Diffraction imaging using geometric-mean reverse time migration and common-reflection surface

We develop a new data-driven algorithm that uses directional elastic wavepackets as seismic sources to image subsurface voids (i.e., cavities). Compared with a point source, the advantage of the new approach is that the wavepacket illuminates only a small volume of the medium around the ray path to significantly reduce multiple scattering effects in imaging. We take the difference of traces at identical source-receiver offsets from each of two neighboring source packets. The difference contains mainly the void scattering events but not the direct waves, the layer reflections, refractions, nor layer-related multiples. We use both P-to-P and P-to-S scattered waves to locate the voids and the results using scattered P and S waves can cross-validate each other to reduce the possibility of false detections. The directional wavepacket can be numerically synthesized using existing shot gathers so no special physical source is required. We demonstrate our method using data calculated using a boundary element method to model the seismic wavefield in an irregularly layered medium containing several empty voids. We show the robustness of our method using the same data but with 15% RMS random noise added. Furthermore, we compare our method with the RTM imaging method using the same data and find that our method provides superior results that are not dependent on the construction of a velocity model.

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Separation and imaging of seismic diffractions using geometric mode decomposition

Lin

Xiang

et al. 2022

GEOPHYSICS

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Seismic diffractions from small-scale discontinuities or inhomogeneities carry key geologic information and can provide high-resolution images of these objects. Because diffractions characterized by weak energy are easily masked by strong reflections, diffraction-enhancement processing is essential before subwavelength information detection. Therefore, we propose a novel diffraction-separation method that uses the Fourier-based geometric-mode decomposition (GMDF) algorithm to remove reflections and separate diffractions in the common-offset or poststack domain. The key idea of the proposed method is that in the frequency-wavenumber ( f- k) domain, strong reflections concentrate linearly along a certain dip direction, whereas weak diffractions are distributed over a wide range of wavenumbers owing to their variable dips in the time-space domain. The GMDF algorithm can effectively represent reflections with directional and linear geometric features by adaptively decomposing seismic data as a combination of the band-limited modes consisting of linear characteristics in the f- k domain. The alternating direction method of the multipliers algorithm is used to solve the GMDF optimization problem and obtain linear reflections. Because this method considers the energy sparsity property and linear geometric features of reflections in the f- k domain, kinematic and dynamic differences between reflections and diffractions are exploited to separate diffractions. Applied synthetic and field examples demonstrate the good performance of the proposed method in removing strong reflections and separating weak diffractions, providing interpreters with detailed structural and stratigraphic information.

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Diffraction imaging using geometric-mean reverse time migration and common-reflection surface

Cited by 4 publications

References 33 publications

Systematic literature review on seismic diffraction imaging

Systematic literature review on seismic diffraction imaging

Mapping subsurface karsts and voids using directional elastic wave packets

Separation and imaging of seismic diffractions using geometric mode decomposition

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