Interpretive advantages of 90°-phase wavelets: Part 1 — Modeling

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. C7-C15 ◽  
Author(s):  
Hongliu Zeng ◽  
Milo M. Backus

We discuss, in a two-part article, the benefits of 90°-phase wavelets in stratigraphic and lithologic interpretation of seismically thin beds. In Part 1, seismic models of Ricker wavelets with selected phases are constructed to assess interpretability of composite waveforms in increasingly complex geologic settings. Although superior for single surface and thick-layer interpretation, zero-phase seismic data are not optimal for interpreting beds thinner than a wavelength because their antisymmetric thin-bed responses tie to the reflectivity series rather than to impedance logs. Nonsymmetrical wavelets (e.g., minimum-phase wavelets) are generally not recommended for interpretation because their asymmetric composite waveforms have large side lobes. Integrated zero-phase traces are also less desirable because they lose high-frequency components in the integration process. However, the application of 90°-phase data consistently improves seismic interpretability. The unique symmetry of 90°-phase thin-bed response eliminates the dual polarity of thin-bed responses, resulting in better imagery of thin-bed geometry, impedance profiles, lithology, and stratigraphy. Less amplitude distortion and less stratigraphy-independent, thin-bed interference lead to more accurate acoustic impedance estimation from amplitude data and a better tie of seismic traces to lithology-indicative wireline logs. Field data applications are presented in part 2 of this article.

Geophysics ◽  
2005 ◽  
Vol 70 (3) ◽  
pp. C17-C24 ◽  
Author(s):  
Hongliu Zeng ◽  
Milo M. Backus

We examine field seismic data to test the benefits of 90°-phase wavelets in thin-bed interpretation that are predicted by seismic modeling in part 1 of this paper. In an interbedded sandstone-shale Miocene succession in the Gulf of Mexico basin, a 90°-phase shift of nearly zero-phase seismic data significantly improves lithologic and stratigraphic interpretation. A match between seismic and acoustic impedance (AI) profiles results in a better tie between seismic amplitude traces and lithology-indicative logs. Better geometric imaging of AI units that does not use dual-polarity seismic events results in easier and more accurate reservoir delineation. Less amplitude distortion and the stratigraphy-independent nature of thin-bed interference significantly improves stratigraphic resolution and seismic stratigraphic profiling. For a Ricker-like wavelet having small side lobes, stratigraphic resolution of 90°-phase data is considerably higher than that of zero-phase data. In this specific case, stratigraphic resolution of 90°-phase data is λ/4 (λ = wavelength), compared with λ/2 for its zero-phase counterpart. Stratal slices made from 90°-phase data show geomorphologic patterns of depositional systems with less noise and fewer interference fingerprints. A Permian Basin field provides a real-world example of porous zones in thin, high-frequency carbonate sequences that are better visualized with 90°-phase seismic data than with zero-phase data.


2019 ◽  
Vol 37 (4) ◽  
Author(s):  
Carlos Cunha Filho ◽  
Leonardo Teixeira Da Silva ◽  
Nathalia Souto Muniz Da Cruz ◽  
Andrea Damasceno ◽  
Tatiana Soares De Oliveira ◽  
...  

ABSTRACTThe identification of clay-rich layers is crucial for development of pre-salt reservoirs. They represent flow barriers and compromise the return of investment of the project if the thickness is misvalued. This issue becomes more relevant for thin clay-rich layers. The solution for the characterization of thin beds is classic: increase of the frequency bandwidth in seismic data. Here, we present a new methodology to derive high-frequency impedance volume. The approach starts with the recovery of low and high-frequency components in seismic data by the application of interactive deconvolution (iterdec). The extended bandwidth data is employed as an input amplitude data to the sparse-spike inversion. The outcome is a high-frequency acoustic impedance volume, which improves the interpretation of thin clay-rich layers. We present a study case of a presalt reservoir to demonstrate that this technique mitigated the location risk of an injection well and helped to maximize the oil swept of its vicinity. Furthermore, we discuss the required adaptations in the sparse-spike inversion workflow, and present the advantages of this approach when compared with conventional inversion results.Keywords: Inversion, resolution, broadband, pre-salt. RESUMOA identificação de camadas argilosas é crucial para o desenvolvimento de reservatórios do pre-sal. Elas atuam como barreira para o fluxo dos fluidos, comprometendo o retorno do investimento no projeto, caso sua espessura seja subavaliada. Esta questão se torna mais relevante no caso the camadas argilosas de pequena espessura. A solução para a caracterização de camadas finas é clássica: torna-se necessário aumentar a banda espectral do dado sísmico. O presente trabalho apresenta a metodologia e os primeiros resultados da incorporação de uma nova metodologia para geração de volumes de impedância de alta resolução. Nesta abordagem, os componentes de baixa e alta frequência do dado sísmico são recuperados através da aplicação de um processo de deconvolução iterativa (iterdec). Em seguida, este dado com banda espectral expandida é utilizado como entrada para uma inversão esparsa, resultando num volume de impedância acústica, que reduz as incertezas na interpretação de camadas argilosas de pouca espessura. Apresenta-se o estudo de caso de um reservatório do pre-sal para demonstrar a efetividade desta técnica na mitigação de risco associado ao posicionamento de um poço injetor, resultando na maximização da varredura de óleo em torno do poço. São apresentadas e discutidas as adaptações necessárias ao fluxo tradicional de inversão e condicionamento de dados sísmicos, bem como as vantagens da aplicação dessa metodologia sobre os resultados da inversão.Palavras-chave: Inversão, resolução, banda-larga, pre-sal.


