observed lateral variations in the direct S‐wave arrival. The data set consists of multichannel seismic refraction data from a USGS-GSC survey in the state of Maine and the province of Quebec. These data exhibit significant lateral changes in the moveout of the ground‐roll as well as the S‐wave first arrivals. A sequence of surface‐wave processing steps are used to obtain a final laterally varying S‐wave velocity model. These steps include visual examination of the data, stacking, waveform inversion of selected traces, phase velocity adjustment by crosscorrelation, and phase velocity inversion. These models are used to predict the S‐wave first arrivals by using two‐dimensional (2D) ray tracing techniques. Observed and calculated S‐wave arrivals match well over 30 km long data paths, where lateral variations in the S‐wave velocity in the upper 1–2 km are as much as ±8 percent. The modeled correlation between the lateral variations in the ground‐roll and S‐wave arrival demonstrates that a laterally varying structure can be constrained by using surface‐wave data. The application of this technique to data from shorter spreads and shallower depths is discussed.


Geophysics ◽  
2007 ◽  
Vol 72 (5) ◽  
pp. R77-R85 ◽  
Author(s):  
Donghong Pei ◽  
John N. Louie ◽  
Satish K. Pullammanappallil

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.


2013 ◽  
Author(s):  
Deying Zhong ◽  
Kun Yuan ◽  
Xinxin Fang ◽  
Jun Li ◽  
Mark Mo

2019 ◽  
Author(s):  
Yang Jun ◽  
Yang Huidong ◽  
Chai Wei ◽  
Luo Wenshan ◽  
Ning Bin ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document