scholarly journals Near-surface attenuation estimation using wave-propagation modeling

Geophysics ◽  
2008 ◽  
Vol 73 (6) ◽  
pp. U27-U37 ◽  
Author(s):  
Nizare El Yadari ◽  
Fabian Ernst ◽  
Wim Mulder
Keyword(s):  
Wave Attenuation ◽  
Waveform Inversion ◽  
Real Data ◽  
P Wave ◽  
Surface Model ◽  
Near Surface ◽  
Propagation Modeling ◽  
Static Corrections ◽  
Starting Model ◽  
Wave Propagation Modeling

The effect of the near surface on seismic land data can be so severe that static corrections are insufficient. Full-waveform inversion followed by redatuming may be an alternative, but inversion will work only if the starting model is sufficiently close to the true model. As a first step toward determining a viscoelastic near-surface model, we assume that existing methods can provide a horizontally layered velocity and density model. Because near-surface attenuation is strongest, we propose a method to estimate the P-wave attenuation based on viscoacoustic finite-difference modeling. We compare energy decay along traveltime curves of reflection and refraction events in the modeled and observed seismic data for a range of attenuation parameters. The best match provides an estimate of the attenuation. First, we estimate only the attenuation of the top layer and study the sensitivity to depth and velocity perturbations. Then, we consider multiple layers. We apply the method to synthetic and real data and investigate the effect of source wavelet and topography. The method is robust against depth and velocity perturbations smaller than 10%. The results are sensitive to the source wavelet. Incorporating the surface topography in the computed traveltimes reduces the uncertainty of the attenuation estimates, especially for deeper layers.

FULL-WAVEFORM INVERSION WITH SOURCE AND RECEIVER COUPLING EFFECTS CORRECTION

Geophysics ◽  
2021 ◽  
pp. 1-37
Author(s):  
Xinhai Hu ◽  
Wei Guoqi ◽  
Jianyong Song ◽  
Zhifang Yang ◽  
Minghui Lu ◽  
...  
Keyword(s):  
Waveform Inversion ◽  
Nonlinear Least Squares ◽  
Real Data ◽  
Model Parameters ◽  
Full Waveform Inversion ◽  
Linear Inversion ◽  
Near Surface ◽  
Model Parameter ◽  
Full Waveform ◽  
Coupling Factors

Coupling factors of sources and receivers vary dramatically due to the strong heterogeneity of near surface, which are as important as the model parameters for the inversion success. We propose a full waveform inversion (FWI) scheme that corrects for variable coupling factors while updating the model parameter. A linear inversion is embedded into the scheme to estimate the source and receiver factors and compute the amplitude weights according to the acquisition geometry. After the weights are introduced in the objective function, the inversion falls into the category of separable nonlinear least-squares problems. Hence, we could use the variable projection technique widely used in source estimation problem to invert the model parameter without the knowledge of source and receiver factors. The efficacy of the inversion scheme is demonstrated with two synthetic examples and one real data test.

Full waveform inversion: early start of model building process to resolve the complex near surface model in the Exmouth sub-basin, North West Shelf Australia

2018 ◽  
Vol 58 (2) ◽  
pp. 884
Author(s):  
Lianping Zhang ◽  
Haryo Trihutomo ◽  
Yuelian Gong ◽  
Bee Jik Lim ◽  
Alexander Karvelas
Keyword(s):  
Model Building ◽  
Waveform Inversion ◽  
Velocity Model ◽  
Surface Velocity ◽  
Image Point ◽  
Surface Model ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
North West ◽  
Full Waveform

The Schlumberger Multiclient Exmouth 3D survey was acquired over the Exmouth sub-basin, North West Shelf Australia and covers 12 600 km2. One of the primary objectives of this survey was to produce a wide coverage of high quality imaging with advanced processing technology within an agreed turnaround time. The complexity of the overburden was one of the imaging challenges that impacted the structuration and image quality at the reservoir level. Unlike traditional full-waveform inversion (FWI) workflow, here, FWI was introduced early in the workflow in parallel with acquisition and preprocessing to produce a reliable near surface velocity model from a smooth starting model. FWI derived an accurate and detailed near surface model, which subsequently benefitted the common image point (CIP) tomography model updates through to the deeper intervals. The objective was to complete the FWI model update for the overburden concurrently with the demultiple stages hence reflection time CIP tomography could start with a reasonably good velocity model upon completion of the demultiple process.

