Fast and Parallel Semblance Algorithm for Detecting Faults in Large Seismic Volumes

Seismic fault detection has become an important research topic in geo-science. Semblance-based coherence algorithm is widely used to detect seismic faults, folds, and fractures. However, the algorithm is computationally expensive on Central Processing Unit (CPU) when the seismic datasets are too large. Also, existing commercial geoscience software solutions use serial or batch processing modes using CPU-based computation which leads to a long execution time. In this paper, we present a fast and parallel implementation of semblance algorithm using General Purpose Graphical Processing Unit (GPGPU) powered with Compute Unified Device Architecture (CUDA). This is accomplished with a parallel kernel map of the algorithm through multiple threads. We also adopted a strategy for efficient memory occupancy and CPU-GPU communication with minimal latency. The algorithm is implemented on NVIDIA RTX 4000 GPU model with 8GB dedicated GPU memory and tested with Netherland F3 and Indian Krishna-Godavari (KG) Basin datasets. Our CUDA implementation achieved considerable speedup over its conventional CPU implementation on both datasets. Also, the proposed algorithm achieves faster times speedup are reported on both datasets over the commercial software OpenDtect. For extensive study, optimal runtime of the algorithm with variation of the parallel threads is also reported here.