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1
Dey, Ayon K., and Larry R. Lines. "Reflectivity randomness revisited." GEOPHYSICS 64, no. 5 (September 1999): 1630–36. http://dx.doi.org/10.1190/1.1444668.
Повний текст джерелаАнотація:
In seismic exploration, statistical wavelet estimation and deconvolution are standard tools. Both of these processes assume randomness in the seismic reflectivity sequence. The validity of this assumption is examined by using well‐log synthetic seismograms and by using a procedure for evaluating the resulting deconvolutions. With real data, we compare our wavelet estimations with the in‐situ recording of the wavelet from a vertical seismic profile (VSP). As a result of our examination of the randomness assumption, we present a fairly simple test that can be used to evaluate the validity of a randomness assumption. From our test of seismic data in Alberta, we conclude that the assumption of reflectivity randomness is less of a problem in deconvolution than other assumptions such as phase and stationarity.
2
Li, Yanqin, and Guoshan Zhang. "A Seismic Blind Deconvolution Algorithm Based on Bayesian Compressive Sensing." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/427153.
Повний текст джерелаАнотація:
Compressive sensing in seismic signal processing is a construction of the unknown reflectivity sequence from the incoherent measurements of the seismic records. Blind seismic deconvolution is the recovery of reflectivity sequence from the seismic records, when the seismic wavelet is unknown. In this paper, a seismic blind deconvolution algorithm based on Bayesian compressive sensing is proposed. The proposed algorithm combines compressive sensing and blind seismic deconvolution to get the reflectivity sequence and the unknown seismic wavelet through the compressive sensing measurements of the seismic records. Hierarchical Bayesian model and optimization method are used to estimate the unknown reflectivity sequence, the seismic wavelet, and the unknown parameters (hyperparameters). The estimated result by the proposed algorithm shows the better agreement with the real value on both simulation and field-data experiments.
3
Dai, Ronghuo, Cheng Yin, and Da Peng. "An Application of Elastic-Net Regularized Linear Inverse Problem in Seismic Data Inversion." Applied Sciences 13, no. 3 (January 24, 2023): 1525. http://dx.doi.org/10.3390/app13031525.
Повний текст джерелаАнотація:
In exploration geophysics, seismic impedance is a physical characteristic parameter of underground formations. It can mark rock characteristics and help stratigraphic analysis. Hence, seismic data inversion for impedance is a key technology in oil and gas reservoir prediction. To invert impedance from seismic data, one can perform reflectivity series inversion first. Then, under a simple exponential integration transformation, the inverted reflectivity series can give the final inverted impedance. The quality of the inverted reflectivity series directly affects the quality of impedance. Sparse-spike inversion is the most common method to obtain reflectivity series with high resolution. It adopts a sparse regularization to impose sparsity on the inverted reflectivity series. However, the high resolution of sparse-spike-like reflectivity series is obtained at the cost of sacrificing small reflectivity. This is the inherent problem of sparse regularization. In fact, the reflectivity series from the actual impedance well log is not strictly sparse. It contains not only the sparse major large reflectivity, but also small reflectivity between major reflectivity. That is to say, the large reflectivity is sparse, but the small reflectivity is dense. To combat this issue, we adopt elastic-net regularization to replace sparse regularization in seismic impedance inversion. The elastic net is a hybrid regularization that combines sparse regularization and dense regularization. The proposed inversion method was performed on a synthetic seismic trace, which is created from an actual well log. Then, a real seismic data profile was used to test the practice application. The inversion results showed that it provides an effective new alternative method to invert impedance.
4
Ursin, Bjørn. "Methods for estimating the seismic reflection response." GEOPHYSICS 62, no. 6 (November 1997): 1990–95. http://dx.doi.org/10.1190/1.1444299.
Повний текст джерелаАнотація:
The convolutional model of the seismic trace consists of a seismic pulse convolved with a reflectivity series plus measurement noise. The seismic deconvolution problem is to estimate the reflectivity series, given the data and an estimate of the seismic pulse. The classical solution to this problem is a weighted least‐squares estimate of the reflectivity series, which is optimal when the noise covariance matrix is known and there are no errors in the pulse. The seismic convolutional model has been reformulated, taking into account errors in the pulse and measurement noise, which is taken to be white noise filtered by a finite‐impulse‐response filter. All variables are assumed to be Gaussian, with known a priori mean values and covariance matrices. The unknown parameters may be the reflectivity series, the noise‐filter coefficients, and the white noise variance or, when the noise covariance matrix is known, just the reflectivity series. This results in maximum a posteriori (MAP) and maximum likelihood (ML) estimates of the reflectivity series that take into account uncertainty in the seismic pulse and colored noise. These estimates generally can be computed by solving a nonlinear minimization problem. The constrained total least‐squares (CTLS) estimate of the reflectivity series is found by minimizing a function that contains one less term than does the function that gives the ML estimate. When there is no uncertainty in the pulse and the noise covariance matrix is known all estimates are linear functions of the data corresponding to weighted least squares. The stabilized least‐squares (SLS) estimate of the reflectivity series is a special case of the MAP estimate with a simple statistical model. The problem of estimating the seismic pulse, given seismic data and an estimate of the reflectivity series, is identical to the problem of estimating the reflectivity series, except that the initial conditions in the convolutional model are slightly different.
5
Chen, Fubin, Zhaoyun Zong, and Man Jiang. "Seismic reflectivity and transmissivity parametrization with the effect of normal in situ stress." Geophysical Journal International 226, no. 3 (May 4, 2021): 1599–614. http://dx.doi.org/10.1093/gji/ggab179.
Повний текст джерелаАнотація:
SUMMARY In situ stress has a significant effect on the properties of underground formations, including seismic wave velocity, porosity and permeability, and further affects seismic reflectivity and transmissivity. Research works on the effect of in situ stress are helpful to construct more precise seismic reflection and transmission coefficient equations. However, previous studies on seismic reflectivity equations did not take the effect of normal in situ stress into consideration. The mechanism of stress on seismic reflectivity and transmissivity is still ambiguous. In this study, we propose new explicit equations to help analyse the changes of seismic reflectivity and transmissivity under the effect of normal in situ stress. First, we deduce the Christoffel equation on the basis of solid acoustoelastic theory. Then, we utilize appropriate boundary conditions to formulate analytical equations of the reflectivity at the interface between two stressed formations, which can provide some new insights into the role of in situ stress. The shear wave birefringence will vanish because we assume that the wave propagates in the X–Z plane. Different rock models with different lithology and saturation are used to analyse the variation of seismic reflectivity and transmissivity with normal stress and incident angle at the interface. The main effect of normal stress on reflection and transmission coefficients is to change amplitude and critical incident angle. When the upper and lower layers are sandstones, the critical incident angle decreases with the increase of normal in situ stress, which is consistent with previous studies. In addition, the reflectivity equation can be degenerated to the Zoeppritz equation when the normal in situ stress vanishes, which further validates that the equation proposed is correct. Seismic reflectivity equations that couple the effect of stress can lay a foundation for direct prediction of in situ stress.
6
Liang, Chen, John Castagna, and Marcelo Benabentos. "Reflectivity decomposition: Theory and application." Interpretation 9, no. 2 (April 21, 2021): B7—B23. http://dx.doi.org/10.1190/int-2020-0203.1.
