Enhancing resolution of nonstationary seismic data by molecular-Gabor transform

Wang, Lingling; Gao, Jinghuai; Zhao, Wei; Jiang, Xiudi

doi:10.1190/geo2011-0450.1

Cited by 46 publications

(15 citation statements)

References 25 publications

(24 reference statements)

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“…Gao, W. Zhao and X.D. Jiang [11]. The self-adaptability manifests as the length of each segment adjusting automatically with the characteristics of seismic record.…”

Section: ) Piecewise Stationary Deconvolutionmentioning

confidence: 99%

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Zhang¹,

Dai²,

Zhang³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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Abstract-To adjust to the development of dynamic prediction of oil and gas reservoir, exploration of subtle reservoir, exploration and development of thin interbeds, processed seismic profiles are required to be of high signal to noise ratio, high resolution and high fidelity. While the time-varying wavelet in seismic data is an important factor affecting the quality of seismic profiles, for the purpose of improving the effects of seismic data processing, the influence law of wavelet time-varying nature is necessarily to be analyzed and researched, so that the timevarying wavelet can be suppressed based on more targeted methods. In the paper, the formation mechanism of time-varying nature is studied, and the influence law of time-varying wavelet is analyzed in detail, which is different in various links of seismic data processing, including amplitude compensation, pre-stack deconvolution, post-stack deconvolution and migration. Meanwhile the methods and application effects for suppressing wavelet time-varying nature in every link are summarized, and the advantages and disadvantages of these approaches are concluded through analysis and comparison, so that the development direction of researching more effective methods for suppressing or eliminating wavelet time-varying nature in the future is pointed out.

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“…Gao, W. Zhao and X.D. Jiang [11]. The self-adaptability manifests as the length of each segment adjusting automatically with the characteristics of seismic record.…”

Section: ) Piecewise Stationary Deconvolutionmentioning

confidence: 99%

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Zhang¹,

Dai²,

Zhang³

et al. 2015

Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication

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“…Inappropriate truncation leads to weak focusing ability, failure to detect details and low resolution (Wang et al . ). In reality, obtaining a suitable time local series is important due to the given variations in the subsurface medium.…”

Section: Introductionmentioning

confidence: 97%

Coherence algorithm with a high‐resolution time–time transform and feature matrix for seismic data

Sun

Gao

Zhang

et al. 2020

Geophysical Prospecting

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A B S T R A C TTraditional coherence algorithms are often based on the assumption that seismic traces are stationary and Gaussian. However, seismic traces are actually non-stationary and non-Gaussian. A constant time window and the canonical correlation analysis in traditional coherence algorithms are not optimal for nonstationary seismic traces and cannot describe the similarity between adjacent seismic traces in detail. To overcome this problem, a new coherence algorithm using the high-resolution time-time transform and the feature matrix is designed. The high-resolution time-time transform used to replace the constant time window can produce a frequency-dependent time local series to analyse non-stationary seismic traces. The feature matrix, constructed by the frequency-dependent time local series and the related local gradients, defines a new correlation metric that enhances more details of the geological discontinuities in seismic images than does the canonical correlation analysis. Additionally, the Riemannian metric is introduced for related calculations because the feature matrices are not defined in a Euclidean space but rather in a manifold space. Application to field data illustrates that the proposed method reveals more details of structural and stratigraphic features.

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“…Gabor deconvolution (Margrave et al, 2011) first estimates a time-and frequency-dependent Gabor deconvolution operator formed from the Gabor transform of a seismic trace by an iterative smoothing process, then it estimates the Gabor transform of the reflectivity by spectral division in the time-frequency domain, and it finally uses an inverse Gabor transform to recover the time-domain reflectivity. Subsequently, Wang et al (2013) perform spectrum broadening and amplitude compensation after molecular Gabor transform. The inverse molecular Gabor transform is also needed to obtain a high-resolution, nonstationary seismic trace.…”

Section: Introductionmentioning

confidence: 99%

Seismic data deconvolution using Kalman filter based on a new system model

2016

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Seismic resolution plays an important role in geologic interpretation and reservoir prediction. To improve the vertical resolution of a seismic image, we have developed a new Kalman filter system model for seismic deconvolution. Similar to the conventional Kalman filter model for seismic deconvolution, our new Kalman model is also based on the common viewpoint that a reflected seismic record can be regarded as a convolution of a seismic wavelet with a reflection coefficient series. The new model uses a reversed seismic wavelet to slide across a reflectivity function to achieve the convolution result, instead of using a reversed reflectivity function to slide across a seismic wavelet in the conventional Kalman filter model. A simpler state equation for the new model is achieved, and the number of parameters to select is fewer than the conventional. Furthermore, the number of parameters can be reduced to only one by a theoretical demonstration for stationary noisy signals, which decreases the requirement for multiple parameters selection in the conventional model. The practical selection for this parameter should be a compromise between resolution improvement and noise amplification. Experimental results in the time and frequency domains on synthetic and field seismic records revealed that the Kalman filter based on the new model has the advantages of a higher resolution and peak signal-to-noise ratio (PS/N) than the conventional Kalman filter for stationary and nonstationary signals, and it works similarly to the Wiener filter for stationary signals, and it is superior to the Wiener filter in resolution and PS/N for nonstationary signals. The Kalman filter based on the new model can be applied to seismic resolution improvement.

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Enhancing resolution of nonstationary seismic data by molecular-Gabor transform

Cited by 46 publications

References 25 publications

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Research on the Influence Law and Suppression Methods of Seismic Wavelet Time-varying Nature on Seismic Data Processing

Coherence algorithm with a high‐resolution time–time transform and feature matrix for seismic data

Seismic data deconvolution using Kalman filter based on a new system model

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