Simulated annealing statics computation using an order‐based energy function

Geophysics ◽  
1991 ◽  
Vol 56 (11) ◽  
pp. 1831-1839 ◽  
Author(s):  
Kris Vasudevan ◽  
William G. Wilson ◽  
William G. Laidlaw
Keyword(s):  
Simulated Annealing ◽  
Seismic Data ◽  
Input Data ◽  
Real Data ◽  
Optimization Criterion ◽  
Optimization Techniques ◽  
Data Sets ◽  
Common Depth Point ◽  
Optimization Function ◽  
Initial Results

The residual statics problem in seismic data analysis is treated by introducing an optimization function that emphasizes the coherence of neighboring common depth point (CDP) gathers within a nonlinear simulated annealing technique. This optimization criterion contrasts with stack power optimization which only considers the coherence between traces within a single CDP. Emphasizing coherence between CDPs removes many of the phase space degeneracies that result from stack‐power based optimization techniques. We have applied the method to both synthetic and real data sets, and initial results display significant improvement over the input data in the coherence of reflections, even in structurally complex areas.

Relative time seislet transform

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V223-V232 ◽  
Author(s):  
Zhicheng Geng ◽  
Xinming Wu ◽  
Sergey Fomel ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Computational Cost ◽  
Lifting Scheme ◽  
Real Data ◽  
Data Sets ◽  
Relative Time ◽  
New Approach ◽  
New Formulation ◽  
Wavelet Lifting ◽  
Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

Denoising seismic data using the nonlocal means algorithm

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. A5-A8 ◽  
Author(s):  
David Bonar ◽  
Mauricio Sacchi
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Seismic Energy ◽  
Real Data ◽  
Spatial Proximity ◽  
Data Sets ◽  
Noise Attenuation ◽  
Nonlocal Means ◽  
Reflection Seismic ◽  
Generally True

The nonlocal means algorithm is a noise attenuation filter that was originally developed for the purposes of image denoising. This algorithm denoises each sample or pixel within an image by utilizing other similar samples or pixels regardless of their spatial proximity, making the process nonlocal. Such a technique places no assumptions on the data except that structures within the data contain a degree of redundancy. Because this is generally true for reflection seismic data, we propose to adopt the nonlocal means algorithm to attenuate random noise in seismic data. Tests with synthetic and real data sets demonstrate that the nonlocal means algorithm does not smear seismic energy across sharp discontinuities or curved events when compared to seismic denoising methods such as f-x deconvolution.

Diffraction images and their attributes based on asymmetric summation: real data application

2020 ◽  
Author(s):  
Maxim I. Protasov ◽  
◽  
Vladimir A. Tcheverda ◽  
Valery V. Shilikov ◽  
◽  
...  
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Data Application ◽  
Diffraction Imaging

The paper deals with a 3D diffraction imaging with the subsequent diffraction attribute calculation. The imaging is based on an asymmetric summation of seismic data and provides three diffraction attributes: structural diffraction attribute, point diffraction attribute, an azimuth of structural diffraction. These attributes provide differentiating fractured and cavernous objects and to determine the fractures orientations. Approbation of the approach was provided on several real data sets.

Accurate poststack acoustic-impedance inversion by well-log calibration

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. R59-R67 ◽  
Author(s):  
Igor B. Morozov ◽  
Jinfeng Ma
Keyword(s):  
Seismic Data ◽  
Acoustic Impedance ◽  
Joint Inversion ◽  
Low Frequency ◽  
Real Data ◽  
Data Sets ◽  
Frequency Model ◽  
Physical Constraints ◽  
Impedance Inversion ◽  
Reliable Solution

The seismic-impedance inversion problem is underconstrained inherently and does not allow the use of rigorous joint inversion. In the absence of a true inverse, a reliable solution free from subjective parameters can be obtained by defining a set of physical constraints that should be satisfied by the resulting images. A method for constructing synthetic logs is proposed that explicitly and accurately satisfies (1) the convolutional equation, (2) time-depth constraints of the seismic data, (3) a background low-frequency model from logs or seismic/geologic interpretation, and (4) spectral amplitudes and geostatistical information from spatially interpolated well logs. The resulting synthetic log sections or volumes are interpretable in standard ways. Unlike broadly used joint-inversion algorithms, the method contains no subjectively selected user parameters, utilizes the log data more completely, and assesses intermediate results. The procedure is simple and tolerant to noise, and it leads to higher-resolution images. Separating the seismic and subseismic frequency bands also simplifies data processing for acoustic-impedance (AI) inversion. For example, zero-phase deconvolution and true-amplitude processing of seismic data are not required and are included automatically in this method. The approach is applicable to 2D and 3D data sets and to multiple pre- and poststack seismic attributes. It has been tested on inversions for AI and true-amplitude reflectivity using 2D synthetic and real-data examples.

