Time-Synchroextracting General Chirplet Transform for Seismic Time-Frequency Analysis

• Published in: IEEE transactions on geoscience and remote sensing Vol. 58; no. 12; pp. 8626 - 8636
• Main Authors: Li, Zhen, Gao, Jinghuai, Wang, Zhiguo, Liu, Naihao, Yang, Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Chirp; Continuous wavelet transforms; Feature extraction; Frequency analysis; Frequency dependence; Frequency modulation; Information processing; Localization; Offshore; Oil reservoirs; Sea surface; Seismic analysis; Seismic data; Seismic interpretation; seismic processing; Signal processing; Signal resolution; Signal to noise ratio; Spectrum analysis; Surface temperature; synchrosqueezing transform (SST); Time-frequency analysis; time-frequency analysis (TFA); time-synchroextracting general chirplet transform (TEGCT)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.2989403

Synchrosqueezing transform (SST) is an effective time-frequency analysis (TFA) approach for the processing of nonstationary signals. The SST shows a satisfactory ability of the TF localization of the nonlinear signal with a slowly time-varying instantaneous frequency (IF). However, for the signal of which ridge curves in the TF domain are fast varying, or even almost parallel to the frequency axis, the SST will provide a blurred TF representation (TFR). To solve this issue, the transient-extracting transform (TET) was recently put forward. The TET can effectively characterize and extract transient features in the much concentrated TFR for the strongly frequency-modulated (FM) signal, especially the impulse-like signal. However, contrary to the SST, it is not suitable for weak FM modes. In this study, we propose a TFA method called the time-synchroextracting general chirplet transform (TEGCT). The TEGCT can achieve a highly concentrated TFR for strong FM signals as well as weak FM ones. Quantized indicators, the concentration measurement and the peak signal-to-noise ratio, are used to analyze the performances of the proposed method compared with those of other methods. The comparisons show that the TEGCT can provide a result with better TF localization. Then, the proposed method was applied to the spectrum analysis of the seismic data for oil reservoir characteristics. The horizontal slices of the offshore 3-D seismic data show that the TEGCT delineates more distinct and continuous subsurface channels in a fluvial-delta deposition system. All the results illustrate that our proposed method is a good potential tool for seismic processing and interpretation in the geoscience.