Повний текст джерелаАнотація:
Sparse reflectivity inversion of processed reflection seismic data is intended to produce reflection coefficients that represent boundaries between geologic layers. However, the objective function for sparse inversion is usually dominated by large reflection coefficients, which may result in unstable inversion for weak events, especially those interfering with strong reflections. We have determined that any seismogram can be decomposed according to the characteristics of the inverted reflection coefficients that can be sorted and subset by magnitude, sign, and sequence, and new seismic traces can be created from only reflection coefficients that pass the sorting criteria. We call this process reflectivity decomposition. For example, original inverted reflection coefficients can be decomposed by magnitude, large ones removed, the remaining reflection coefficients reconvolved with the wavelet, and this residual reinverted, thereby stabilizing inversions for the remaining weak events. As compared with inverting an original seismic trace, subtle impedance variations occurring in the vicinity of nearby strong reflections can be better revealed and characterized when only the events caused by small reflection coefficients are passed and reinverted. When we apply reflectivity decomposition to a 3D seismic data set in the Midland Basin, seismic inversion for weak events is stabilized such that previously obscured porous intervals in the original inversion can be detected and mapped, with a good correlation to the actual well logs.
7
Wang, Lingling, Qian Zhao, Jinghuai Gao, Zongben Xu, Michael Fehler, and Xiudi Jiang. "Seismic sparse-spike deconvolution via Toeplitz-sparse matrix factorization." GEOPHYSICS 81, no. 3 (May 2016): V169—V182. http://dx.doi.org/10.1190/geo2015-0151.1.
Повний текст джерелаАнотація:
We have developed a new sparse-spike deconvolution (SSD) method based on Toeplitz-sparse matrix factorization (TSMF), a bilinear decomposition of a matrix into the product of a Toeplitz matrix and a sparse matrix, to address the problems of lateral continuity, effects of noise, and wavelet estimation error in SSD. Assuming the convolution model, a constant source wavelet, and the sparse reflectivity, a seismic profile can be considered as a matrix that is the product of a Toeplitz wavelet matrix and a sparse reflectivity matrix. Thus, we have developed an algorithm of TSMF to simultaneously deconvolve the seismic matrix into a wavelet matrix and a reflectivity matrix by alternatively solving two inversion subproblems related to the Toeplitz wavelet matrix and sparse reflectivity matrix, respectively. Because the seismic wavelet is usually compact and smooth, the fused Lasso was used to constrain the elements in the Toeplitz wavelet matrix. Moreover, due to the limitations of computer memory, large seismic data sets were divided into blocks, and the average of the source wavelets deconvolved from these blocks via TSMF-based SSD was used as the final estimation of the source wavelet for all blocks to deconvolve the reflectivity; thus, the lateral continuity of the seismic data can be maintained. The advantages of the proposed deconvolution method include using multiple traces to reduce the effect of random noise, tolerance to errors in the initial wavelet estimation, and the ability to preserve the complex structure of the seismic data without using any lateral constraints. Our tests on the synthetic seismic data from the Marmousi2 model and a section of field seismic data demonstrate that the proposed method can effectively derive the wavelet and reflectivity simultaneously from band-limited data with appropriate lateral coherence, even when the seismic data are contaminated by noise and the initial wavelet estimation is inaccurate.
8
Wang, Ruo, and Yanghua Wang. "Multichannel algorithms for seismic reflectivity inversion." Journal of Geophysics and Engineering 14, no. 1 (December 2, 2016): 41–50. http://dx.doi.org/10.1088/1742-2132/14/1/41.
Повний текст джерела
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Barnes, Arthur E. "Moho reflectivity and seismic signal penetration." Tectonophysics 232, no. 1-4 (April 1994): 299–307. http://dx.doi.org/10.1016/0040-1951(94)90091-4.
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Ker, S., Y. Le Gonidec, L. Marié, Y. Thomas, and D. Gibert. "Multiscale seismic reflectivity of shallow thermoclines." Journal of Geophysical Research: Oceans 120, no. 3 (March 2015): 1872–86. http://dx.doi.org/10.1002/2014jc010478.
Повний текст джерела
11
Porsani, Milton J., Bjørn Ursin, and Michelângelo G. Silva. "Dynamic estimation of reflectivity by minimum-delay seismic trace decomposition." GEOPHYSICS 78, no. 3 (May 1, 2013): V109—V117. http://dx.doi.org/10.1190/geo2012-0077.1.
Повний текст джерелаАнотація:
Spiking deconvolution corrects for the effect of the seismic wavelet, assumed to be minimum delay, by applying an inverse filter to the seismic trace to get an estimate of reflectivity. To compensate for propagation and absorption effects, one may use time-varying deconvolution, in which a different inverse filter is computed and applied for each output sample position. We modified this procedure by estimating a minimum-delay wavelet for each time-sample position of the seismic trace. This gives a decomposition of the seismic trace as a sum of minimum-delay wavelets, each multiplied by a reflectivity coefficient. The data vector is equal to a lower triangular wavelet matrix, with element 1 on the diagonal, multiplied by the seismic reflectivity vector. Recursive solution of this equation provides an estimate of reflectivity. The reflectivity estimation is a single-trace process that is sensitive to nonwhite noise, and it does not take into account lateral continuity of reflections. Therefore, we have developed a new data processing strategy by combining it with adaptive singular value decomposition (SVD) filtering. The SVD filtering process is applied to the data in two steps: (1) in a sliding spatial window on NMO-corrected CMP or common shot gathers (2) next, after local dip estimation and correction, on local patches in common-offset panels. After the SVD filtering, we applied the new reflectivity estimation procedure. The SVD filtering removes noise and improves lateral continuity, whereas the reflectivity estimation increases the high-frequency content in the data and improves vertical resolution. The new data processing strategy was successfully applied to land seismic data from northeast Brazil. Improvements in data quality are evident in prestack data panels, velocity analysis, and the stacked section.
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Horgan, Huw J., Sridhar Anandakrishnan, Richard B. Alley, Peter G. Burkett, and Leo E. Peters. "Englacial seismic reflectivity: imaging crystal-orientation fabric in West Antarctica." Journal of Glaciology 57, no. 204 (2011): 639–50. http://dx.doi.org/10.3189/002214311797409686.
Повний текст джерелаАнотація:
AbstractAbrupt changes in crystal-orientation fabric (COF), and therefore viscosity, are observed near the base of the ice sheet throughout West Antarctica. We report on active-source seismic observations from WAIS Divide, mid-stream and downstream on Thwaites Glacier, and the onset region of Bindschadler Ice Stream. These data reveal a prevalence of englacial seismic reflectivity in the bottom quarter of the ice sheet. The observed seismic reflectivity is complex but largely bed-conformable, with long-spatial-wavelength features observed in the flow direction and short-wavelength features observed across flow. A correspondence of englacial structures with bed features is also observed. We determine the origin of the reflectivity to be abrupt changes in the COF of ice, based on the following: (1) observations of englacial reflectivity are consistent with current knowledge of COF within ice sheets, (2) englacial reflectivity caused by COF contrasts requires the simplest genesis, especially at ice divides, and (3) amplitude analysis shows that the observed englacial reflectivity can be explained by contrasts in seismic velocity due to COF changes. We note that the downstream increase in the quantity and complexity of observations indicates that direct observations of COF at ice divides likely underestimate the role that fabric plays in ice-sheet dynamics.
13
de Macedo, Isadora A. S., and José Jadsom S. de Figueiredo. "On the seismic wavelet estimative and reflectivity recovering based on linear inversion: Well-to-seismic tie on a real data set from Viking Graben, North Sea." GEOPHYSICS 85, no. 5 (September 1, 2020): D157—D165. http://dx.doi.org/10.1190/geo2019-0183.1.
