Shear-wave seismic reflection processing - the importance of velocity analysis

2021 ◽  
Author(s):  
Barbara Dietiker ◽  
André J.-M. Pugin ◽  
Matthew P. Griffiths ◽  
Kevin Brewer ◽  
Timothy Cartwright
Keyword(s):  
Shear Wave ◽  
Seismic Reflection ◽  
Velocity Analysis ◽  
P Waves ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Wave Velocities ◽  
Reflection Data ◽  
Shear Wave Velocities ◽  
Local Shift

<p>Based on our experience, one of the most important steps in processing shear-wave seismic reflection data is the velocity analysis. In unconsolidated materials a very fine velocity analysis is more essential for S-waves than for P-waves because shear-wave velocities vary over several orders of magnitude and can change very quickly laterally and with depth. Velocities between 100m/s in glaciolacustine/marine deposits (clay-sized silts) and 1200m/s in stiff diamicton (till) were encountered in recent surveys. Shear-wave velocities have the large advantage of not being changed by the phase of the pore content such as the groundwater table.</p><p>We present two fundamentally different methods for velocity determination: 1) velocity semblance analysis based on hyperbolic reflection move-out on common midpoint (cmp) gathers and 2) Local Phase – Local Shift (LPLS) method which automatically estimates the reflection slope (local static shift) in the time-frequency domain of cmp gathers. Published in 2020, the latter method can be used for automated processing and substantially saves processing time.</p><p>Processing steps in preparation for velocity analysis (independent of the chosen method) include frequency filtering, trace equalizing and muting. We show velocity semblance images from different geological settings (glacial, postglacial) and from different shear components and discuss differences. Information gained besides shear velocities include mapped reflectors and located diffractions. Using those examples, we demonstrate how combining all information using visualisation techniques enhances interpretation of such data sets.</p>

Investigation of generalized S-transform analysis windows for time-frequency analysis of seismic reflection data

Geophysics ◽  
2016 ◽  
Vol 81 (3) ◽  
pp. V235-V247 ◽  
Author(s):  
Duan Li ◽  
John Castagna ◽  
Gennady Goloshubin
Keyword(s):  
Temporal Resolution ◽  
Seismic Reflection ◽  
Frequency Resolution ◽  
Low Frequencies ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Gaussian Window ◽  
Reflection Data ◽  
S Transform ◽  
Generalized S Transform

The frequency-dependent width of the Gaussian window function used in the S-transform may not be ideal for all applications. In particular, in seismic reflection prospecting, the temporal resolution of the resulting S-transform time-frequency spectrum at low frequencies may not be sufficient for certain seismic interpretation purposes. A simple parameterization of the generalized S-transform overcomes the drawback of poor temporal resolution at low frequencies inherent in the S-transform, at the necessary expense of reduced frequency resolution. This is accomplished by replacing the frequency variable in the Gaussian window with a linear function containing two coefficients that control resolution variation with frequency. The linear coefficients can be directly calculated by selecting desired temporal resolution at two frequencies. The resulting transform conserves energy and is readily invertible by an inverse Fourier transform. This modification of the S-transform, when applied to synthetic and real seismic data, exhibits improved temporal resolution relative to the S-transform and improved resolution control as compared with other generalized S-transform window functions.

scholarly journals Depth characterization of shallow aquifers with seismic reflection, Part I—The failure of NMO velocity analysis and quantitative error prediction

Geophysics ◽  
2002 ◽  
Vol 67 (1) ◽  
pp. 89-97 ◽  
Author(s):  
John H. Bradford
Keyword(s):  
Layer Thickness ◽  
Water Table ◽  
Seismic Reflection ◽  
Negative Impact ◽  
Compressional Wave ◽  
Unconsolidated Sediments ◽  
Velocity Analysis ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Saturated Layer

As seismic reflection data become more prevalent as input for quantitative environmental and engineering studies, there is a growing need to assess and improve the accuracy of reflection processing methodologies. It is common for compressional‐wave velocities to increase by a factor of four or more where shallow, unconsolidated sediments change from a dry or partially water‐saturated regime to full saturation. While this degree of velocity contrast is rare in conventional seismology, it is a common scenario in shallow environments and leads to significant problems when trying to record and interpret reflections within about the first 30 m below the water table. The problem is compounded in shallow reflection studies where problems primarily associated with surface‐related noise limit the range of offsets we can use to record reflected energy. For offset‐to‐depth ratios typically required to record reflections originating in this zone, the assumptions of NMO velocity analysis are violated, leading to very large errors in depth and layer thickness estimates if the Dix equation is assumed valid. For a broad range of velocity profiles, saturated layer thickness will be overestimated by a minimum of 10% if the boundary of interest is <30 m below the water table. The error increases rapidly as the boundary shallows and can be very large (>100%) if the saturated layer is <10 m thick. This degree of error has a significant and negative impact if quantitative interpretations of aquifer geometry are used in aquifer evaluation such as predictive groundwater flow modeling or total resource estimates.