Geophysics ◽  
2018 ◽  
Vol 83 (3) ◽  
pp. O55-O66 ◽  
Author(s):  
Yanting Duan ◽  
Chaodong Wu ◽  
Xiaodong Zheng ◽  
Yucheng Huang ◽  
Jian Ma

The eigenstructure-based coherence attribute is a type of efficient and mature tool for mapping geologic edges such as faults and/or channels in the 3D seismic interpretation. However, the eigenstructure-based coherence algorithm is sensitive to low signal-to-noise ratio seismic data, and the coherence results are affected by the dipping structures. Due to the large energy gap between the low- and high-frequency components, the low-frequency components play the principal role in coherence estimation. In contrast, the spectral variance balances the difference between the low- and high-frequency components at a fixed depth. The coherence estimation based on amplitude spectra avoids the effect of the time delays resulting from the dipping structures. Combining the spectral variance with the amplitude spectra avoids the effect of dipping structures and enhances the antinoise performance of the high-frequency components. First, we apply the short-time Fourier transform to obtain the time-frequency spectra of seismic data. Next, we compute the variance values of amplitude spectra. Then, we apply the fast Fourier transform to obtain the amplitude spectra of spectral variance. Finally, we calculate the eigenstructure coherence by using the amplitude spectra of spectral variance as the input. We apply the method to the theoretical models and practical seismic data. In the Marmousi velocity model, the coherence estimation using the amplitude spectra of the spectral variance as input shows more subtle discontinuities, especially in deeper layers. The results from field-data examples demonstrate that the proposed method is helpful for mapping faults and for improving the narrow channel edges’ resolution of interest. Therefore, the coherence algorithm based on the spectral variance analysis may be conducive to the seismic interpretation.


Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. V99-V118
Author(s):  
Yi Lin ◽  
Jinhai Zhang

Random noise attenuation plays an important role in seismic data processing. Most traditional methods suppress random noise either in the time-space domain or in the transformed domain, which may encounter difficulty in retaining the detailed structures. We have introduced the progressive denoising method to suppress random noise in seismic data. This method estimates random noise at each sample independently by imposing proper constraints on local windowed data in the time-space domain and then in the transformed domain, and the denoised results of the whole data set are gradually improved by many iterations. First, we apply an unnormalized bilateral kernel in time-space domain to reject large-amplitude signals; then, we apply a range kernel in the frequency-wavenumber domain to reject medium-amplitude signals; finally, we can obtain a total estimate of random noise by repeating these steps approximately 30 times. Numerical examples indicate that the progressive denoising method can achieve a better denoising result, compared with the two typical single-domain methods: the [Formula: see text]-[Formula: see text] deconvolution method and the curvelet domain thresholding method. As an edge-preserving method, the progressive denoising method can greatly reduce the random noise without harming the useful signals, especially to those high-frequency components, which would be crucial for high-resolution imaging and interpretations in the following stages.


Author(s):  
G. Y. Fan ◽  
J. M. Cowley

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.


2019 ◽  
Vol 14 (7) ◽  
pp. 658-666
Author(s):  
Kai-jian Xia ◽  
Jian-qiang Wang ◽  
Jian Cai

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.


Author(s):  
Priya R. Kamath ◽  
Kedarnath Senapati ◽  
P. Jidesh

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.


2019 ◽  
Vol 16 (6) ◽  
pp. 1017-1031 ◽  
Author(s):  
Yong Hu ◽  
Liguo Han ◽  
Rushan Wu ◽  
Yongzhong Xu

Abstract Full Waveform Inversion (FWI) is based on the least squares algorithm to minimize the difference between the synthetic and observed data, which is a promising technique for high-resolution velocity inversion. However, the FWI method is characterized by strong model dependence, because the ultra-low-frequency components in the field seismic data are usually not available. In this work, to reduce the model dependence of the FWI method, we introduce a Weighted Local Correlation-phase based FWI method (WLCFWI), which emphasizes the correlation phase between the synthetic and observed data in the time-frequency domain. The local correlation-phase misfit function combines the advantages of phase and normalized correlation function, and has an enormous potential for reducing the model dependence and improving FWI results. Besides, in the correlation-phase misfit function, the amplitude information is treated as a weighting factor, which emphasizes the phase similarity between synthetic and observed data. Numerical examples and the analysis of the misfit function show that the WLCFWI method has a strong ability to reduce model dependence, even if the seismic data are devoid of low-frequency components and contain strong Gaussian noise.


2015 ◽  
Vol 3 (1) ◽  
pp. SB5-SB15 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Tiago M. Alves

Seismic attributes are routinely used to accelerate and quantify the interpretation of tectonic features in 3D seismic data. Coherence (or variance) cubes delineate the edges of megablocks and faulted strata, curvature delineates folds and flexures, while spectral components delineate lateral changes in thickness and lithology. Seismic attributes are at their best in extracting subtle and easy to overlook features on high-quality seismic data. However, seismic attributes can also exacerbate otherwise subtle effects such as acquisition footprint and velocity pull-up/push-down, as well as small processing and velocity errors in seismic imaging. As a result, the chance that an interpreter will suffer a pitfall is inversely proportional to his or her experience. Interpreters with a history of making conventional maps from vertical seismic sections will have previously encountered problems associated with acquisition, processing, and imaging. Because they know that attributes are a direct measure of the seismic amplitude data, they are not surprised that such attributes “accurately” represent these familiar errors. Less experienced interpreters may encounter these errors for the first time. Regardless of their level of experience, all interpreters are faced with increasingly larger seismic data volumes in which seismic attributes become valuable tools that aid in mapping and communicating geologic features of interest to their colleagues. In terms of attributes, structural pitfalls fall into two general categories: false structures due to seismic noise and processing errors including velocity pull-up/push-down due to lateral variations in the overburden and errors made in attribute computation by not accounting for structural dip. We evaluate these errors using 3D data volumes and find areas where present-day attributes do not provide the images we want.


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