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.

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.

scholarly journals NOISE ATTENUATION IN SEISMIC REFLECTION DATA COMBINING EMPIRICAL MODE DECOMPOSITION AND SVD FILTERING

2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Felipe Da Mota Alves ◽  
Milton J. Porsani
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Reflection ◽  
Seismic Noise ◽  
Singular Value ◽  
Noise Attenuation ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Mode Decomposition ◽  
General Frequency ◽  
Value Decomposition

ABSTRACT. Noises are common events in seismic reflection that have very striking features in the seismograms, hindering the data processing and interpretation. The attenuation of seismic noise is a challenge, in general frequency filters are employed, but they often do not show good results. The characteristic of noise...Keywords: seismic noise, Empirical Mode Decomposition, Singular Value Decomposition. RESUMO. Ruídos são eventos comuns na sísmica de reflexão que possuem características bem marcantes nos sismogramas, atrapalhando o processamento e interpretação dos dados. A atenuação de ruídos sísmicos é um desafio, em geral são utilizados filtros de...Palavras-chave: ruídos sísmicos, Decomposição em Modos Empíricos, Decomposição em Valores Singulares.

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.

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>

The style of transverse faulting in the Dead Sea basin from seismic reflection data: The Amazyahu fault

2006 ◽  
Vol 55 (3) ◽  
pp. 129-139 ◽  
Author(s):  
Avihu Ginzburg ◽  
Moshe Reshef ◽  
Zvi Ben-Avraham ◽  
Uri Schattner
Keyword(s):  
Seismic Reflection ◽  
Dead Sea ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Dead Sea Basin ◽  
The Dead
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