Synchroextracting Transform

Yu, Gang; Yu, Mingjin; Xu, Chuanyan

doi:10.1109/tie.2017.2696503

Cited by 486 publications

(185 citation statements)

References 22 publications

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“…Furthermore, we use the synchroextracting operator (SEO; Yu et al . ) to extract the time–frequency coefficients with the maximum values to obtain a new time–frequency distribution. The operator

δ (f - f_{x} (f, τ))

is formulated as

δ (f - f_{x} (f, τ)) = \{\begin{matrix} left 1, \begin{matrix}  \end{matrix} f = f_{x} (f, τ) \\ left 0, \begin{matrix}  \end{matrix} f \neq f_{x} (f, τ) \end{matrix} .

…”

Section: The Theory Of Synchroextracting Generalized S‐transformmentioning

confidence: 99%

A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

Chen

Qian

et al. 2019

Geophysical Prospecting

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A B S T R A C TImproving the seismic time-frequency resolution is a crucial step for identifying thin reservoirs. In this paper, we propose a new high-precision time-frequency analysis algorithm, synchroextracting generalized S-transform, which exhibits superior performance at characterizing reservoirs and detecting hydrocarbons. This method first calculates time-frequency spectra using generalized S-transform; then, it squeezes all but the most smeared time-frequency coefficients into the instantaneous frequency trajectory and finally obtains highly accurate and energy-concentrated time-frequency spectra. We precisely deduce the mathematical formula of the synchroextracting generalized S-transform. Synthetic signal examples testify that this method can correctly decompose a signal and provide a better time-frequency representation. The results of a synthetic seismic signal and real seismic data demonstrate that this method can identify some reservoirs with thincknesses smaller than a quarter wavelength and can be successfully applied for hydrocarbon detection. In addition, examples of synthetic signals with different levels of Gaussian white noise show that this method can achieve better results under noisy conditions. Hence, the synchroextracting generalized Stransform has great application prospects and merits in seismic signal processing and interpretation.

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δ (f - f_{x} (f, τ))

is formulated as

δ (f - f_{x} (f, τ)) = \{\begin{matrix} left 1, \begin{matrix}  \end{matrix} f = f_{x} (f, τ) \\ left 0, \begin{matrix}  \end{matrix} f \neq f_{x} (f, τ) \end{matrix} .

…”

Section: The Theory Of Synchroextracting Generalized S‐transformmentioning

confidence: 99%

A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

Chen

Qian

et al. 2019

Geophysical Prospecting

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show abstract

“…To quantify the performance of the SSGST and the other algorithms for generating TF representations of synthetic examples, we choose Rényi Entroy [30] as a quantitative performance index. The Rényi entropy is an objective indicator to evaluate the energy concentration of a time-frequency result; a lower Rényi entropy value denotes a more energy-concentrated time-frequency representation.…”

Section: Comparative Quantification and Reconstruction Analysismentioning

confidence: 99%

The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

Chen

et al. 2017

Applied Sciences

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Abstract:In this paper, a new time-frequency analysis method-Synchrosqueezing Generalized S-transform (SSGST)-is proposed to meet the needs of high-resolution seismic signal processing and interpretation. The basic wavelet of the generalized S-transform (GST) in the paper is a modulated harmonic wave with four undetermined parameters that can be constructed by adjusting the four parameters to make the GST more suitable for seismic signals processing. The SSGST method squeezes and reconstructs the complex coefficient spectra of GST results along the frequency direction so that the energy distributions on the time-frequency spectra are concentrated around the real instantaneous frequency of the signal; thus, the time-frequency resolution can be improved. Based on mathematical theory, the basic principle of the new transformation method is given, and the mathematical expressions of the positive transformation and lossless inverse transformation of the method are strictly deduced. The experimental results of numerical signals illustrate that the proposed method can correctly decompose signals with different spectral characteristics into a high time-frequency resolution spectrum and can recovery the original signal from the time-frequency spectrum with satisfying reconstructing accuracy. Application on field seismic data shows the superiority of the new method in seismic time-frequency analysis for hydrocarbon detection.

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“…SET is a new time-frequency analysis method proposed by Yu in 2017 [25], which can be regarded as a post-processing procedure of STFT. The greatest advantage of SET is that this algorithm can obtain a more energy-concentrated time-frequency representation compared with classical time-frequency analysis methods through only obtaining the time-frequency information of STFT results most related to time-varying features of the target signal.…”

Section: Synchroextracting Transform (Set)mentioning

confidence: 99%

“…In 2017, inspired by the recently proposed SST and the theory that the signal energy of ideal time-frequency analysis should only appear in the instantaneous frequency trajectory, Yu et al [25] proposed a novel time-frequency analysis method called synchroextracting transform (SET). Differing from the main idea of SST, which squeezes all time-frequency coefficients into the instantaneous frequency trajectory, SET removes the most-smeared time-frequency energy and only retains the time-frequency information from the STFT results most related to the time-varying features of the target signal, thus greatly enhancing the energy concentration [25].…”

Section: Introductionmentioning

confidence: 99%

“…Differing from the main idea of SST, which squeezes all time-frequency coefficients into the instantaneous frequency trajectory, SET removes the most-smeared time-frequency energy and only retains the time-frequency information from the STFT results most related to the time-varying features of the target signal, thus greatly enhancing the energy concentration [25].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Time-Frequency Analysis Method for Hydrocarbon Detection Based on EWT and SET

et al. 2017

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Oil and gas reservoirs can cause increased attenuation of seismic waves, which can be revealed by time-frequency analysis for direct detection of hydrocarbons. In this paper, a new method applying the empirical wavelet transform (EWT) in association with the synchroextracting transform (SET), named EWT-SET, is proposed as an improved time-frequency analysis method for hydrocarbon detection. The SET is a novel time-frequency analysis method which can be considered as a post-processing procedure of short-time Fourier transform and can improve the energy concentration of the time-frequency representation by retaining only the time-frequency information most related to the signal time-varying features. Given the potential limitations of SET for broadband nonstationary seismic signals, using the EWT-SET method which applies SET to the signal after EWT decomposition, not only effectively extracts time-varying features of seismic signals but also improves the performance of SET in concentrating instantaneous energy. The preliminary model tests demonstrate that EWT-SET can effectively depict the location and extent of attenuation anomalies related to hydrocarbons with changing thicknesses of the gas-bearing layer. Application to field data further confirms the capacity for hydrocarbon detection of the presented method. Thus, the EWT-SET method shows significant application prospects and promotion value for hydrocarbon detection.

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Synchroextracting Transform

Cited by 486 publications

References 22 publications

A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

A high‐precision time–frequency analysis for thin hydrocarbon reservoir identification based on synchroextracting generalized S‐transform

The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis

An Improved Time-Frequency Analysis Method for Hydrocarbon Detection Based on EWT and SET

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