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.

Nonstationary local time-frequency transform

Geophysics ◽  
2021 ◽  
Vol 86 (3) ◽  
pp. V245-V254
Author(s):  
Yangkang Chen
Keyword(s):  
Local Time ◽  
Seismic Data ◽  
Low Frequency ◽  
Data Sets ◽  
Frequency Spectra ◽  
Time Frequency ◽  
Data Registration ◽  
Frequency Decomposition ◽  
Temporal Smoothing ◽  
The Time Domain

Time-frequency analysis is a fundamental approach to many seismic problems. Time-frequency decomposition transforms input seismic data from the time domain to the time-frequency domain, offering a new dimension to probe the hidden information inside the data. Considering the nonstationary nature of seismic data, time-frequency spectra can be obtained by applying a local time-frequency transform (LTFT) method that matches the input data by fitting the Fourier basis with nonstationary Fourier coefficients in the shaping regularization framework. The key part of LTFT is the temporal smoother with a fixed smoothing radius that guarantees the stability of the nonstationary least-squares fitting. We have developed a new LTFT method to handle the nonstationarity in all time, frequency, and space ( x and y) directions of the input seismic data by extending fixed-radius temporal smoothing to nonstationary smoothing with a variable radius in all physical dimensions. The resulting time-frequency transform is referred to as the nonstationary LTFT method, which could significantly increase the resolution and antinoise ability of time-frequency transformation. There are two meanings of nonstationarity, i.e., coping with the nonstationarity in the data by LTFT and dealing with the nonstationarity in the model by nonstationary smoothing. We evaluate the performance of our nonstationary LTFT method in several standard seismic applications via synthetic and field data sets, e.g., arrival picking, quality factor estimation, low-frequency shadow detection, channel detection, and multicomponent data registration, and we benchmark the results with the traditional stationary LTFT method.

COLOR SONAGRAMS: A NEW DIMENSION IN SEISMIC DATA INTERPRETATION

Geophysics ◽  
1971 ◽  
Vol 36 (6) ◽  
pp. 1074-1098 ◽  
Author(s):  
A. H. Balch
Keyword(s):  
Seismic Data ◽  
Computer Graphic ◽  
Data Interpretation ◽  
Seismic Events ◽  
Time Varying ◽  
Light Spectrum ◽  
Seismic Section ◽  
Frequency Spectra ◽  
Low Frequencies ◽  
High Frequencies

We have developed a computer‐graphic‐photographic system which uses color mimicry to display the frequency spectra of seismic events simultaneously with their time‐varying waveforms. Mimicking the visible light spectrum, we have used red for the low frequencies and violet for the highs. The output of our system is a variable‐area‐wiggle‐trace seismic cross‐section. The waveforms are the same as those on a conventional section; however, the variable‐area part of the section appears in color. The color represents the frequency spectrum of the wavelets. Lateral changes in rock attenuation show up as color shifts on this type of display. Faults often stand out as interrupted color bands. Fault diffractions sometimes have a characteristic color signature. The cancellation of high frequencies due to misalignment of events on constant‐velocity stacks can show up in color. Loss of high frequencies due to slight lateral changes in moveout velocity, and consequent trace misalignment, is often indicated by a shift toward red on a color seismic section.

Offset panel

Geophysics ◽  
1984 ◽  
Vol 49 (8) ◽  
pp. 1140-1152 ◽  
Author(s):  
Thomas K. Fulton ◽  
K. Michele Darr
Keyword(s):  
Seismic Data ◽  
Single Channel ◽  
Seismic Source ◽  
Data Interpretation ◽  
High Amplitude ◽  
Processing Parameters ◽  
Near Surface ◽  
Geometric Patterns ◽  
Field Recording ◽  
Common Depth Point

