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Using Principal Component Analysis to Decouple Seismic Diffractions from Specular Reflections

October 14, 2020
This presentation is now available for viewing.

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This presentation was offered as part of the Technical Program at the SEG 2020 Annual Meeting.

Presented by:  Raanan Dafni (presenter), Rina Schwartz, Ronit Levy and Zvi Koren
Featured Domain:  Processing and Imaging
Featured Product: EarthStudy 360 Diffraction Imaging

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Abstract

Methods developed for diffraction imaging are commonly based on a separation technique between continuous and discontinuous structural elements, based on their seismic response. Seismic imaging in the dip-angle domain decomposes direction-dependent images where the response of different structural elements is clearly distinguished. The subsurface structural geometry dictates a local dip-angle seismic signature of preferable scattering directions, accordingly. Assuming these signatures are uncorrelated, we propose a practical workflow for dip-angle domain principal component analysis as a comprehensive structural feature separator. Once the dip-angle images are transformed into their principal components, a back projection yields structural-specific images of the subsurface main building blocks. Our proposal emphasizes the strength of principal component analysis as a producer of probable geologic features from pre-stack dip-angle data. These should be incorporated as structural attributes to enhance seismic interpretation, reduce uncertainty and enable automation.


Biography

Dr. Raanan Dafni is R&D Project Manager, Seismic Imaging in the Emerson E&P Software group.  Among other interests, his research expertise includes seismic processing and inversion, high-performance computing, automatic interpretation, and machine learning.  He holds a PhD in Applied Geophysics from Tel-Aviv University, and served as a postdoctoral research associate in Rice University, Houston. He is the author or co-author of numerous peer-viewed articles.