Adaptive multiple subtraction based on multiband pattern coding

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. V69-V78 ◽  
Author(s):  
Jinlin Liu ◽  
Wenkai Lu
Keyword(s):  
Seismic Data ◽  
Real Data ◽  
Data Sets ◽  
Subtraction Problem ◽  
Frequency Bands ◽  
Main Challenge ◽  
Multiple Elimination ◽  
Pattern Coding

Adaptive multiple subtraction is the key step of surface-related multiple elimination methods. The main challenge of this technique resides in removing multiples without distorting primaries. We have developed a new pattern-based method for adaptive multiple subtraction with the consideration that primaries can be better protected if the multiples are differentiated from the primaries. Different from previously proposed methods, our method casts the adaptive multiple subtraction problem as a pattern coding and decoding process. We set out to learn distinguishable patterns from the predicted multiples before estimating the multiples contained in seismic data. Hence, in our method, we first carried out pattern coding of the predicted multiples to learn the special patterns of the multiples within different frequency bands. This coding process aims at exploiting the key patterns contained in the predicted multiples. The learned patterns are then used to decode (extract) the multiples contained in the seismic data, in which process those patterns that are similar to the learned patterns were identified and extracted. Because the learned patterns are obtained from the predicted multiples only, our method avoids the interferences of primaries in nature and shows an impressive capability for removing multiples without distorting the primaries. Our applications on synthetic and real data sets gave some promising results.

6D phase-difference attributes for wide-azimuth seismic data interpretation

Geophysics ◽  
2020 ◽  
Vol 85 (6) ◽  
pp. IM37-IM49 ◽  
Author(s):  
Sanyi Yuan ◽  
Jinghan Wang ◽  
Tao Liu ◽  
Tao Xie ◽  
Shangxu Wang
Keyword(s):  
Phase Difference ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Data Interpretation ◽  
Data Sets ◽  
Q Value ◽  
Frequency Spectra ◽  
Karst Caves ◽  
Common Depth Point ◽  
Value Decomposition

Phase information of seismic signals is sensitive to subsurface discontinuities. However, 1D phase attributes are not robust when dealing with noisy data. In addition, variations of seismic phase attributes with azimuth are seldom explored. To address these issues, we have developed 6D phase-difference attributes (PDAs) derived from azimuthal phase-frequency spectra. For the seismic volume of a certain azimuth and frequency, we first construct stacked phase traces at each common-depth point along a certain decomposed trending direction. Then, the 6D PDA is extracted by calculating the complex-valued covariance at a 6D phase space. The proposed method enables characterization of the subsurface discontinuities and indicates seismic anisotropy. Moreover, we provide one q-value attribute obtained by singular value decomposition to describe the variation intensity of PDA along different azimuths. Simulated and field wide-azimuth seismic data sets are used to illustrate the performance of the proposed 6D PDA and the derived q-value attribute. The results show that PDA at different frequencies can image various geologic features, including faults, fracture groups, and karst caves. Our field data example shows that PDA is able to discern the connectivity of karst caves using large-azimuth data. In addition, PDA along different azimuths and the q-value attribute provide a measurement of azimuthal variations, as well as the anisotropy. Our 6D PDA method can be used as a potential tool for wide-azimuth seismic data interpretation.

Direct estimation of shear‐wave interval velocities from seismic data

Geophysics ◽  
1985 ◽  
Vol 50 (4) ◽  
pp. 530-538 ◽  
Author(s):  
P. M. Carrion ◽  
S. Hassanzadeh
Keyword(s):  
Shear Wave ◽  
Small Angle ◽  
Seismic Data ◽  
Mode Conversion ◽  
Real Data ◽  
Compressional Wave ◽  
Compressional Waves ◽  
Common Depth Point ◽  
Interval Velocities ◽  
Wave Decomposition

Conventional velocity analysis of seismic data is based on normal moveout of common‐depth‐point (CDP) traveltime curves. Analysis is done in a hyperbolic framework and, therefore, is limited to using the small‐angle reflections only (muted data). Hence, it can estimate the interval velocities of compressional waves only, since mode conversion is negligible when small‐angle arrivals are concerned. We propose a new method which can estimate the interval velocities of compressional and mode‐converted waves separately. The method is based on slant stacking or plane‐wave decomposition (PWD) of the observed data (seismogram), which transforms the data from the conventional T-X domain into the intercept time‐ray parameter domain. Since PWD places most of the compressional energy into the precritical region of the slant‐stacked seismogram, the compressional‐wave interval velocities can be estimated using the “best ellipse” approximation on the assumption that the elliptic array velocity (stacking velocity) is approximately equal to the root‐mean‐square (rms) velocity. Similarly, shear‐wave interval velocities can be estimated by inverting the traveltime curves in the region of the PWD seismogram, where compressional waves decay exponentially (postcritical region). The method is illustrated by examples using synthetic and real data.