The offset panel is a display of basic seismic data which combines single‐channel profiles from successive offsets (source‐to‐receiver distances) into one format. The profiles are displayed one below another and arranged vertically by offset and horizontally by common‐depth‐point. This arrangement allows for comparison of variations observed at one offset to those at another offset. Alteration of the data due to near‐surface geologic variations generates geometric patterns on the display which are different from patterns due to changes in seismic source or receiver. This display has utility in data processing to verify field recording geometry, monitor the seismic source (primarily a marine application), and determine processing parameters. It aids data interpretation by allowing for the detection of an anomalous velocity zone in the near‐surface which may affect deeper structural interpretation. Utilization of the offset panel in identification of shallow events of high amplitude also allows identification of shallow drilling hazards in the marine environment with conventional seismic data.

scholarly journals Crosshole seismic tomography with cross-firing geometry

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. R139-R146 ◽  
Author(s):  
Ying Rao ◽  
Yanghua Wang ◽  
Shumin Chen ◽  
Jianmin Wang
Keyword(s):  
Seismic Tomography ◽  
Seismic Data ◽  
Seismic Anisotropy ◽  
Joint Inversion ◽  
Transverse Isotropy ◽  
Thin Layers ◽  
Horizontal Velocity ◽  
Data Sets ◽  
Anisotropic Parameter ◽  
Traveltime Tomography

We have developed a case study of crosshole seismic tomography with a cross-firing geometry in which seismic sources were placed in two vertical boreholes alternatingly and receiver arrays were placed in another vertical borehole. There are two crosshole seismic data sets in a conventional sense. These two data sets are used jointly in seismic tomography. Because the local sediment is dominated by periodic, flat, thin layers, there is seismic anisotropy with different velocities in the vertical and horizontal directions. The vertical transverse isotropy anisotropic effect is taken into account in inversion processing, which consists of three stages in sequence. First, isotropic traveltime tomography is used for estimating the maximum horizontal velocity. Then, anisotropic traveltime tomography is used to invert for the anisotropic parameter, which is the normalized difference between the maximum horizontal velocity and the maximum vertical velocity. Finally, anisotropic waveform tomography is implemented to refine the maximum horizontal velocity. The cross-firing acquisition geometry significantly improves the ray coverage and results in a relatively even distribution of the ray density in the study area between two boreholes. Consequently, joint inversion of two crosshole seismic data sets improves the resolution and increases the reliability of the velocity model reconstructed by tomography.

Data-driven edge detectors for seismic data interpretation

Geophysics ◽  
2021 ◽  
pp. 1-41
Author(s):  
Julián L. Gómez ◽  
Lucía E. N. Gelis ◽  
Danilo R. Velis
Keyword(s):  
Seismic Data ◽  
Structural Information ◽  
Data Interpretation ◽  
Data Driven ◽  
Data Sets ◽  
3D Data ◽  
Edge Detectors ◽  
Novel Method ◽  
2D And 3D ◽  
Structure Enhancement

We present a novel method to assist in seismic interpretation. The algorithm learns data-driven edge-detectors for structure enhancement when applied to time slices of 3D poststack seismic data. We obtain the operators by distilling the local and structural information retrieved from patches taken randomly from the input time slices. The filters conform to an orthogonal family that behaves as structure-aware Sobel-like edge detectors, and the user can set their size and number. The results from marine Canada and New Zealand 3D seismic data demonstrate that the proposed algorithm allows the semblance attribute to improve the delineation of the subsurface channels. This fact is further supported by testing the method with realistic synthetic 2D and 3D data sets containing channeling and meandering systems. We contrast the results with standard plain Sobel filtering, multidirectional Sobel filters of variable size, and the dip-oriented plane-wave destruction Sobel attribute. The proposed method gives results that are comparable or superior to those of Sobel-based approaches. In addition, the obtained filters can adapt to the geological structures present in each time slice, which reduces the number of unwanted artifacts in the final product.

