Three Dimensional Parallel Sobel Seismic Fault Detection
A. Al-Naeem, S. Al-Dossary and Z. Zhou
Event name: Third EAGE Workshop on High Performance Computing for Upstream
Publication date: 01 October 2017
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The Sobel filter is a discrete differentiation operator widely used in seismic image processing algorithms for automatic fault detection and extraction. The filter approximates the local gradient by combining derivatives of the amplitude between neighboring traces along the x, y, and z directions. However, 3D-Sobel Seismic Fault Detection algorithm runs very slowly and is computationally intensive, even with dual Sandy Bridge Xeon 8-core 2.6 GHz CPU, it requires more than one minute to process a 560x390x320 seismic volume data. Herein we present a multiple parallel computing designs leveraging shared memory (OpnMp), distributed memory (MPI) and many-core Graphical processing Unit (GPU) to reduce total execution time of 3D-Soble Seismic Fault Detection; experiments demonstrate a significant speed up over serial CPU version. For a 250 MB poststack seismic volume, the speedup using the parallel algorithm with MPI was six times faster than the serial version of the algorithm. We first introduce the new 3D-Sobel seismic fault detection algorithm and the parallel implementations, and then show the experimental results.