Повний текст джерелаАнотація:
Tying seismic data to well data is critical in reservoir characterization. In general, the main factors controlling a successful seismic well tie are an accurate time-depth relationship and a coherent wavelet estimate. Wavelet estimation methods are divided into two major groups: statistical and deterministic. Deterministic methods are based on using the seismic trace and the well data to estimate the wavelet. Statistical methods use only the seismic trace and generally require assumptions about the wavelet’s phase or a random process reflectivity series. We have compared the estimation of the wavelet for seismic well tie purposes through least-squares minimization and zero-order quadratic regularization with the results obtained from homomorphic deconvolution. Both methods make no assumption regarding the wavelet’s phase or the reflectivity. The best-estimated wavelet is used as the input to sparse-spike deconvolution to recover the reflectivity near the well location. The results show that the wavelets estimated from both deconvolutions are similar, which builds our confidence in their accuracy. The reflectivity of the seismic section is recovered according to known stratigraphic markers (from gamma-ray logs) present in the real data set from the Viking Graben field, Norway.
14
Ma, Ming, Shangxu Wang, Sanyi Yuan, Jingjing Wang, and Junxiang Wen. "Multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning." GEOPHYSICS 82, no. 4 (July 1, 2017): V191—V199. http://dx.doi.org/10.1190/geo2016-0366.1.
Повний текст джерелаАнотація:
The reflectivity inversion approach based on a variety of regularization terms was extensively developed and applied to image subsurface structure in recent years. In addition, multichannel reflectivity inversion or deconvolution considering the lateral continuity of reflection interfaces or reflectivity in adjacent channels has been developed. However, these processing operations seldom adaptively judge the stratal continuity or automatically alter the parameters of the corresponding algorithm. To use the special correlation of the reflection information contained in the seismic data, a multichannel spatially correlated reflectivity inversion using block sparse Bayesian learning (bSBL) is introduced. The method adopts a covariance matrix that describes the spatial relationship of reflectivity and simultaneously controls the temporal sparsity. With an expectation-maximization algorithm, we can obtain the parameters of the multichannel reflectivity model, including the mean (i.e., the estimated multichannel reflectivity) and the covariance matrix (i.e., the correlation of nonzero reflection impulses). The noise variance in the observed seismic data is also estimated during the inversion processing. Due to the contribution of reflectivity correlation in different traces, the performance of the multichannel spatially correlated reflectivity inversion using bSBL is significantly superior to the trace-by-trace processing method in the presence of a medium level of noise. The synthetic and real data examples illustrate that the lateral continuity is well-preserved in seismic profiles after inversion.
15
Gomez, Carmen T., and Robert H. Tatham. "Sensitivity analysis of seismic reflectivity to partial gas saturation." GEOPHYSICS 72, no. 3 (May 2007): C45—C57. http://dx.doi.org/10.1190/1.2437121.
Повний текст джерелаАнотація:
We analyze the sensitivity of seismic reflectivity to contrasts in density, seismic propagation velocities, Poisson’s ratio, and gas saturation using the complete Zoeppritz equations. Sensitivities of reflection coefficients to each bulk elastic parameter are computed as the partial derivatives of the seismic reflectivities, relative to each parameter. The sensitivity of reflectivity to gas saturation is then calculated as the full derivative of the reflectivities with respect to gas saturation, assuming both a homogeneous and a patchy distribution of gas in the pore fluids. We compute sensitivities for a sealing shale/gas-sand interface and a gas-sand/wet-sand (gas-water contact, GWC) interface. For the SH-SH reflectivity, the effect of density contrast is strongest in the 30°–50° range of incidence angles for the fluid-fluid interface and at nearer offsets for the shale/gas-sand interface. P-SV reflectivity forthe fluid-fluid interfaces is more sensitive to density contrast in the range of angles of incidence from 30° to 60°. The overall response of P-SV reflectivity to gas saturation throughout all offsets is dominated by the Poisson’s ratio of the gas sand. In the case of P-P reflectivity, the sensitivity to gas saturation increases with increasing incidence angles. The sensitivity of P-SV reflectivity to gas saturation tends to be greatest in the 20°–40° range of incidence angles. For SH-SH reflectivity, the sensitivity to gas saturation for most offsets is controlled mainly by the density contrast, and the sensitivity to density decreases with increasing offset. There is still not a generally accepted seismic reflection method to discriminate commercial gas concentrations from low gas saturation. From the sensitivity analysis, we conclude that the use of P-SV or SH-SH amplitude variation with offset (AVO), integrated with the P-P AVO, will be an essential element in understanding this problem fully.
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Li, Shengjun, Bo Zhang, Xueshan Yong, and Wang Shangxu. "Seismic quality factor estimation using prestack seismic gathers: A simulated annealing approach." Interpretation 8, no. 2 (May 1, 2020): T441—T451. http://dx.doi.org/10.1190/int-2019-0066.1.
Повний текст джерелаАнотація:
Fluid movement and grain boundary friction are the two main factors affecting the anelastic attenuation of seismic data. The quality factor ([Formula: see text]) quantifies the degree of anelastic attenuation and is commonly used in assisting the identification of gas reservoirs. We can accurately compute [Formula: see text] if we obtain the accurate amplitude spectrum of seismic wavelets at refereed and at target time indexes of the seismic profile. However, it is very difficult to obtain the accurate wavelets at the referred and target time indexes. Instead, we usually compare the changes of the amplitude spectrum of refereed and target seismic waveform. The seismic waveform is the convolution result between the seismic wavelet and the reflectivity series. Thus, the reflectivity series would affect our [Formula: see text] computation. We have assumed that the changes of the reflectivity series are negligible within one common-midpoint gather. Then the effect of relativity to the seismic spectrum is the same for the same seismic waveform at different offsets. The seismic waveforms at near offsets are treated as referred seismic signals, and the seismic waveforms at medium and far offsets are regarded as target seismic signals. We use simulated annealing to simultaneously obtain [Formula: see text] values at the entire time index of seismic traces. The method is applied to synthetic and real seismic data to demonstrate its validity and effectiveness. Both applications illustrate the effectiveness of the method in estimating seismic attenuation.
17
Okaya, David A. "Spectral properties of the earth’s contribution to seismic resolution." GEOPHYSICS 60, no. 1 (January 1995): 241–51. http://dx.doi.org/10.1190/1.1443752.
Повний текст джерелаАнотація:
Layered reflectivity sequences have spectral signatures (impulse responses) in accordance with time‐frequency transformations. These signatures are filtered by a source under the convolutional definition of a seismogram. Spectral signatures of wedge models indicate that thin layers have preferred source bandwidths needed to produce either a tuned reflection or separate interface reflections. Sources that do not include these preferred frequencies do not produce optimally imaged reflections. Criteria for the classic tuning thickness and behavior of source‐dependent amplitude versus time‐thickness crossplots are better understood in relation to the reflectivity impulse response. Reflectivity spectra indicate that higher‐order tuning thicknesses exist. Earth reflectivity also prevents the return of certain source frequencies; this behavior may possibly be an interpretive tool.
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Chai, Xintao, Shangxu Wang, Sanyi Yuan, Jianguo Zhao, Langqiu Sun, and Xian Wei. "Sparse reflectivity inversion for nonstationary seismic data." GEOPHYSICS 79, no. 3 (May 1, 2014): V93—V105. http://dx.doi.org/10.1190/geo2013-0313.1.
Повний текст джерелаАнотація:
Conventional reflectivity inversion methods are based on a stationary convolution model and theoretically require stationary seismic traces as input (i.e., those free of attenuation and dispersion effects). Reflectivity inversion for nonstationary data, which is typical for field surveys, requires us to first compensate for the earth’s [Formula: see text]-filtering effects by inverse [Formula: see text] filtering. However, the attenuation compensation for inverse [Formula: see text] filtering is inherently unstable, and offers no perfect solution. Thus, we presented a sparse reflectivity inversion method for nonstationary seismic data. We referred to this method as nonstationary sparse reflectivity inversion (NSRI); it makes the novel contribution of avoiding intrinsic instability associated with inverse [Formula: see text] filtering by integrating the earth’s [Formula: see text]-filtering operator into the stationary convolution model. NSRI also avoids time-variant wavelets that are typically required in time-variant deconvolution. Although NSRI is initially designed for nonstationary signals, it is suitable for stationary signals (i.e., using an infinite [Formula: see text]). The equations for NSRI only use reliable frequencies within the seismic bandwidth, and the basis pursuit optimizes a cost function of mixed [Formula: see text] norms to derive a stable and sparse solution. Synthetic examples show that NSRI can directly retrieve reflectivity from nonstationary data without advance inverse [Formula: see text] filtering. NSRI is satisfactorily stable in the presence of severe noise, and a slight error in the [Formula: see text] value does not greatly disturb the sensitivity of NSRI. A field data example confirmed the effectiveness of NSRI.
