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Data-driven Gaussian Beam Migration Based on Local Similarity Analysis
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 77th EAGE Conference and Exhibition 2015, Jun 2015, Volume 2015, p.1 - 5
Abstract
Gaussian beam migration is an efficient, flexible and robust depth imaging method, which could image multiple arrivals accurately and has no steep-dip limitation. But like Kirchhoff migration, it also maps the reflection energy to the travel-time ellipse isochrones, which produces lots of coherence noise and swing artifacts, especially for sparse acquired seismic data. Based on the local similarity analysis of reflection events, we present a data-driven Gaussian beam migration method in this paper. Through the local similarity analysis in common-shot and common-receiver gathers, the instantaneous ray parameters of specular reflection at shot-point and receiver position are exacted from original seismic records and can be converted principal ray emergence angles. Using these information and Fresnel zone radius along central ray, we can construct a quality control coefficient in Gaussian beam migration to suppress coherence noise and improve image quality. Typical numerical examples and the field data processing demonstrate the validity and adaptability of our method.