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Abstract

Summary

Diffraction imaging has proven to be an attractive method for delivering high-resolution subsurface images containing different types and scales of continuous and discontinuous geometrical objects. For depth domain 3D subsurface models, described an imaging method which is based on the ability to decompose the full recorded seismic wavefield into continuous full-azimuth directivity components in situ at the subsurface image points. This method follows the concept of imaging and analysis in the “Local Angle Domain” and allows us to generate azimuthal directivity gathers, from which we can separate specular and diffracted energies.

As part of the ongoing effort to automatically enhance procedures for classifying directivity driven image data into N geometrical features such as continuous reflectors, faults, point diffractors, acquisition noise, and ambient noise, presented a Deep Learning (DL) approach to this challenging task. This work expands on this method, as in addition to vertical section image patches, we also train the net with horizontal patches. This leads to further improvements, particularly in areas masked by ambient and coherency noise for classifying different geometrical features. We demonstrate our method on seismic data from the Eagle Ford and Barnett unconventional shale plays.

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/content/papers/10.3997/2214-4609.202032023
2020-11-30
2024-03-29
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References

  1. Itan, L., Serfaty, Y., Chase, D. and Koren, Z.
    [2017] Wavefield separation using pca and deep learning in the local angle domain. SEG Technical Program Expanded Abstracts 2017: pp. 991–995.
    [Google Scholar]
  2. KorenZ. and Ravve, I.
    [2011] Full azimuth subsurface angle domain wavefield decomposition and imaging Part 1 and 2. Geophysics, 76 (1–2).
    [Google Scholar]
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