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Suprajitno, M., and S. A. Greenhalgh. "Theoretical vertical seismic profiling seismograms." GEOPHYSICS 51, no. 6 (June 1986): 1252–65. http://dx.doi.org/10.1190/1.1442178.
Повний текст джерелаАнотація:
Offset vertical seismic profiling (VSP) theoretical seismograms which include multiples and mode conversions can be computed using a modified “reflectivity” method. In this method, the transformed displacement potentials are first calculated by multiplying the source spectrum by the composite reflectivity function. Integration over wavenumber, followed by inverse Fourier transformation over the frequency range of the signal, yields the synthetic trace. The composite reflectivity function for a buried receiver is derived from Kennett’s matrices (Kennett, 1974, 1979) which are synthesized to form phase‐related reflection and transmission coefficients from a layer stack. Both conventional fixed source‐moving receiver and fixed receiver‐walkaway source (multioffset) VSP geometries can be handled easily. The method can also readily accommodate deviated‐hole VSP. The method is general in that no ray needs to be specified. Because the order of the multiples can be controlled, wraparound problems with the discrete Fourier transform can be avoided. The normal‐incidence VSP seismograms can be rapidly generated as a special case. Several examples illustrate the method. Some classes of laterally varying structures can be approximately handled by restricting the range of ray‐angle integration and by using the principle of superposition.
20
Zhang, Rui, Kui Zhang, and Jude E. Alekhue. "Depth-domain seismic reflectivity inversion with compressed sensing technique." Interpretation 5, no. 1 (February 1, 2017): T1—T9. http://dx.doi.org/10.1190/int-2016-0005.1.
Повний текст джерелаАнотація:
More and more seismic surveys produce 3D seismic images in the depth domain by using prestack depth migration methods, which can present a direct subsurface structure in the depth domain rather than in the time domain. This leads to the increasing need for applications of seismic inversion on the depth-imaged seismic data for reservoir characterization. To address this issue, we have developed a depth-domain seismic inversion method by using the compressed sensing technique with output of reflectivity and band-limited impedance without conversion to the time domain. The formulations of the seismic inversion in the depth domain are similar to time-domain methods, but they implement all the elements in depth domain, for example, a depth-domain seismic well tie. The developed method was first tested on synthetic data, showing great improvement of the resolution on inverted reflectivity. We later applied the method on a depth-migrated field data with well-log data validated, showing a great fit between them and also improved resolution on the inversion results, which demonstrates the feasibility and reliability of the proposed method on depth-domain seismic data.
21
Ross, W. S., and P. M. Shah. "Vertical seismic profile reflectivity: Ups over downs." GEOPHYSICS 52, no. 8 (August 1987): 1149–54. http://dx.doi.org/10.1190/1.1442379.
Повний текст джерелаАнотація:
The idea of designing a deconvolution operator for the vertical seismic profile (VSP) based on its downgoing waves is well known (Anstey, 1976; Gaiser et al., 1984; Hubbard, 1979; Lee and Balch, 1983; Kennett et al., 1980). Many variations of the scheme exist. Anstey (1976) recommends the average of the downgoing wave from all levels as the basis for designing an inverse operator. Lee and Balch (1983) use the downgoing wave from a single level to deconvolve all the VSP traces. Gaiser et al. (1984) and Hubbard (1979) recommend doing the deconvolution independently at each depth level. As observed by Hubbard, there are similar disparities in the literature about whether all or only part of the downgoing wave train should be used in the design of an inverse operator. Although all of the above approaches are identical if multiples are generated in a limited zone near the sea bottom, they differ for more complex media. We recommend, and in this note we explore the theoretical properties of, the level‐by‐level deconvolution based on the entire downgoing wave train. The expressions we develop apply to the general case of the vertical VSP response to any number of horizontal layers with any degree of complexity in the process that generates multiples.
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Wang, L. X. "A neural detector for seismic reflectivity sequences." IEEE Transactions on Neural Networks 3, no. 2 (March 1992): 338–40. http://dx.doi.org/10.1109/72.125877.
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Wang, Yanfei, Yan Cui, and Changchun Yang. "Hybrid regularization methods for seismic reflectivity inversion." GEM - International Journal on Geomathematics 2, no. 1 (March 18, 2011): 87–112. http://dx.doi.org/10.1007/s13137-011-0014-1.
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Zhang, Rui, and John Castagna. "Seismic sparse-layer reflectivity inversion using basis pursuit decomposition." GEOPHYSICS 76, no. 6 (November 2011): R147—R158. http://dx.doi.org/10.1190/geo2011-0103.1.
Повний текст джерелаАнотація:
A basis pursuit inversion of seismic reflection data for reflection coefficients is introduced as an alternative method of incorporating a priori information in the seismic inversion process. The inversion is accomplished by building a dictionary of functions representing reflectivity patterns and constituting the seismic trace as a superposition of these patterns. Basis pursuit decomposition finds a sparse number of reflection responses that sum to form the seismic trace. When the dictionary of functions is chosen to be a wedge-model of reflection coefficient pairs convolved with the seismic wavelet, the resulting reflectivity inversion is a sparse-layer inversion, rather than a sparse-spike inversion. Synthetic tests suggest that a sparse-layer inversion using basis pursuit can better resolve thin beds than a comparable sparse-spike inversion. Application to field data indicates that sparse-layer inversion results in the potentially improved detectability and resolution of some thin layers and reveals apparent stratigraphic features that are not readily seen on conventional seismic sections.
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Blouin, Martin, and Erwan Gloaguen. "Colored inversion." Leading Edge 36, no. 10 (October 2017): 858–61. http://dx.doi.org/10.1190/tle36100858.1.
Повний текст джерелаАнотація:
Whether it is deterministic, band-limited, or stochastic, seismic inversion can bear many names depending on the algorithm used to produce it. Broadly, inversion converts reflectivity data to physical properties of the earth, such as acoustic impedance (AI), the product of seismic velocity and bulk density. This is crucial because, while reflectivity informs us about boundaries, impedance can be converted to useful earth properties such as porosity and fluid content via known petrophysical relationships.
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Boulfoul, M., and Doyle R. Watts. "Application of instantaneous rotations to S‐wave vertical seismic profiling." GEOPHYSICS 62, no. 5 (September 1997): 1365–68. http://dx.doi.org/10.1190/1.1444240.
Повний текст джерелаАнотація:
The petroleum exploration industry uses S‐wave vertical seismic profiling (VSP) to determine S‐wave velocities from downgoing direct arrivals, and S‐wave reflectivities from upgoing waves. Seismic models for quantitative calibration of amplitude variation with offset (AVO) data require S‐wave velocity profiles (Castagna et al., 1993). Vertical summations (Hardage, 1983) of the upgoing waves produce S‐wave composite traces and enable interpretation of S‐wave seismic profile sections. In the simplest application of amplitude anomalies, the coincidence of high amplitude P‐wave reflectivity and low amplitude S‐wave reflectivity is potentially a direct indicator of the presence of natural gas.
