Abstract
Diffracted seismic waves may be used to help identify and track geologically heterogeneous bodies or zones. However, the energy of diffracted waves is weaker than that of reflections. Therefore, the extraction of diffracted waves is the basis for the effective utilization of diffracted waves. Based on the difference in travel times between diffracted and reflected waves, we developed a method for separating the diffracted waves via singular value decomposition filters and presented an effective processing flowchart for diffracted wave separation and imaging. The research results show that the horizontally coherent difference between the reflected and diffracted waves can be further improved using normal move-out (NMO) correction. Then, a band-rank or high-rank approximation is used to suppress the reflected waves with better transverse coherence. Following, separation of reflected and diffracted waves is achieved after the filtered data are transformed into the original data domain by inverse NMO. Synthetic and field examples show that our proposed method has the advantages of fewer constraints, fast processing speed and complete extraction of diffracted waves. And the diffracted wave imaging results can effectively improve the identification accuracy of geological heterogeneous bodies or zones.
Highlights
Accurate identification of faults, thin-outs, karsts, lens bodies, collapse columns, fracture zones and other heterogeneous regions is one of the important, though difficult, goals of seismic data processing and interpretation (Yilmaz 2001; Khaidukov et al 2004)
The singular value decomposition (SVD) filter is a method based on the difference in the lateral coherence between different signals to achieve seismic wave field separation and denoising
To achieve thorough processing of the diffracted waves, we presented a complete set of diffracted wave separation and imaging processing flowcharts (Fig. 2)
Summary
Thin-outs, karsts, lens bodies, collapse columns, fracture zones and other heterogeneous regions is one of the important, though difficult, goals of seismic data processing and interpretation (Yilmaz 2001; Khaidukov et al 2004). Lin et al (2020) developed a method based on the multichannel singular-spectrum analysis algorithm to suppress time-linear signals (reflections) and separate weaker time-nonlinear signals (diffractions) in the common-offset or poststack domain. The SVD filter is a method based on the difference in the lateral coherence between different signals to achieve seismic wave field separation and denoising. Because of the difference in travel time between diffracted and reflected waves, SVD filters should be applied to the separation of reflected and diffracted waves. Based on this understanding, we developed a method to separate diffracted waves via SVD filters. The extracted diffracted waves can be used for the identification and tracking of geological heterogeneous bodies (zones) after migration imaging
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