3D polarimetric GPR coherency attributes and full-waveform inversion of transmission data for characterizing fractured rock

Geophysics ◽  
2009 ◽  
Vol 74 (3) ◽  
pp. J23-J34 ◽  
Author(s):  
Douglas S. Sassen ◽  
Mark E. Everett
Keyword(s):  
Time Domain ◽  
Field Data ◽  
Fractured Rock ◽  
Waveform Inversion ◽  
Fracture Aperture ◽  
Destructive Interference ◽  
Full Waveform Inversion ◽  
Wave Shape ◽  
Full Waveform ◽  
Transmission Data

Ground-penetrating radar (GPR) can detect and describe fractures to help us characterize fractured rock formations. A fracture alters the incident waveform, or wave shape, of a GPR signal through constructive and destructive interference, depending on the aperture, fill, and orientation of the fracture. Because the electromagnetic (EM) waves of GPR are vectorial, features exhibiting strong directionality can change the state of polarization of the incident field. GPR methods that focus on changes in waveform or polarization can improve detection and discrimination of fractures within rock bodies. An algorithm based on coherency, a seismic attribute that delineates discontinuities in wavelet shape, is developed for polarimetric GPR. It uses the largest eigenvalue of the time-domain scattering matrix when calculating coherence. This algorithm is sensitive to wave shape and is unbiased by the polarization of GPR antennas. Polarimetric coherency works better than scalar coherency in removing the effects of polarization on field data collected from a fractured limestone plot used for hydrologic experimentation. Another method, for time-domain full-waveform inversion of transmission data, quantitatively determines fracture aperture and EM properties of fill, based on a thin-layer model. Inversion results from field data show consistency with the location of fractures from reflection data. These two methods offer better fracture-detection capability and quantitative information on fracture aperture, dielectric permittivity, and electrical conductivity of the fill than traditional GPR imaging and scalar-coherency attributes.

scholarly journals Receiver-extension strategy for time-domain full waveform inversion using a relocalization approach

Geophysics ◽  
2021 ◽  
pp. 1-85
Author(s):  
Ludovic Métivier ◽  
Romain Brossier
Keyword(s):  
Time Domain ◽  
Field Data ◽  
Waveform Inversion ◽  
Computational Cost ◽  
Velocity Model ◽  
Full Waveform Inversion ◽  
Geological Environment ◽  
Extension Method ◽  
Velocity Models ◽  
Full Waveform

A receiver-extension strategy is presented as an alternative to recently promoted source-extension strategies, in the framework of high resolution seismic imaging by full waveform inversion. This receiver-extension strategy is directly applicable in time-domain full waveform inversion, and unlike source-extension methods it incurs negligible extra computational cost. After connections between difference source-extension strategies are reviewed, the receiver-extension method is introduced and analyzed for single-arrival data. The method results in a misfit function convex with respect to the velocity model in this context. The method is then applied to three exploration scale synthetic case studies representative of different geological environment, based on: the Marmousi model, the BP 2004 salt model, and the Valhall model. In all three cases the receiver-extension strategy makes it possible to start full waveform inversion with crude initial models, and reconstruct meaningful subsurface velocity models. The good performance of the method even considering inaccurate amplitude prediction due to noise, imperfect modeling, and source wavelet estimation, bodes well for field data applications.

scholarly journals Time domain viscoelastic full waveform inversion

2017 ◽  
Vol 209 (3) ◽  
pp. 1718-1734 ◽  
Author(s):  
Gabriel Fabien-Ouellet ◽  
Erwan Gloaguen ◽  
Bernard Giroux
Keyword(s):  
Time Domain ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
Full Waveform

Multiscale time-domain time-lapse full-waveform inversion with a model-difference regularization

2017 ◽  
Author(s):  
Musa Maharramov ◽  
Ganglin Chen ◽  
Partha S. Routh ◽  
Anatoly I. Baumstein ◽  
Sunwoong Lee ◽  
...  
Keyword(s):  
Time Domain ◽  
Waveform Inversion ◽  
Time Lapse ◽  
Full Waveform Inversion ◽  
Full Waveform

A graphics processing unit implementation of time-domain full-waveform inversion

Geophysics ◽  
2015 ◽  
Vol 80 (3) ◽  
pp. F31-F39 ◽  
Author(s):  
Pengliang Yang ◽  
Jinghuai Gao ◽  
Baoli Wang
Keyword(s):  
Time Domain ◽  
Graphics Processing Unit ◽  
Waveform Inversion ◽  
Processing Unit ◽  
Full Waveform Inversion ◽  
Full Waveform ◽  
Graphics Processing

scholarly journals Normalized nonzero-lag crosscorrelation elastic full-waveform inversion

Geophysics ◽  
2019 ◽  
Vol 84 (1) ◽  
pp. R1-R10 ◽  
Author(s):  
Zhendong Zhang ◽  
Tariq Alkhalifah ◽  
Zedong Wu ◽  
Yike Liu ◽  
Bin He ◽  
...  
Keyword(s):  
Field Data ◽  
Extreme Values ◽  
Time Lag ◽  
Waveform Inversion ◽  
Optimal Time ◽  
Full Waveform Inversion ◽  
Data Set ◽  
Velocity Models ◽  
Full Waveform ◽  
The Earth

Full-waveform inversion (FWI) is an attractive technique due to its ability to build high-resolution velocity models. Conventional amplitude-matching FWI approaches remain challenging because the simplified computational physics used does not fully represent all wave phenomena in the earth. Because the earth is attenuating, a sample-by-sample fitting of the amplitude may not be feasible in practice. We have developed a normalized nonzero-lag crosscorrelataion-based elastic FWI algorithm to maximize the similarity of the calculated and observed data. We use the first-order elastic-wave equation to simulate the propagation of seismic waves in the earth. Our proposed objective function emphasizes the matching of the phases of the events in the calculated and observed data, and thus, it is more immune to inaccuracies in the initial model and the difference between the true and modeled physics. The normalization term can compensate the energy loss in the far offsets because of geometric spreading and avoid a bias in estimation toward extreme values in the observed data. We develop a polynomial-type weighting function and evaluate an approach to determine the optimal time lag. We use a synthetic elastic Marmousi model and the BigSky field data set to verify the effectiveness of the proposed method. To suppress the short-wavelength artifacts in the estimated S-wave velocity and noise in the field data, we apply a Laplacian regularization and a total variation constraint on the synthetic and field data examples, respectively.

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