To: “Combining full wavefield migration and full waveform inversion, a glance into the future of seismic imaging,” A. J. Berkhout, Geophysics, 77, no. 2, S43–S50.

Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. Y3-Y3
Keyword(s):  
Seismic Imaging ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
The Future

Full-waveform inversion for full-wavefield imaging: Decades in the making

2021 ◽  
Vol 40 (5) ◽  
pp. 324-334
Author(s):  
Rongxin Huang ◽  
Zhigang Zhang ◽  
Zedong Wu ◽  
Zhiyuan Wei ◽  
Jiawei Mei ◽  
...  
Keyword(s):  
Model Building ◽  
Seismic Imaging ◽  
Structural Information ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Data Sets ◽  
Full Waveform Inversion ◽  
Data Types ◽  
Velocity Models ◽  
Full Waveform

Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.

Near-surface seismic imaging of tunnels using 3D elastic full-waveform inversion

2017 ◽  
Author(s):  
James Smith ◽  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Jeroen Tromp ◽  
Harley Cudney ◽  
...  
Keyword(s):  
Seismic Imaging ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform

scholarly journals Full waveform inversion in time and frequency domain of velocity modeling in seismic imaging: FWISIMAT a Matlab code

2018 ◽  
Vol 22 (4) ◽  
pp. 291-300
Author(s):  
Sagar Singh ◽  
Ali Ismet Kanli ◽  
Sagarika Mukhopadhyay
Keyword(s):  
Frequency Domain ◽  
Seismic Imaging ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Initial Model ◽  
Final Model ◽  
Full Waveform ◽  
True Model ◽  
Starting Model

This paper investigates the capability of acoustic Full Waveform Inversion (FWI) in building Marmousi velocity model, in time and frequency domain. FWI is an iterative minimization of misfit between observed and calculated data which is generally solved in three segments: forward modeling, which numerically solves the wave equation with an initial model, gradient computation of the objective function, and updating the model parameters, with a valid optimization method. FWI codes developed in MATLAB herein FWISIMAT (Full Waveform Inversion in Seismic Imaging using MATLAB) are successfully implemented using the Marmousi velocity model as the true model. An initial model is obtained by smoothing the true model to initiate FWI procedure. Smoothing ensures an adequate starting model for FWI, as the FWI procedure is known to be sensitive on the starting model. The final model is compared with the true model to review the number of recovered velocities. FWI codes developed in MATLAB herein FWISIMAT (Full Waveform Inversion in Seismic Imaging using MATLAB) are successfully implemented usingMarmousi velocity model astrue model. An initial model is derived from smoothing the true model to initiate FWI procedure. Smoothing ensures an adequate starting model for FWI, as the FWI procedure is known to be sensitive onstarting model. The final model is compared with the true model to review theamount of recovered velocities. 

Time-lapse seismic imaging using regularized full-waveform inversion with a prior model: which strategy?

2014 ◽  
Vol 63 (1) ◽  
pp. 78-98 ◽  
Author(s):  
A. Asnaashari ◽  
R. Brossier ◽  
S. Garambois ◽  
F. Audebert ◽  
P. Thore ◽  
...  
Keyword(s):  
Seismic Imaging ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Prior Model ◽  
Full Waveform
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