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Victor Koehne

Publications and source records attributed to Victor Koehne.

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3D Marchenko applications: implementation and examples

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 by 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.

physics.geo-ph

A multi-GPU benchmark for 2D Marchenko Imaging

The Marchenko method allows estimating Green's functions with a virtual source in the subsurface from a reflection response on the surface. It is an inverse problem that can be solved directly or by an iterative scheme, with the latter being more feasible computationally. In this work we present a multi-GPU implementation of a well-established iterative Marchenko algorithm based on (the) Neumann series. The time convolution and space integration performed on each iteration, also referred to as synthesis, are here represented as a segmented dot product, which can be accelerated on modern GPUs through the usage of warp-shuffle instructions and CUDA libraries. The original CPU version is benchmarked on 36 CPU cores versus the implemented version on 4 GPUs, over three different reflection data sets, with sizes ranging from 3 GB to 250 GB.

physics.geo-ph