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.

scholarly journals Integrated magnetotelluric and seismic investigation of Cenozoic graben structure near Obrzycko, Poland

2021 ◽  
Author(s):  
Adam Cygal ◽  
Michał Stefaniuk ◽  
Anna Kret
Keyword(s):  
Seismic Data ◽  
Geophysical Methods ◽  
Geological Structure ◽  
Data Sets ◽  
Low Velocity ◽  
Shallow Subsurface ◽  
Geophysical Model ◽  
Novel Approach ◽  
Loose Deposits ◽  
Static Corrections

AbstractThis article presents the results of an integrated interpretation of measurements made using Audio-Magnetotellurics and Seismic Reflection geophysical methods. The obtained results were used to build an integrated geophysical model of shallow subsurface cover consisting of Cenozoic deposits, which then formed the basis for a detailed lithological and tectonic interpretation of deeper Mesozoic sediments. Such shallow covers, consisting mainly of glacial Pleistocene deposits, are typical for central and northern Poland. This investigation concentrated on delineating the accurate geometry of Obrzycko Cenozoic graben structure filled with loose deposits, as it was of great importance to the acquisition, processing and interpretation of seismic data that was to reveal the tectonic structure of the Cretaceous and Jurassic sediments which underly the study area. Previously, some problems with estimation of seismic static corrections over similar grabens filled with more recent, low-velocity deposits were encountered. Therefore, a novel approach to estimating the exact thickness of such shallow cover consisting of low-velocity deposits was applied in the presented investigation. The study shows that some alternative geophysical data sets (such as magnetotellurics) can be used to significantly improve the imaging of geological structure in areas where seismic data are very distorted or too noisy to be used alone

scholarly journals A Machine Learning-Based Seismic Data Compression and Interpretation Using a Novel Shifted-Matrix Decomposition Algorithm

2021 ◽  
Vol 11 (11) ◽  
pp. 4874
Author(s):  
Milan Brankovic ◽  
Eduardo Gildin ◽  
Richard L. Gibson ◽  
Mark E. Everett
Keyword(s):  
Real Time ◽  
Seismic Data ◽  
Matrix Decomposition ◽  
Original Data ◽  
Velocity Estimation ◽  
Singular Vectors ◽  
Well Stimulation ◽  
Marine Seismic ◽  
Value Decomposition ◽  
Seismic Data Compression

Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseimic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.

scholarly journals Comparing global tomography-derived and gravity-based upper mantle density models

2020 ◽  
Vol 221 (3) ◽  
pp. 1542-1554 ◽  
Author(s):  
B C Root
Keyword(s):  
Seismic Tomography ◽  
Upper Mantle ◽  
Seismic Data ◽  
Gravity Data ◽  
Heterogeneous Data ◽  
Clear Correlation ◽  
Data Sets ◽  
Satellite Gravity ◽  
Similar Density ◽  
Similar Peak

SUMMARY Current seismic tomography models show a complex environment underneath the crust, corroborated by high-precision satellite gravity observations. Both data sets are used to independently explore the density structure of the upper mantle. However, combining these two data sets proves to be challenging. The gravity-data has an inherent insensitivity in the radial direction and seismic tomography has a heterogeneous data acquisition, resulting in smoothed tomography models with de-correlation between different models for the mid-to-small wavelength features. Therefore, this study aims to assess and quantify the effect of regularization on a seismic tomography model by exploiting the high lateral sensitivity of gravity data. Seismic tomography models, SL2013sv, SAVANI, SMEAN2 and S40RTS are compared to a gravity-based density model of the upper mantle. In order to obtain similar density solutions compared to the seismic-derived models, the gravity-based model needs to be smoothed with a Gaussian filter. Different smoothening characteristics are observed for the variety of seismic tomography models, relating to the regularization approach in the inversions. Various S40RTS models with similar seismic data but different regularization settings show that the smoothening effect is stronger with increasing regularization. The type of regularization has a dominant effect on the final tomography solution. To reduce the effect of regularization on the tomography models, an enhancement procedure is proposed. This enhancement should be performed within the spectral domain of the actual resolution of the seismic tomography model. The enhanced seismic tomography models show improved spatial correlation with each other and with the gravity-based model. The variation of the density anomalies have similar peak-to-peak magnitudes and clear correlation to geological structures. The resolvement of the spectral misalignment between tomographic models and gravity-based solutions is the first step in the improvement of multidata inversion studies of the upper mantle and benefit from the advantages in both data sets.

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