Effective velocities artifacts caused by floating datum and compensation methods

Seismic data processing from a floating datum is accompanied by difficulties in estimating effective velocities. These difficulties are associated with the roughness of the datum surface, which, if ignored, leads to artifacts in the estimated effective velocities. The study presents the results of a quantitative analysis of the distortion of effective velocities with model data, as well as the technique to minimize the distorting effect of the elevation on effective velocities. The essence of the method is bringing the sources and receivers within one CDP to a local constant level. This approach has been tested on modeled and real data. It showed a significant reduction in the effect of floating level roughness on kinematic parameters. At the same time, there is no need to modify the processing flow whatsoever.

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Optimization of seismic data processing flow for tunnel detection using energy spectrum and stacking spectral energy peaks of overlapping arrays

10.3997/2214-4609-pdb.157.sageep047 ◽

2009 ◽

Author(s):

O. Kovin ◽

N. Putnam ◽

E. Torgashov

Keyword(s):

Energy Spectrum ◽

Data Processing ◽

Seismic Data ◽

Spectral Energy ◽

Seismic Data Processing ◽

Tunnel Detection ◽

Processing Flow

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Image-guided raytracing and its applications

Geophysics ◽

10.1190/geo2020-0642.1 ◽

2021 ◽

pp. 1-44

Author(s):

Eduardo Silva ◽

Jessé Costa ◽

Jörg Schleicher

Keyword(s):

Data Processing ◽

Seismic Data ◽

Eikonal Equation ◽

Real Data ◽

Data Sets ◽

Seismic Data Processing ◽

Fast Marching ◽

Image Guided ◽

Ray Density ◽

Original Motivation

Eikonal solvers have found important applications in seismic data processing and in-version, the so-called image-guided methods. To this day in image-guided applications, thesolution of the eikonal equation is implemented using partial-differential-equationsolvers, such as fast-marching or fast-sweeping methods. We show that alternatively, onecan numerically integrate the dynamic Hamiltonian system defined by the image-guidedeikonal equation and reconstruct the solution with image-guided rays. We present interest-ing applications of image-guided raytracing to seismic data processing, demonstrating theuse of the resulting rays in image-guided interpolation and smoothing, well-log interpola-tion, image flattening, and residual-moveout picking. Some of these applications make useof properties of the raytracing system that are not directly obtained by eikonal solvers, suchas ray position, ray density, wavefront curvature, and ray curvature. These ray propertiesopen space for a different set of applications of the image-guided eikonal equation, beyondthe original motivation of accelerating the construction of minimum distance tables. Westress that image-guided raytracing is an embarrassingly parallel problem, which makes itsimplementation highly efficient on massively parallel platforms. Image-guided raytracing isadvantageous for most applications involving the tracking of seismic events and imaging-guided interpolation. Our numerical experiments using synthetic and real data sets showthe efficiency and robustness of image-guided rays for the selected applications.

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