Multichannel matching pursuit for seismic trace decomposition

Abstract

The technique of matching pursuit can adaptively decompose a seismic trace into a series of wavelets. However, the solution is not unique and is also strongly affected by data noise. Multichannel matching pursuit (MCMP), exploiting lateral coherence as a constraint, might improve the uniqueness of the solution. It extracts a constituent wavelet that has an optimal correlation coefficient to neighboring traces, instead of to a single trace only. According to linearity theory, a wavelet shared by neighboring traces is the best match to the average of multiple traces, and therefore it might effectively suppress the data noise and stabilize the performance. It is found that the MCMP scheme greatly improves spatial continuity in decomposition and can generate a plausible time-frequency spectrum with high resolution for reservoir detection.