Abstract
As one of the important seismic attributes, instantaneous frequency is widely used to reveal geological features. Compared to the normally instantaneous frequency extraction using Hilbert transform (HT), instantaneous frequency extraction using the wavelet ridge is demonstrated to have a more robust characteristic and anti-noise feature. Due to instantaneous frequency extraction using the wavelet ridge is suitable for the gradual single-frequency signal, to get a meaningful instantaneous attributes Empirical Mode Decomposition (EMD) is applied to the seismic signal first to decompose each seismic trace into the IMFs which are single-frequency signal. Application of the simulated signals and the seismic trace show that instantaneous frequency extraction using the EMD-based wavelet ridge is more accurate than HT does. Application of the EMD-based wavelet ridge method in a gas field located in PengLai, Central Sichuan(China), shows its effectiveness. Instantaneous frequency extraction using the EMD-based wavelet ridge method can be used as a strong tool to detect the instantaneous spectral properties of a reservoir to reveal hydrocarbon related strong amplitude anomaly and reservoir thickness variation and lateral changes information.
Highlights
Frequency components of a seismic signal are mainly determined by the bandwidth of the source pulse and the absorption characteristics of the subsurface medium
Since instantaneous frequency (IF) extraction using wavelet ridge only use for a gradual single-frequency signal, we introduce the Empirical Mode Decomposition (EMD) method (Huang et al, 1998) to decompose the multiple seismic signal into Intrinsic Mode Functions (IMFs) which are gradual single-frequency signals first
By extending IF extraction of seismic signal using the wavelet ridge, the EMD based wavelet ridge is demonstrated to be a FIGURE 8 | the IFs of the first two IMFs extracted by the wavelet ridge and Hilbert transform (HT) respectively. (A) The IF of the IMF1. (B) The IF of the IMF2
Summary
Frequency components of a seismic signal are mainly determined by the bandwidth of the source pulse and the absorption characteristics of the subsurface medium Factors such as fluidcontaining, thickness variation, and lateral changes and so on in the formation all can cause changes in frequency components of a seismic signal (Chopra and Marfurt, 2005; Zeng, 2010; Zhou et al, 2012). There exist many IF extraction methods for seismic data such as complex trace analysis (Taner et al, 1979), IF extraction based on time-frequency method (e.g., Hardy et al, 2003; Huang and Milkereit, 2009; Han and van der Baan, 2011; Fomel, 2013) It is still a very hot research topic to seek the IF calculation method and improve the IF estimation precision and make the IF physical interpretation clearly and further explore its various applications
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