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Information Journal Paper

Title

Time-frequency analysis of seismic data by reassigned S-transform to detect low frequency shadows

Pages

  283-293

Abstract

 Summary Assessing a time-frequency representation of signal with an acceptable timefrequency resolution, and for specific purposes in different applied studies, has always been a challenge for signal processing researchers. In case of seismic data, using a time-frequency representation with high resolution will yield a higher precision in processing and interpretational applications of timefrequency analysis of data. In the most of Time-frequency analysis methods, a form of smoothing is used for generating time-frequency map, which it causes energy dissipation in time-frequency plane and decreasing the resolution. Reassignment is an efficient technique for compensating this issue and increasing the resolution. It can provide a high time-frequency resolution through moving and concentrating the energy distribution in the time-frequency plane to true location. Reassignment has been applied to various Time-frequency analysis methods and its performance has been presented in different researches. In this paper, the reassigned Stransform as a new development on S-transform to provide higher time and frequency resolution is utilized to extract some Seismic attributes. The performance of the method in providing an acceptable time-frequency resolution is shown by testing on synthetic non-stationary chirp and seismic signals. As a seismic application, the reassigned S-transform is utilized in studying Low frequency shadows through Time-frequency analysis of seismic data set acquired on a hydrocarbon reservoir. For this purpose, some time-frequency attributes including single-frequency, instantaneous amplitude, instantaneous dominant frequency and sweetness factor are extracted by this method. The results show that the reassigned S-transform can provide much higher energy concentration rather than standard S-transform, and the events and anomalies can be interpreted with more precision due to their better time and space resolution in attribute sections. Introduction The Time-frequency analysis methods are among the most common signal and image processing techniques in different applied fields of electric engineering, mechanical engineering, geoscience, etc. Time-frequency methods are employed in seismic data processing and interpretation applications for denoising, attenuation estimation, deconvolution, hydrocarbon detection, channels and faults visualization and so on. There are several Time-frequency analysis methods. One of the main reasons of developing new time-frequency methods is to reach higher time-frequency resolution. The Reassignment is one of the successful approaches in this field. The mission of Reassignment method (RM) is to move the energy distribution of the time-frequency plane to true location. Through this way, a precise distribution of instantaneous frequency has been provided for any time sample. Reassigning is also carried out in time direction. The RM has been applied in several time-frequency methods such as wavelet transform, Wigner-Ville distribution, Gabor transform and S-transform. In this paper, the reassigned Stransform has been studied in seismic data Time-frequency analysis. The method has been utilized for detection of Low frequency shadows in a seismic dataset to locate probable gas reservoir. Methodology and Approaches Auger et al. (2013) state that the RM can be applied on any time-frequency energy distribution in which a form of smoothing is applied in generating it, as seen in S transform. Therefore, Fourer et al. (2015) have introduced the reassigned S-transform. As mentioned above, the mission of the RM is to move the distributed energy in time-frequency plane to true coordinates. Then, it is needed to determine the true center coordinates of energy distribution, known as reassigning operators. Concerning the S-transform, the operators are computed as (Fourer et al., 2015): (, ) ˆ (, ) Re (, ) Tg x x ST t t t t ST t            (, ) ˆ (, ) Im (, ) Dg x x ST t t ST t              where ( ) ( ) Tg t tg t  , () () dg t Dg t dt  and Re and Im represent the real and imaginary parts of the arguments inside the parentheses. Then, the reassigned representation of S-transform is determined as: 2 2 (, ) (, ) ( ˆ (, )) ( ˆ (, )). g x x R RST t     ST t   t  t         d d where  represents Dirac function. However, in this paper, the reassigning process is implemented using the Levenberg-Marquardt approach, developed by Auger et al. (2012), in which a damping parameter could adjust the timefrequency concentration. Results and Conclusions In this paper, the performance of reassigned S-transform has been studied by its application on synthetic chirp signal and seismic trace. The results show that the method is capable of providing a well-concentrated time-frequency maps. As an application in real seismic data, the method has been utilized for studying the Low frequency shadows related to probable gas bearing zones. This approach extracts some attributes including single frequency, instantaneous amplitude, instantaneous dominant frequency and sweetness factor, through Time-frequency analysis of the data. The results show that the reassigned S-transform can provide higher time and space resolution, and thus, the events and anomalies can be interpreted more precise compared to standard S-transform results.

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    APA: Copy

    Radad, Mohammad. (2020). Time-frequency analysis of seismic data by reassigned S-transform to detect low frequency shadows. JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS, 5(2 ), 283-293. SID. https://sid.ir/paper/268620/en

    Vancouver: Copy

    Radad Mohammad. Time-frequency analysis of seismic data by reassigned S-transform to detect low frequency shadows. JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS[Internet]. 2020;5(2 ):283-293. Available from: https://sid.ir/paper/268620/en

    IEEE: Copy

    Mohammad Radad, “Time-frequency analysis of seismic data by reassigned S-transform to detect low frequency shadows,” JOURNAL OF RESEARCH ON APPLIED GEOPHYSICS, vol. 5, no. 2 , pp. 283–293, 2020, [Online]. Available: https://sid.ir/paper/268620/en

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