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He, Ruiqing, Brian Hornby, and Gerard Schuster. "3D wave-equation interferometric migration of VSP free-surface multiples." GEOPHYSICS 72, no. 5 (September 2007): S195—S203. http://dx.doi.org/10.1190/1.2743375.
Повний текст джерелаАнотація:
Interferometric migration of free-surface multiples in vertical-seismic-profile (VSP) data has two significant advantages over standard VSP imaging: (1) a significantly larger imaging area compared to migrating VSP primaries and (2) less sensitivity to velocity-estimation and static errors than other methods for migration of multiples. In this paper, we present a 3D wave-equation interferometric migration method that efficiently images VSP free-surface multiples. Synthetic and field data results confirm that a reflectivity image volume, comparable in size to a 3D surface seismic survey around the well, can be computed economically. The reflectivity image volume has less fold density and lower signal-to-noise ratio than that obtained by a conventional 3D surface seismic survey because of the relatively weak energy of multiples and the limited number of geophones in the well. However, the efficiency of this method for migrating VSP multiples suggests that it might sometimes be a useful tool for 4D seismic monitoring where reflectivity images can be computed quickly for each time-lapse survey.
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Kaaresen, Kjetil F., and Tofinn Taxt. "Multichannel blind deconvolution of seismic signals." GEOPHYSICS 63, no. 6 (November 1998): 2093–107. http://dx.doi.org/10.1190/1.1444503.
Повний текст джерелаАнотація:
A new algorithm for simultaneous wavelet estimation and deconvolution of seismic reflection signals is given. To remove the inherent ambiguity in this blind deconvolution problem, we introduce relevant a priori information. Our major assumption is sparseness of the reflectivity, which corresponds to a layered‐earth model. This allows nonminimum‐phase wavelets to be recovered reliably and closely spaced reflectors to be resolved. To combine a priori knowledge and data, we use a Bayesian framework and derive a maximum a posteriori estimate. Computing this estimate is a difficult optimization problem solved by a suboptimal iterative procedure. The procedure alternates steps of wavelet estimation and reflectivity estimation. The first step only requires a simple least‐squares fit, while the second step is solved by the iterated window maximization algorithm proposed by Kaaresen. This enables better efficiency and optimality than established alternatives. The resulting optimization method can easily handle multichannel models with only a moderate increase of the computational load. Lateral continuity of the reflectors is achieved by modeling local dependencies between neighboring traces. Major improvements in both wavelet and reflectivity estimates are obtained by taking the wavelet to be invariant across several traces. The practicality of the algorithm is demonstrated on synthetic and real seismic data. An application to multivariate well‐log segmentation is also given.
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Bao, Chen, Juan R. Jimenez, Stephan Gelinsky, and Raphic van der Weiden. "Seismic resonance: Wavelet-free reflectivity retrieval via modified cepstral decomposition." GEOPHYSICS 87, no. 1 (December 21, 2021): IM11—IM24. http://dx.doi.org/10.1190/geo2020-0727.1.
Повний текст джерелаАнотація:
Spectral decomposition is a proven tool in seismic interpretation. It helps interpreters highlight channels and map temporal bed thickness and other geologic discontinuities. In spectrally decomposed seismic data, amplitude spectra contain notch patterns. The period of these notch patterns inversely correlates to the reservoir layer’s thickness and/or its interval velocity. Additional cepstral decomposition will directly extract the bed time thickness or arrival time under simple reflectivity setups. Based on these observations, we have adopted a new workflow for reflectivity retrieval in stably phased seismic. There is no need to understand the details of the wavelet in this workflow. We find that the reflector time and its “apparent strength” can be identified in a transformed seismic resonance domain resulting from a modified cepstrum analysis. We find that linear hot spots in this domain lead to direct quantification of the reflectivity series. The timing and strength of the linear hot spots reveals reflector times and scaled reflectivity coefficients. We applied this method for thickness prediction for a target deepwater reservoir with a complex geologic setting. Because of the large reservoir thickness variations and weak impedance contrast with underlying lithology, identification of the base reservoir and therefore the reservoir thickness has been historically challenging in this field. In a blind test, the reservoir thicknesses evaluated from this method are close to the true vertical thickness found in wells.
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Yuan, Cheng, and Mingjun Su. "Seismic spectral sparse reflectivity inversion based on SBL-EM: experimental analysis and application." Journal of Geophysics and Engineering 16, no. 6 (October 18, 2019): 1124–38. http://dx.doi.org/10.1093/jge/gxz082.
Повний текст джерелаАнотація:
Abstract In this paper, we propose a new method of seismic spectral sparse reflectivity inversion that, for the first time, introduces Expectation-Maximization-based sparse Bayesian learning (SBL-EM) to enhance the accuracy of stratal reflectivity estimation based on the frequency spectrum of seismic reflection data. Compared with the widely applied sequential algorithm-based sparse Bayesian learning (SBL-SA), SBL-EM is more robust to data noise and, generally, can not only find a sparse solution with higher precision, but also yield a better lateral continuity along the final profile. To investigate the potential of SBL-EM in a seismic spectral sparse reflectivity inversion, we evaluate the inversion results by comparing them with those of a SBL-SA-based approach in multiple aspects, including the sensitivity to different frequency bands, the robustness to data noise, the lateral continuity of the final profiles and so on. Furthermore, we apply the mean square error (MSE), residual variance (RV) of seismograms and residual energy (RE) between the frequency spectra of the true and inverted reflectivity model to highlight the advantages of the proposed method over the SBL-SA-based approach in terms of spectral sparse reflectivity inversion within a sparse Bayesian learning framework. Multiple examples, including both numerical and field experiments, are carried out to validate the proposed method.
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Verschuur, D. J., A. J. Berkhout, and C. P. A. Wapenaar. "Adaptive surface‐related multiple elimination." GEOPHYSICS 57, no. 9 (September 1992): 1166–77. http://dx.doi.org/10.1190/1.1443330.
Повний текст джерелаАнотація:
The major amount of multiple energy in seismic data is related to the large reflectivity of the surface. A method is proposed for the elimination of all surface‐related multiples by means of a process that removes the influence of the surface reflectivity from the data. An important property of the proposed multiple elimination process is that no knowledge of the subsurface is required. On the other hand, the source signature and the surface reflectivity do need to be provided. As a consequence, the proposed process has been implemented adaptively, meaning that multiple elimination is designed as an inversion process where the source and surface reflectivity properties are estimated and where the multiple‐free data equals the inversion residue. Results on simulated data and field data show that the proposed multiple elimination process should be considered as one of the key inversion steps in stepwise seismic inversion.
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Scheuer, T. E., and D. E. Wagner. "Deconvolution by autocepstral windowing." GEOPHYSICS 50, no. 10 (October 1985): 1533–40. http://dx.doi.org/10.1190/1.1441843.
Повний текст джерелаАнотація:
The autocepstrum of a reflection seismogram is defined by the cepstrum of its autocorrelation function. Using the autocepstrum extends the basic deconvolution method for removing a minimum‐phase source wavelet to unmask subsurface reflectivity. When we record only the seismic trace and assume a minimumphase source wavelet, deconvolution reduces to estimating the wavelet autocorrelation. In practice, a portion of the seismic trace autocorrelation is used as an estimate of the wavelet autocorrelation. This can be justified by assuming a random reflectivity series with a white power spectrum. However, in cases where the reflectivity spectrum is not white, a preferred wavelet autocorrelation may be obtained by low‐pass windowing the trace autocepstrum. This approach liberates the selection of various deconvolution parameters such as filter length and design window length that are typically chosen to reinforce the assumption of a white reflectivity spectrum. For problems that require short, deconvolution‐filter design windows, and thus nonwhite reflectivity spectra, windowing the trace autocepstrum is an appropriate alternative to the conventional practice of windowing the trace autocorrelation.