Near-surface modeling challenges highlighted in recent workshop

2020 ◽  
Vol 39 (5) ◽  
pp. 354-356
Author(s):  
Abdulaziz Saad ◽  
Moosa Al-Jahdhami
Keyword(s):  
Guided Waves ◽  
Joint Inversion ◽  
Waveform Inversion ◽  
Surface Modeling ◽  
Hydrocarbon Exploration ◽  
Surface Model ◽  
Near Surface ◽  
Velocity Inversion ◽  
Geophysical Imaging ◽  
Near Surface Geophysics

Despite technological and computational advances in geophysical imaging, near-surface geophysics continues to pose significant challenges in modeling and imaging the subsurface. Geoscientists from around the world attended the first and second editions of the SEG/DGS Near-surface Modeling and Imaging Workshop in 2014 and 2016 to address these challenges. A range of near-surface disciplines were represented from academia and industry, covering aspects of engineering and hydrocarbon exploration. The previous workshops explored emerging and underdeveloped techniques, including deep learning (machine learning), nonseismic methods, full-waveform inversion (FWI), and joint inversion. The necessity to further understand guided waves, anisotropy, velocity inversion, and the creation of an inclusive near-surface model was identified. The previous editions led to a greater understanding of the importance of knowledge sharing among various disciplines in modeling and imaging of the near surface.

Quantitative conductivity and permittivity estimation using full-waveform inversion of on-ground GPR data

Geophysics ◽  
2012 ◽  
Vol 77 (6) ◽  
pp. H79-H91 ◽  
Author(s):  
Sebastian Busch ◽  
Jan van der Kruk ◽  
Jutta Bikowski ◽  
Harry Vereecken
Keyword(s):  
Quantitative Estimation ◽  
Waveform Inversion ◽  
Measured Data ◽  
Optimization Approach ◽  
Full Waveform Inversion ◽  
Near Surface ◽  
Full Waveform ◽  
Novel Approach ◽  
Wide Range ◽  
Starting Model

Conventional ray-based techniques for analyzing common-midpoint (CMP) ground-penetrating radar (GPR) data use part of the measured data and simplified approximations of the reality to return qualitative results with limited spatial resolution. Whereas these methods can give reliable values for the permittivity of the subsurface by employing only the phase information, the far-field approximations used to estimate the conductivity of the ground are not valid for near-surface on-ground GPR, such that the estimated conductivity values are not representative for the area of investigation. Full-waveform inversion overcomes these limitations by using an accurate forward modeling and inverts significant parts of the measured data to return reliable quantitative estimates of permittivity and conductivity. Here, we developed a full-waveform inversion scheme that uses a 3D frequency-domain solution of Maxwell’s equations for a horizontally layered subsurface. Although a straightforward full-waveform inversion is relatively independent of the permittivity starting model, inaccuracies in the conductivity starting model result in erroneous effective wavelet amplitudes and therefore in erroneous inversion results, because the conductivity and wavelet amplitudes are coupled. Therefore, the permittivity and conductivity are updated together with the phase and the amplitude of the source wavelet with a gradient-free optimization approach. This novel full-waveform inversion is applied to synthetic and measured CMP data. In the case of synthetic single layered and waveguide data, where the starting model differs significantly from the true model parameter, we were able to reconstruct the obtained model properties and the effective source wavelet. For measured waveguide data, different starting values returned the same wavelet and quantitative permittivities and conductivities. This novel approach enables the quantitative estimation of permittivity and conductivity for the same sensing volume and enables an improved characterization for a wide range of applications.

3‐D tomographic static correction

Geophysics ◽  
2002 ◽  
Vol 67 (4) ◽  
pp. 1275-1285 ◽  
Author(s):  
Xu Chang ◽  
Yike Liu ◽  
Hui Wang ◽  
Fuzhong Li ◽  
Jing Chen
Keyword(s):  
Computation Time ◽  
Matrix Decomposition ◽  
Velocity Model ◽  
Seismic Survey ◽  
Surface Model ◽  
Low Velocity ◽  
Near Surface ◽  
Model Inversion ◽  
Static Corrections ◽  
Refracted Waves

A 3‐D tomographic inversion approach based on a surface‐consistent model for static corrections is presented in this paper. Direct, reflected, and refracted waves are used simultaneously to update the near‐surface model. We analyze the characteristics of the first‐break traveltime in complicated low‐velocity layers. To improve the accuracy for the velocity model, the various first‐break times from direct, reflected, and refracted waves are considered for model inversion. A fractal algorithm which overcomes the error caused by wavelet shape differences is applied to pick first breaks. It also overcomes the leg jump of refractions. The method can pick a large number of first breaks automatically. The raypaths and traveltimes are calculated with a 3‐D ray tracer that does not increase computation time for complicated geological models. Our method can determine the raypath associated with minimum traveltimes regardless of wave mode (direct, refracted, or reflected). We use a least‐squares approach in conjunction with a matrix decomposition to reconstruct a 3‐D velocity model from the actual first‐break times obtained from 3‐D data. Finally, long‐ and short‐wavelength static corrections are calculated concurrently, based on the reconstructed velocity profile. The method can be applied to wide‐line profiles, crooked lines, and 2‐D and 3‐D seismic survey geometries. The results applied to a real 3‐D data example indicate that the 3‐D tomographic static corrections are effective for field data.

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.

