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

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.

I. Introduction

Spectral estimation is a fundamental analysis technique of signal processing with applications in many areas, such as telecommunications, biomedicine, machine diagnostics, and geophysics. The frequency content of a signal can be provided by converting it into the frequency domain. One of the most important transforms to achieve this goal is the Fourier transform (FT) [1], [2]. The FT represents the signal as a superposition of the cosine and sine functions. In the case of signal processing such as seismic data analysis, the FT has shown its powerful and efficient performance. However, it does not provide the time locations of varying frequencies in the signal. The method of determining where certain frequencies are present or absent in the time domain is a significant and longstanding problem. These time-varying frequencies can be obtained using time–frequency analysis (TFA), also known as spectral decomposition, which can map 1-D time series into a 2-D space of TF [3]. Actually, many TFA methods have been designed for nonstationary signals analysis, such as the short-time Fourier transform (STFT) [3], [4], continuous wavelet transform (CWT) [5], and linear chirplet transform (LCT) [6].