4.6 Article

Enhancement of diffractions in prestack domain by means of a finite-offset double-square-root traveltime

期刊

GEOPHYSICS
卷 84, 期 1, 页码 V81-V96

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SOC EXPLORATION GEOPHYSICISTS
DOI: 10.1190/GEO2018-0160.1

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  1. National Council for Scientific and Technological Development (CNPq-Brazil)
  2. National Institute of Science and Technology of Petroleum Geophysics (INCT-GP-Brazil)
  3. Center for Computational Engineering and Sciences (Fapesp/Cepid) Brazil [2013/08293-7]
  4. Wave Inversion Technology Consortium

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Exploration of redundancy contained in the seismic data set assures enhancement of images that are based on stacking results. This enhancement is the goal of developing multiparametric traveltime equations that are able to approximate reflection and diffraction events in general source-receiver configurations. The main challenge of using these equations is to estimate a large number of parameters in a computationally feasible, reliable, and fast way. To obtain a better fit for diffraction traveltime events than the ones in the literature, we have derived a finite-offset (FO) double-square-root (DSR) diffraction traveltime equation (which depends on 10 parameters in three dimensions and four parameters in two dimensions). Moreover, to reduce the number of parameters, we have developed another version called simplified FO-DSR diffraction traveltime equation (which depends on five parameters in three dimensions and two parameters in two dimensions), which delivers a similar performance. We have developed operators that make use of the simplified FO-DSR traveltime equation to construct the so-called diffraction-only data set volumes (or, more simply, D-volumes) assuring enhancement in the diffraction extraction process. The D-volume construction has two steps: first, a stacking procedure to separate the diffraction events from the input data set and second, a spreading procedure to enhance the quality of these diffractions. As proof of concept, our approach has been tested on 2D/3D synthetic and 2D field data sets with successful results.

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