Simultaneous determination of time delays and stacking weights in seismic array beamforming

Geophysics ◽  
1995 ◽  
Vol 60 (2) ◽  
pp. 491-502 ◽  
Author(s):  
Weijian Mao ◽  
David Gubbins
Keyword(s):  
Conjugate Gradient ◽  
Time Delays ◽  
Random Noise ◽  
Waveform Inversion ◽  
Real Data ◽  
Seismic Array ◽  
Model Parameters ◽  
Static Corrections ◽  
Array Beamforming ◽  
Value Decomposition

An algorithm for the estimation of time delays and weights in an arbitrary‐single or three‐component seismic array is developed by the use of a linearized waveform inversion technique. This algorithm differs from conventional crosscorrelation methods in its ability to simultaneously obtain time delays and weights by minimizing residuals of all possible waveform fittings, and by its robustness in the presence of high random noise levels and local geological scattering. There are N stations in an array, and for each station, a beam is formed by a weighted linear combination of the remaining (N − 1) seismic traces. The time delays and weights are model parameters to be found by minimizing the sum of N objective functions. Two optimization algorithms for solving the least‐squares problem, singular‐value decomposition and conjugate gradient, are compared, and the conjugate gradient method is found to be satisfactory and faster for large arrays. The algorithm was tested using synthetic array data with high noise, real data from shots in a borehole to a linear array on land, and Ms 6.7 earthquake data recorded with a broadband three‐component array. The success with synthetic and real data shows the algorithm to be useful for seismic data stacking, residual static corrections, and phase picking when the data quality is poor.

Elastic reflection waveform inversion based on the decomposition of sensitivity kernels

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. R235-R250 ◽  
Author(s):  
Zhiming Ren ◽  
Zhenchun Li ◽  
Bingluo Gu
Keyword(s):  
Wave Velocity ◽  
Waveform Inversion ◽  
Velocity Model ◽  
P Wave ◽  
Background Model ◽  
Reverse Time ◽  
S Wave ◽  
Velocity Models ◽  
Time Migration ◽  
Starting Model

Full-waveform inversion (FWI) has the potential to obtain an accurate velocity model. Nevertheless, it depends strongly on the low-frequency data and the initial model. When the starting model is far from the real model, FWI tends to converge to a local minimum. Based on a scale separation of the model (into the background model and reflectivity model), reflection waveform inversion (RWI) can separate out the tomography term in the conventional FWI kernel and invert for the long-wavelength components of the velocity model by smearing the reflected wave residuals along the transmission (or “rabbit-ear”) paths. We have developed a new elastic RWI method to build the P- and S-wave velocity macromodels. Our method exploits a traveltime-based misfit function to highlight the contribution of tomography terms in the sensitivity kernels and a sensitivity kernel decomposition scheme based on the P- and S-wave separation to suppress the high-wavenumber artifacts caused by the crosstalk of different wave modes. Numerical examples reveal that the gradients of the background models become sufficiently smooth owing to the decomposition of sensitivity kernels and the traveltime-based misfit function. We implement our elastic RWI in an alternating way. At each loop, the reflectivity model is generated by elastic least-squares reverse time migration, and then the background model is updated using the separated traveltime kernels. Our RWI method has been successfully applied in synthetic and real reflection seismic data. Inversion results demonstrate that the proposed method can retrieve preferable low-wavenumber components of the P- and S-wave velocity models, which are reliable to serve as a starting model for conventional elastic FWI. Also, our method with a two-stage inversion workflow, first updating the P-wave velocity using the PP kernels and then updating the S-wave velocity using the PS kernels, is feasible and robust even when P- and S-wave velocities have different structures.

The WIM method for refraction statics

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1859-1870 ◽  
Author(s):  
Luigi Zanzi
Keyword(s):  
Real Data ◽  
Linear Operators ◽  
Surface Model ◽  
Near Surface ◽  
Model Driven ◽  
Iterative Model ◽  
Interpolation Problems ◽  
Final Optimization ◽  
Irregular Spacing ◽  
Reciprocal Method

We know that first breaks may be efficiently inverted in the wavenumber‐offset domain to determine a robust estimate of the near‐surface model. This domain is indicated particularly for regularly covered surveys so that data can be Fourier transformed without interpolation problems. However, it usually happens that data are collected with irregular spacing of the source points. In this case the initial interpolation may bias the solution. An efficient method to prevent bias consists of an iterative model‐driven interpolation. The database is decomposed and recomposed iteratively according to a linear approach to fill in the data space with model‐consistent first arrivals. The method is proved to converge to the unbiased solution and will be called the wavenumber iterative modeling (WIM) method. Experiments, both on synthetic and real data, show that convergence occurs after few iterations so that the procedure is still quite efficient. Moreover, the WIM method can be extended easily to automatically detect and remove the mispicks and suppress coherent and incoherent noise. Simple linear operators are also available for the conversion of the solution to the generalized reciprocal method (GRM) if the interpreter intends to guide the final optimization of the model.

Sign in / Sign up

Export Citation Format

Share Document