Efficient 3D velocity modeling building using joint inline and crossline plane-wave wave-equation migration velocity analyses

2020 ◽  
Author(s):  
Xuejian Liu ◽  
Lianjie Huang ◽  
Zongcai Feng ◽  
George El-kaseeh ◽  
Robert Will ◽  
...  
Keyword(s):  
Wave Equation ◽  
Plane Wave ◽  
Model Building ◽  
Velocity Model ◽  
Migration Velocity ◽  
Inversion Method ◽  
Data Interpolation ◽  
Image Domain ◽  
Velocity Model Building ◽  
3D Velocity Model

Summary Wave-equation migration velocity analysis (WEMVA) is an image-domain inversion method for velocity model building. Automatic plane-wave WEMVA (PWEMVA) calculates the moveouts of plane-wave common-image gathers (CIGs) by searching a best-fitting parabola with semblance analysis and back-projects residual CIG moveouts into wavefield wavepaths with a reflection tomographic kernel. However, 3D PWEMVA is very computationally expensive because 3D reflection tomographic inversion requires at least five 3D reverse-time migrations per iteration and stores two types of source wavefields at model boundaries. We develop a joint inline and crossline PWEMVA method for efficient 3D velocity model building. We alternatively implement the inline and crossline PWEMVAs with a constraint for each other, in which we iteratively construct the 3D velocity model update through 1D spline interpolation of 2D gradients. The inline and crossline joint inversion is practical since PWEMVA only inverts for low-wavenumber velocity perturbations along wavepaths, and the method can take less than one per cent of the computational cost of full 3D PWEMVA. To construct unaliased plane-waves for our joint inline and crossline PWEMVA, we develop a 3D data interpolation method in the frequency-wavenumber (FK) domain to recover regularly and randomly missing traces. The method minimizes the misfit on sufficiently localized data subsets with iterative optimal step-lengths and a gradient preconditioner that iteratively selects dominant dips along different azimuths. In numerical experiments, we use a 3D synthetic seismic dataset and a land 3D field seismic dataset acquired at the Farnsworth CO2-EOR [Enhanced Oil Recovery] field to demonstrate the efficacy of our velocity model building and data interpolation methods.

Velocity model building with wave equation migration velocity focusing analysis

2008 ◽  
Author(s):  
Morgan P. Brown ◽  
Joseph H. Higginbotham ◽  
Robert G. Clapp
Keyword(s):  
Wave Equation ◽  
Model Building ◽  
Velocity Model ◽  
Migration Velocity ◽  
Velocity Model Building

scholarly journals Seismic velocity estimation: A deep recurrent neural-network approach

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. U21-U29
Author(s):  
Gabriel Fabien-Ouellet ◽  
Rahul Sarkar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Model Building ◽  
Seismic Velocity ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Interval Velocity ◽  
Velocity Model Building ◽  
3D Velocity Model

Applying deep learning to 3D velocity model building remains a challenge due to the sheer volume of data required to train large-scale artificial neural networks. Moreover, little is known about what types of network architectures are appropriate for such a complex task. To ease the development of a deep-learning approach for seismic velocity estimation, we have evaluated a simplified surrogate problem — the estimation of the root-mean-square (rms) and interval velocity in time from common-midpoint gathers — for 1D layered velocity models. We have developed a deep neural network, whose design was inspired by the information flow found in semblance analysis. The network replaces semblance estimation by a representation built with a deep convolutional neural network, and then it performs velocity estimation automatically with recurrent neural networks. The network is trained with synthetic data to identify primary reflection events, rms velocity, and interval velocity. For a synthetic test set containing 1D layered models, we find that rms and interval velocity are accurately estimated, with an error of less than [Formula: see text] for the rms velocity. We apply the neural network to a real 2D marine survey and obtain accurate rms velocity predictions leading to a coherent stacked section, in addition to an estimation of the interval velocity that reproduces the main structures in the stacked section. Our results provide strong evidence that neural networks can estimate velocity from seismic data and that good performance can be achieved on real data even if the training is based on synthetics. The findings for the 1D problem suggest that deep convolutional encoders and recurrent neural networks are promising components of more complex networks that can perform 2D and 3D velocity model building.

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