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Johansen, Tor Arne, Per Digranes, Mark van Schaack, and Ida Lønne. "Seismic mapping and modeling of near‐surface sediments in polar areas." GEOPHYSICS 68, no. 2 (March 2003): 566–73. http://dx.doi.org/10.1190/1.1567226.
Повний текст джерелаАнотація:
A knowledge of permafrost conditions is important for planning the foundation of buildings and engineering activities at high latitudes and for geological mapping of sediment thicknesses and architecture. The freezing of sediments is known to greatly affect their seismic velocities. In polar regions the actual velocities of the upper sediments may therefore potentially reveal water saturation and extent of freezing. We apply various strategies for modeling seismic velocities and reflectivity properties of unconsolidated granular materials as a function of water saturation and freezing conditions. The modeling results are used to interpret a set of high‐resolution seismic data collected from a glaciomarine delta at Spitsbergen, the Norwegian Arctic, where the upper subsurface sediments are assumed to be in transition from unfrozen to frozen along a transect landward from the delta front. To our knowledge, this is the first attempt to study pore‐fluid freezing from such data. Our study indicates that the P‐ and S‐wave velocities may increase as much as 80–90% when fully, or almost fully, water‐saturated unconsolidated sediments freeze. Since a small amount of frozen water in the voids of a porous rock can lead to large velocity increases, the freezing of sediments reduces seismic resolution; thus, the optimum resolution is obtained at locations where the sediments appear unfrozen. The reflectivity from boundaries separating sediments of slightly different porosity may depend more strongly on the actual saturation rather than changes in granular characteristics. For fully water‐saturated sediments, the P‐wave reflectivity decreases sharply with freezing, while the reflectivity becomes less affected as the water saturation is lowered. Thus, a combination of velocity and reflectivity information may reveal saturation and freezing conditions.
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Xue, Jiao, Chengguo Cai, Hanming Gu, and Zongjie Li. "Matching pursuit-based sparse spectral analysis: Estimating frequency-dependent anomalies from nonstationary seismic data." GEOPHYSICS 85, no. 5 (August 17, 2020): V385—V396. http://dx.doi.org/10.1190/geo2018-0758.1.
Повний текст джерелаАнотація:
Spectral decomposition has been widely used to detect frequency-dependent anomalies associated with hydrocarbons. By ignoring the time-variant feature of the frequency content of individual reflected wavelets, we have adopted a sparse time-frequency spectrum and developed a matching pursuit-based sparse spectral analysis (MP-SSA) method to estimate the sparse time-frequency representation of the seismic data. Further, we evaluate a generalized nonstationary convolution model concerning propagation attenuation and frequency-dependent reflectivity, and we mathematically evaluate the sparse time-frequency spectrum of the nonstationary seismic data as being equal to the product of the Fourier spectrum of the source wavelet, frequency-dependent reflection coefficient, and the cumulative attenuation during seismic wave propagation. Therefore, the reflectivity spectrum, which is a combination of the frequency-dependent reflectivity and the propagation attenuation, can be determined by dividing the sparse time-frequency spectrum of the seismic data by the Fourier spectrum of the source wavelet. Application of the matching pursuit-based decomposition methods to synthetic nonstationary convolutional data illustrates that the adopted MP-SSA spectrum shows a higher time resolution than the matching pursuit-based Wigner-Ville distribution and the matching pursuit-based instantaneous spectral analysis spectra. Notably, the MP-SSA method can avoid spectral smearing, which may introduce distortions to the frequency-dependent anomaly estimation. Application of the amplitude versus frequency analysis based on MP-SSA to field data illustrates the potential of using the sparse reflectivity spectral intercept and gradient to detect the hydrocarbon reservoirs.
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Thomson, C. J., P. W. Kitchenside, and R. P. Fletcher. "Theory of reflectivity blurring in seismic depth imaging." Geophysical Journal International 205, no. 2 (March 14, 2016): 837–55. http://dx.doi.org/10.1093/gji/ggw025.
Повний текст джерела
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de Macedo, Isadora A. S., and Jose Jadsom S. de Figueiredo. "Using Benford’s law on the seismic reflectivity analysis." Interpretation 6, no. 3 (August 1, 2018): T689—T697. http://dx.doi.org/10.1190/int-2017-0201.1.
Повний текст джерелаАнотація:
Benford’s law (BL) is a mathematical theory of leading digits. This law predicts that the distribution of first digits of real-world observations is not uniform and follows a trend in which measurements with a lower first digit (1, 2, …) occur more frequently than those with higher first digits (…, 8, 9). A data set from earth’s geomagnetic field, the estimated time in years between reversals of earth’s geomagnetic field, the seismic P-wave speed of earth’s mantle below the southwest Pacific, and other geophysical data obey the BL. Although there are other statistical methods for analyzing a data set, we test, for the first time, the analysis of the seismic reflectivity through the Benford distribution point of view. We applied the BL on real reflectivity data from two wells from the Penobscot field and another two from the Viking Graben field. In both data sets, the reflectivity was in conformity with the BL. Moreover, after analyzing the effect of sonic and density logs despiking on Benford’s distribution through the BL, we found an optimum coefficient for the despiking process, which was a common procedure used to edit the well-log data before its use on reservoir studies.
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Wood, Warren T., Kylara M. Martin, Wooyeol Jung, and John Sample. "Seismic reflectivity effects from seasonal seafloor temperature variation." Geophysical Research Letters 41, no. 19 (October 9, 2014): 6826–32. http://dx.doi.org/10.1002/2014gl061383.
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Russell, Brian H. "Prestack seismic amplitude analysis: An integrated overview." Interpretation 2, no. 2 (May 1, 2014): SC19—SC36. http://dx.doi.org/10.1190/int-2013-0122.1.
Повний текст джерелаАнотація:
In this tutorial, I present an overview of the techniques that are in use for prestack seismic amplitude analysis, current and historical. I show that these techniques can be classified as being based on the computation and analysis of either some type of seismic reflection coefficient series or seismic impedance. Those techniques that are based on the seismic reflection coefficient series, or seismic reflectivity for short, are called amplitude variation with offset methods, and those that are based on the seismic impedance are referred to as prestack amplitude inversion methods. Seismic reflectivity methods include: near and far trace stacking, intercept versus gradient analysis, and the fluid factor analysis. Seismic impedance methods include: independent and simultaneous P and S-impedance inversion, lambda-mu-rho analysis, Poisson impedance inversion, elastic impedance, and extended elastic impedance inversion. The objective of this tutorial is thus to make sense of all of these methods and show how they are interrelated. The techniques will be illustrated using a 2D seismic example over a gas sand reservoir from Alberta. Although I will largely focus on isotropic methods, the last part of the tutorial will extend the analysis to anisotropic reservoirs.
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O’Brien, John, and Ron Harris. "Multicomponent VSP imaging of tight-gas sands." GEOPHYSICS 71, no. 6 (November 2006): E83—E90. http://dx.doi.org/10.1190/1.2335646.
Повний текст джерелаАнотація:
Low-porosity Bossier and Cotton Valley sands of the East Texas Basin, U. S., have only a small acoustic impedance contrast with the encasing shales but a greater relative contrast in shear-wave impedance. Vertical seismic profile (VSP) data acquired with both a near-offset and far-offset P-wave source clearly demonstrate the P-P reflectivity and P-S mode conversions within the Bossier section. We designate conventional P-wave reflectivity as P-P, shear-wave reflectivity as S-S, and P-wave/shear-wave mode conversion data as P-S. While Bossier P-P reflectivity is low, it appears to be adequate for mapping thick sandbodies such as the York Sandstone, the main exploration target in this area. However, P-P reflectivity is even lower and is inadequate for imaging the overlying Cotton Valley Sands. In contrast, the far-offset VSP data acquired with a P-wave source demonstrate a high level of P-S-mode conversion, which is used to image this interval with definition that is not provided by P-P reflectivity. This provides strong support for the use of P-S-mode conversion imaging for seismic characterization of tight sand reservoirs. Near-offset shear-wave VSP data acquired with a shearwave source show low S/N ratio and limited bandwidth for the downgoing waveform because of the depth of the target; shear-wave energy appears to have a more limited range of propagation than P-waves. Such effects may also have a strong negative impact on multicomponent imaging of these sands using surface seismic techniques. Multicomponent 3D VSP imaging provides a superior solution by placing the geophones closer to the subsurface zone of interest.
