3D Marchenko applications

implementation and examples

Journal Article (2022)
Author(s)

J. Brackenhoff (ETH Zürich, TU Delft - Applied Geophysics and Petrophysics)

Jan Thorbecke (TU Delft - Applied Geophysics and Petrophysics)

G.A. Meles (University of Lausanne, TU Delft - Applied Geophysics and Petrophysics)

Victor Koehne (SENAI CIMATEC)

Diego F. Barrera (SENAI CIMATEC)

C.P.A. Wapenaar (TU Delft - ImPhys/Medical Imaging, TU Delft - Applied Geophysics and Petrophysics)

Research Group
Applied Geophysics and Petrophysics
Copyright
© 2022 J.A. Brackenhoff, J.W. Thorbecke, G.A. Meles, Victor Koehne, Diego Barrera, C.P.A. Wapenaar
DOI related publication
https://doi.org/10.1111/1365-2478.13151
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 J.A. Brackenhoff, J.W. Thorbecke, G.A. Meles, Victor Koehne, Diego Barrera, C.P.A. Wapenaar
Research Group
Applied Geophysics and Petrophysics
Issue number
1
Volume number
70
Pages (from-to)
35-56
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Abstract

We implement the 3D Marchenko equations to retrieve responses to virtual sources inside the subsurface. For this, we require reflection data at the surface of the Earth that contain no free-surface multiples and are densely sampled in space. The required 3D reflection data volume is very large and solving the Marchenko equations requires a significant amount of computational cost. To limit the cost, we apply floating point compression to the reflection data to reduce their volume and the loading time from disk. We apply the Marchenko implementation to numerical reflection data to retrieve accurate Green's functions inside the medium and use these reflection data to apply imaging. This requires the simulation of many virtual source points, which we circumvent using virtual plane-wave sources instead of virtual point sources. Through this method, we retrieve the angle-dependent response of a source from a depth level rather than of a point. We use these responses to obtain angle-dependent structural images of the subsurface, free of contamination from wrongly imaged internal multiples. These images have less lateral resolution than those obtained using virtual point sources, but are more efficiently retrieved.