Multiple attenuation with 3D high-order high-resolution parabolic Radon transform using lower frequency constraints

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. V317-V328
Author(s):  
Jitao Ma ◽  
Guoyang Xu ◽  
Xiaohong Chen ◽  
Xiaoliu Wang ◽  
Zhenjiang Hao
Keyword(s):  
High Resolution ◽  
Radon Transform ◽  
Seismic Data ◽  
Computational Cost ◽  
Real Data ◽  
Multiple Model ◽  
Multiple Attenuation ◽  
Common Depth Point ◽  
Frequency Constraint ◽  
Parabolic Radon Transform

The parabolic Radon transform is one of the most commonly used multiple attenuation methods in seismic data processing. The 2D Radon transform cannot consider the azimuth effect on seismic data when processing 3D common-depth point gathers; hence, the result of applying this transform is unreliable. Therefore, the 3D Radon transform should be applied. The theory of the 3D Radon transform is first introduced. To address sparse sampling in the crossline direction, a lower frequency constraint is introduced to reduce spatial aliasing and improve the resolution of the Radon transform. An orthogonal polynomial transform, which can fit the amplitude variations in different parabolic directions, is combined with the dealiased 3D high-resolution Radon transform to account for the amplitude variations with offset of seismic data. A multiple model can be estimated with superior accuracy, and improved results can be achieved. Synthetic and real data examples indicate that even though our method comes at a higher computational cost than existing techniques, the developed approach provides better attenuation of multiples for 3D seismic data with amplitude variations.

A new technique for first-arrival picking of refracted seismic data based on digital image segmentation

Geophysics ◽  
2011 ◽  
Vol 76 (5) ◽  
pp. V79-V89 ◽  
Author(s):  
Wail A. Mousa ◽  
Abdullatif A. Al-Shuhail ◽  
Ayman Al-Lehyani
Keyword(s):  
Image Segmentation ◽  
Seismic Data ◽  
Convex Sets ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Real Data ◽  
Color Image Segmentation ◽  
High Signal ◽  
Data Sets ◽  
First Arrival

We introduce a new method for first-arrival picking based on digital color-image segmentation of energy ratios of refracted seismic data. The method uses a new color-image segmentation scheme based on projection onto convex sets (POCS). The POCS requires a reference color for the first break and one iteration to segment the first-break amplitudes from other arrivals. We tested the segmentation method on synthetic seismic data sets with various amounts of additive Gaussian noise. The proposed method gives similar performance to a modified version of Coppens’ method for traces with high signal-to-noise ratio and medium-to-large offsets. Finally, we applied our method and used as well the modified first-arrival picking method based on Coppens’ method to pick the first arrivals on four real data sets, where both were compared to the first breaks that were picked manually and then interpolated. Based on an assessment error of a 20-ms window with respect to manual picks that are interpolated, we find that our method gives comparable performance to Coppens’ method, depending on the data difficulty of picking first arrivals. Therefore, we believe that our proposed method is a good new addition to the existing methods of first-arrival picking.

Three-parameter Radon transform based on shifted hyperbolas

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. V39-V48 ◽  
Author(s):  
Ali Gholami ◽  
Toktam Zand
Keyword(s):  
Radon Transform ◽  
Seismic Data ◽  
High Performance ◽  
Fourier Transforms ◽  
Real Data ◽  
Data Sets ◽  
Radon Transforms ◽  
Slice Theorem ◽  
Ground Roll ◽  
Zero Offset

The focusing power of the conventional hyperbolic Radon transform decreases for long-offset seismic data due to the nonhyperbolic behavior of moveout curves at far offsets. Furthermore, conventional Radon transforms are ineffective for processing data sets containing events of different shapes. The shifted hyperbola is a flexible three-parameter (zero-offset traveltime, slowness, and focusing-depth) function, which is capable of generating linear and hyperbolic shapes and improves the accuracy of the seismic traveltime approximation at far offsets. Radon transform based on shifted hyperbolas thus improves the focus of seismic events in the transform domain. We have developed a new method for effective decomposition of seismic data by using such three-parameter Radon transform. A very fast algorithm is constructed for high-resolution calculations of the new Radon transform using the recently proposed generalized Fourier slice theorem (GFST). The GFST establishes an analytic expression between the [Formula: see text] coefficients of the data and the [Formula: see text] coefficients of its Radon transform, with which a very fast switching between the model and data spaces is possible by means of interpolation procedures and fast Fourier transforms. High performance of the new algorithm is demonstrated on synthetic and real data sets for trace interpolation and linear (ground roll) noise attenuation.

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