Adaptive curvelet-domain primary-multiple separation

Author:

Herrmann Felix J.123,Wang Deli123,Verschuur Dirk J. (Eric)123

Affiliation:

1. University of British Columbia, Department of Earth and Ocean Sciences, Seismic Laboratory for Imaging and Modeling, Vancouver, Canada. .

2. Jilin University, College of Geoexploration Science and Technology, Changchun, China. Visiting the University of British Columbia. .

3. Delft University of Technology, Department of Applied Physics, Laboratory of Acoustical Imaging and Sound Control. .

Abstract

In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function of offset, time, and dip), they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band), and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries.

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

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