INTEGRATED TECHNIQUES FOR DETERMINING THE VELOCITY FUNCTION IN THE SEISMIC REFLECTION: EXAMPLE OF LOW SIGNAL-TO-NOISE DATA

2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Rodrigo Francis Revorêdo ◽  
Carlos César Nascimento da Silva
Keyword(s):  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Hydrocarbon Exploration ◽  
Velocity Analysis ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Noise Data ◽  
Quality And Reliability ◽  
Seismic Images

ABSTRACT. In the hydrocarbon exploration activities, the reprocessing of old seismic reflection data, acquired with few channels and with low signal-to-noise ratio, is commonly undertaken to ameliorate the quality and reliability of the seismic images...Keywords: seismic processing, velocity analysis, CVS. RESUMO. É comum na exploração de hidrocarbonetos o reprocessamento de dados sísmicos antigos, por vezes com um baixo número de canais e baixa razão sinal/ruído, com o objetivo de gerar uma imagem de melhor qualidade e confiabilidade quando comparada àquelas já existentes...Palavras-chave: processamento sísmico, análise de velocidades, CVS. 

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

A strategy for nonlinear elastic inversion of seismic reflection data

Geophysics ◽  
1986 ◽  
Vol 51 (10) ◽  
pp. 1893-1903 ◽  
Author(s):  
Albert Tarantola
Keyword(s):  
Wave Velocity ◽  
Seismic Reflection ◽  
Wave Impedance ◽  
P Wave ◽  
S Wave ◽  
Seismic Reflection Data ◽  
S Waves ◽  
Wave Velocities ◽  
Reflection Data ◽  
P Wave Velocity

The problem of interpretation of seismic reflection data can be posed with sufficient generality using the concepts of inverse theory. In its roughest formulation, the inverse problem consists of obtaining the Earth model for which the predicted data best fit the observed data. If an adequate forward model is used, this best model will give the best images of the Earth’s interior. Three parameters are needed for describing a perfectly elastic, isotropic, Earth: the density ρ(x) and the Lamé parameters λ(x) and μ(x), or the density ρ(x) and the P-wave and S-wave velocities α(x) and β(x). The choice of parameters is not neutral, in the sense that although theoretically equivalent, if they are not adequately chosen the numerical algorithms in the inversion can be inefficient. In the long (spatial) wavelengths of the model, adequate parameters are the P-wave and S-wave velocities, while in the short (spatial) wavelengths, P-wave impedance, S-wave impedance, and density are adequate. The problem of inversion of waveforms is highly nonlinear for the long wavelengths of the velocities, while it is reasonably linear for the short wavelengths of the impedances and density. Furthermore, this parameterization defines a highly hierarchical problem: the long wavelengths of the P-wave velocity and short wavelengths of the P-wave impedance are much more important parameters than their counterparts for S-waves (in terms of interpreting observed amplitudes), and the latter are much more important than the density. This suggests solving the general inverse problem (which must involve all the parameters) by first optimizing for the P-wave velocity and impedance, then optimizing for the S-wave velocity and impedance, and finally optimizing for density. The first part of the problem of obtaining the long wavelengths of the P-wave velocity and the short wavelengths of the P-wave impedance is similar to the problem solved by present industrial practice (for accurate data interpretation through velocity analysis and “prestack migration”). In fact, the method proposed here produces (as a byproduct) a generalization to the elastic case of the equations of “prestack acoustic migration.” Once an adequate model of the long wavelengths of the P-wave velocity and of the short wavelengths of the P-wave impedance has been obtained, the data residuals should essentially contain information on S-waves (essentially P-S and S-P converted waves). Once the corresponding model of S-wave velocity (long wavelengths) and S-wave impedance (short wavelengths) has been obtained, and if the remaining residuals still contain information, an optimization for density should be performed (the short wavelengths of impedances do not give independent information on density and velocity independently). Because the problem is nonlinear, the whole process should be iterated to convergence; however, the information from each parameter should be independent enough for an interesting first solution.

Estimation of seismic quality factor in the time-frequency domain using variational mode decomposition

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. V329-V343
Author(s):  
Ya-Juan Xue ◽  
Jun-Xing Cao ◽  
Xing-Jian Wang ◽  
Hao-Kun Du
Keyword(s):  
Quality Factor ◽  
Seismic Reflection ◽  
Seismic Attenuation ◽  
Reflection Coefficients ◽  
Variational Mode Decomposition ◽  
Seismic Reflection Data ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
Seismic Quality Factor

Seismic attenuation as represented by the seismic quality factor [Formula: see text] has a substantial impact on seismic reflection data. To effectively eliminate the interference of reflection coefficients for [Formula: see text] estimation, a new method is proposed based on the stationary convolutional model of a seismic trace using variational mode decomposition (VMD). VMD is conducted on the logarithmic spectra extracted from the time-frequency distribution of the seismic reflection data generated from the generalized S transform. For the intrinsic mode functions after VMD, mutual information and correlation analysis are used to reconstruct the signals, which effectively eliminates the influence of the reflection coefficients. The difference between the two reconstructed logarithmic spectra within the selected frequency band produces a better linear property, and it is more suitably approximated with the linear function compared to the conventional spectral-ratio method. Least-squares fitting is finally applied for [Formula: see text] estimation. Application of this method to synthetic and real data examples demonstrates the stabilization and accuracy for [Formula: see text] estimation.

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