Image-guided ray tracing and its applications

Abstract

Eikonal solvers have important applications in seismic data processing and inversion, the so-called image-guided methods. To this day, in image-guided applications, the solution of the eikonal equation is implemented using partial-differential-equation solvers, such as fast-marching or fast-sweeping methods. We have found that alternatively, one can numerically integrate the dynamic Hamiltonian system defined by the image-guided eikonal equation and reconstruct the solution with image-guided rays. We evaluate interesting applications of image-guided ray tracing to seismic data processing, demonstrating the use of the resulting rays in image-guided interpolation and smoothing, well-log interpolation, image flattening, and residual-moveout picking. Some of these applications make use of properties of the ray-tracing system that are not directly obtained by eikonal solvers, such as ray position, ray density, wavefront curvature, and ray curvature. These ray properties open space for a different set of applications of the image-guided eikonal equation, beyond the original motivation of accelerating the construction of minimum distance tables. We stress that image-guided ray tracing is an embarrassingly parallel problem that makes its implementation highly efficient on massively parallel platforms. Image-guided ray tracing is advantageous for most applications involving the tracking of seismic events and imaging-guided interpolation. Our numerical experiments using synthetic and real data sets indicate the efficiency and robustness of image-guided rays for the selected applications.