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Chai, Xintao, Genyang Tang, Fangfang Wang, Hanming Gu, and Xinqiang Wang. "Q-compensated acoustic impedance inversion of attenuated seismic data: Numerical and field-data experiments." GEOPHYSICS 83, no. 6 (November 1, 2018): R553—R567. http://dx.doi.org/10.1190/geo2017-0499.1.
Повний текст джерелаАнотація:
Acoustic impedance (AI) inversion is of great interest because it extracts information regarding rock properties from seismic data and has successful applications in reservoir characterization. During wave propagation, anelastic attenuation and dispersion always occur because the subsurface is not perfectly elastic, thereby diminishing the seismic resolution. AI inversion based on the convolutional model requires that the input data be free of attenuation effects; otherwise, low-resolution results are inevitable. The intrinsic instability that occurs while compensating for the anelastic effects via inverse [Formula: see text] filtering is notorious. The gain-limit inverse [Formula: see text] filtering method cannot compensate for strongly attenuated high-frequency components. A nonstationary sparse reflectivity inversion (NSRI) method can estimate the reflectivity series from attenuated seismic data without the instability issue. Although AI is obtainable from an inverted reflectivity series through recursion, small inaccuracies in the reflectivity series can result in large perturbations in the AI result because of the cumulative effects. To address these issues, we have developed a [Formula: see text]-compensated AI inversion method that directly retrieves high-resolution AI from attenuated seismic data without prior inverse [Formula: see text] filtering based on the theory of NSRI and AI inversion. This approach circumvents the intrinsic instability of inverse [Formula: see text] filtering by integrating the [Formula: see text] filtering operator into the convolutional model and solving the inverse problem iteratively. This approach also avoids the ill-conditioned nature of the recursion scheme for transforming an inverted reflectivity series to AI. Experiments on a benchmark Marmousi2 model validate the feasibility and capabilities of our method. Applications to two field data sets verify that the inversion results generated by our approach are mostly consistent with the well logs.
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Sui, Yuhan, and Jianwei Ma. "Blind sparse-spike deconvolution with thin layers and structure." GEOPHYSICS 85, no. 6 (November 1, 2020): V481—V496. http://dx.doi.org/10.1190/geo2019-0423.1.
Повний текст джерелаАнотація:
Blind sparse-spike deconvolution is a widely used method to estimate seismic wavelets and sparse reflectivity in the shape of spikes based on the convolution model. To increase the vertical resolution and lateral continuity of the estimated reflectivity, we further improve the sparse-spike deconvolution by introducing the atomic norm minimization and structural regularization, respectively. Specifically, we use the atomic norm minimization to estimate the reflector locations, which are further used as position constraints in the sparse-spike deconvolution. By doing this, we can vertically separate highly thin layers through the sparse deconvolution. In addition, the seismic structural orientations are estimated from the seismic image to construct a structure-guided regularization in the deconvolution to preserve the lateral continuity of reflectivities. Our improvements are suitable for most types of sparse-spike deconvolution approaches. The sparse-spike deconvolution method with Toeplitz-sparse matrix factorization (TSMF) is used as an example to demonstrate the effectiveness of our improvements. Synthetic and real examples show that our methods perform better than TSMF in estimating the reflectivity of thin layers and preserving the lateral continuities.
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Zhou, Qingbao, Jinghuai Gao, and Zhiguo Wang. "Sparse Spike Deconvolution of Seismic Data Using Trust-Region Based SQP Algorithm." Journal of Computational Acoustics 23, no. 04 (December 2015): 1540002. http://dx.doi.org/10.1142/s0218396x15400020.
Повний текст джерелаАнотація:
A new deconvolution algorithm for retrieving a sparse reflectivity series from noisy seismic traces is proposed. The problem is formulated as a constrained minimization, taking the approximation zero norm of reflectivity as the objective function. The resulting minimization is solved efficiently by the trust-region based sequential quadratic programming (SQP) method, which provides global convergence and local quadratic convergence rates under suitable assumptions. The null space decomposition method and the de-biasing method are employed to reduce computational complexity and further improve the calculation accuracy. Synthetic simulations indicate that the spikes on the reflectivity, both their positions and amplitudes, are recovered effectively by the proposed approach.
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Bennett, Alexandra. "Exploring for stratigraphic traps in the Patchawarra Formation, Cooper Basin: an integrated seismic methodology." APPEA Journal 58, no. 2 (2018): 779. http://dx.doi.org/10.1071/aj17089.
Повний текст джерелаАнотація:
The Patchawarra Formation is characterised by Permian aged fluvial sediments. The conventional hydrocarbon play lies within fluvial sandstones, attributed to point bar deposits and splays, that are typically overlain by floodbank deposits of shales, mudstones and coals. The nature of the deposition of these sands has resulted in the discovery of stratigraphic traps across the Western Flank of the Cooper Basin, South Australia. Various seismic techniques are being used to search for and identify these traps. High seismic reflectivity of the coals with the low reflectivity of the relatively thin sands, often below seismic resolution, masks a reservoir response. These factors, combined with complex geometry of these reservoirs, prove a difficult play to image and interpret. Standard seismic interpretation has proven challenging when attempting to map fluvial sands. Active project examples within a 196 km2 3D seismic survey detail an evolving seismic interpretation methodology, which is being used to improve the delineation of potential stratigraphic traps. This involves an integration of seismic processing, package mapping, seismic attributes and imaging techniques. The integrated seismic interpretation methodology has proven to be a successful approach in the discovery of stratigraphic and structural-stratigraphic combination traps in parts of the Cooper Basin and is being used to extend the play northwards into the 3D seismic area discussed.
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Carr, Bradley J., and Zoltan Hajnal. "P- and S-wave characterization of near‐surface reflectivity from glacial tills using vertical seismic profiles." GEOPHYSICS 64, no. 3 (May 1999): 970–80. http://dx.doi.org/10.1190/1.1444606.
Повний текст джерелаАнотація:
Fundamental reflectivity properties are established within the glacial deposits of central Saskatchewan, Canada. Multicomponent vertical seismic profile (VSP) data collected in three shallow boreholes are used to obtain detailed acoustic property information within the first 80 m of the near‐surface strata. The integration of both P- and S-wave VSP data, in conjunction with other borehole geophysics, provided a unique opportunity to obtain in‐situ seismic reflection response properties in layered clay and sand tills. P- and S-wave interval velocity profiles, in conjunction with P- and S-wave VSP reflectivities are analyzed to provide insight into seismic wavefield behavior within ∼80 m of the surface. In general, shear wave energy identifies more reflective intervals than the P-wave energy because of better vertical resolution for S-wave energy (0.75 m) compared to P-wave energy (2.3 m) based on quarter wavelength criterion. For these saturated, unconsolidated glacial deposits, more details about the lithologic constituents and in‐situ porosity are detectable from the S-wave reflectivity, but P-wave reflections provide a good technique for mapping the bulk changes. The principal cause of seismic reflectivity is the presence and/or amount of sand, and the degree of fluid‐filled porosity within the investigated formations.
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Shao, Jie, and Yibo Wang. "Simultaneous inversion of Q and reflectivity using dictionary learning." GEOPHYSICS 86, no. 5 (September 1, 2021): R763—R776. http://dx.doi.org/10.1190/geo2020-0095.1.
Повний текст джерелаАнотація:
Quality factor ( Q) and reflectivity are two important subsurface properties in seismic data processing and interpretation. They can be calculated simultaneously from a seismic trace corresponding to an anelastic layered model by a simultaneous inversion method based on the nonstationary convolutional model. However, the conventional simultaneous inversion method calculates the optimum Q and reflectivity based on the minimum of the reflectivity sparsity by sweeping each Q value within a predefined range. As a result, the accuracy and computational efficiency of the conventional method depend heavily on the predefined Q value set. To improve the performance of the conventional simultaneous inversion method, we have developed a dictionary learning-based simultaneous inversion of Q and reflectivity. The parametric dictionary learning method is used to update the initial predefined Q value set automatically. The optimum Q and reflectivity are calculated from the updated Q value set based on minimizing not only the sparsity of the reflectivity but also the data residual. Synthetic data and two field data sets are used to test the effectiveness of our method. The results demonstrate that our method can effectively improve the accuracy of these two parameters compared to the conventional simultaneous inversion method. In addition, the dictionary learning method can improve computational efficiency up to approximately seven times when compared to the conventional method with a large predefined dictionary.
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Zhang, Ming Shan, and Jian Huang. "Quasi-Likelihood Deconvolution of Non-Gaussian Non-Invertible Moving Average Model." Advanced Materials Research 605-607 (December 2012): 1781–87. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.1781.
Повний текст джерелаАнотація:
In reflection seismology the reflectivity sequence is of primary interest and must be estimated. Estimation of the reflectivity sequence is based on deconvolution of seismic trace data. Modelling the seismic trace as the non-Gaussian moving average time series, we propose a deconvolution method based on the modified estimation, which is consistent estimation of moving average models with heavy tailed error distribution. The asymptotic equivalence is established between the proposed method and the deconvolution using . Simulation studies are presented to validate the equivalency. Furthermore, based on this equivalence the consistency problem of the deconvolution has been discussed.
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Madrinovella, Iktri, and Waskito Pranowo. "THE DIRECT-INVERSION DECONVOLUTION AND ITS APPLICATION IN SEISMIC DATA." Jurnal Geofisika Eksplorasi 8, no. 1 (March 30, 2022): 31–43. http://dx.doi.org/10.23960/jge.v8i1.187.
Повний текст джерелаАнотація:
Seismic traces are generated by the convolution of reflectivity and seismic wavelet. Due to limited frequency bandwidth, reflectivity can not be resolved easily. Deconvolution is a method to increase the frequency bandwidth and gives seismic data higher resolution, which makes it easier to analyze. Deconvolution is a common method in the seismic data processing. The mathematical definition of deconvolution is an inverse process of convolution, but the computation of deconvolution uses convolution in its process (Wiener deconvolution). We explained a method that is direct from the mathematical definition. We refer to it as direct-inversion deconvolution. The direct-inversion deconvolution process involves the matrix operation between seismic trace and wavelet instead of the deconvolution filter. By applying the direct-inversion deconvolution, the produced (or deconvolved) seismic trace shows a better result with higher resolution, regardless of the wavelet’s phase. Finally, we performed a phase rotation experiment, and the deconvolution result shows no seismic phase alteration. In comparison, we also perform spiking deconvolution in synthetic data experiments. This method is applied to The North Sea Volve Data Village seismic data, and more thin layers are significantly detected. Finally, it turns out that direct-inversion deconvolution gives a higher resolution to seismic data.
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Wang, Yichuan, and Igor B. Morozov. "Time-lapse acoustic impedance variations during CO2 injection in Weyburn oilfield, Canada." GEOPHYSICS 85, no. 1 (November 22, 2019): M1—M13. http://dx.doi.org/10.1190/geo2019-0221.1.
Повний текст джерелаАнотація:
For seismic monitoring injected fluids during enhanced oil recovery or geologic [Formula: see text] sequestration, it is useful to measure time-lapse (TL) variations of acoustic impedance (AI). AI gives direct connections to the mechanical and fluid-related properties of the reservoir or [Formula: see text] storage site; however, evaluation of its subtle TL variations is complicated by the low-frequency and scaling uncertainties of this attribute. We have developed three enhancements of TL AI analysis to resolve these issues. First, following waveform calibration (cross-equalization) of the monitor seismic data sets to the baseline one, the reflectivity difference was evaluated from the attributes measured during the calibration. Second, a robust approach to AI inversion was applied to the baseline data set, based on calibration of the records by using the well-log data and spatially variant stacking and interval velocities derived during seismic data processing. This inversion method is straightforward and does not require subjective selections of parameterization and regularization schemes. Unlike joint or statistical inverse approaches, this method does not require prior models and produces accurate fitting of the observed reflectivity. Third, the TL AI difference is obtained directly from the baseline AI and reflectivity difference but without the uncertainty-prone subtraction of AI volumes from different seismic vintages. The above approaches are applied to TL data sets from the Weyburn [Formula: see text] sequestration project in southern Saskatchewan, Canada. High-quality baseline and TL AI-difference volumes are obtained. TL variations within the reservoir zone are observed in the calibration time-shift, reflectivity-difference, and AI-difference images, which are interpreted as being related to the [Formula: see text] injection.
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Tcheverda, Vladimir, and Kirill Gadylshin. "Elastic Full-Waveform Inversion Using Migration-Based Depth Reflector Representation in the Data Domain." Geosciences 11, no. 2 (February 9, 2021): 76. http://dx.doi.org/10.3390/geosciences11020076.
Повний текст джерелаАнотація:
The depth velocity model is a critical element for providing seismic data processing success, as it is responsible for the times of waves’ propagation and, therefore, prescribes the location of geological objects in the resulting seismic images. Constructing a deep velocity model is the most time-consuming part of the entire seismic data processing, which usually requires interactive human intervention. This article introduces the consistently numerical method for reconstructing a depth velocity model based on the modified version of the elastic Full Waveform Inversion (FWI). The specific feature of this approach to FWI is the decomposition of the space of admissible velocity models into subspaces of propagator (macro velocity) and reflector components. In turn, the latter transforms to the data space reflectivity on the base of migration transformation. Finally, we perform minimisation in two different spaces: (1) Macro velocity as a smooth spatial function; (2) Migration transforms data space reflectivity to the spatial reflectivity. We present numerical experiments confirming less sensitiveness of the modified version of FWI to the lack of the low time frequencies in the data acquired. In our computations, we use synthetic data with valuable time frequencies from 5 Hz.
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Carrière, Olivier, and Peter Gerstoft. "Deep-water subsurface imaging using OBS interferometry." GEOPHYSICS 78, no. 2 (March 1, 2013): Q15—Q24. http://dx.doi.org/10.1190/geo2012-0241.1.
Повний текст джерелаАнотація:
Seismic interferometry processing is applied to an active seismic survey collected on ocean bottom seismometers (OBS) deployed at 900-m water depth over a carbonate/hydrates mound in the Gulf of Mexico. Common midpoint processing and stacking of the extracted Green’s function gives the subsurface PP reflectivity, with a horizontal resolution of half the receiver spacing. The obtained seismic section is comparable to classical upgoing/downgoing wavefield decomposition and deconvolution applied on a common receiver gather. Seismic interferometry does not require precise knowledge of source geometry or shooting times, but more accurate results are obtained when including this information for segmenting the signals before the crosscorrelations, especially when signals from distant surveys are present in the data. Reflectivity estimates can be obtained with the crosscorrelation of pressure or vertical particle velocity signals, but the pressure data gives the best resolution due to the wider frequency bandwidth and the reduced amount of noise bursts.