Accelerating the 2+2+1 method for estimating local traveltime operators in nonlinear beamforming using GPU graphics cards

Abstract

Abstract Local traveltime operators are an effective way to describe local kinematic wavefronts. They are useful for many applications. One of them is nonlinear beamforming for enhancing the signal-to-noise ratio of challenging seismic data. The so-called 2+2+1 method is a pragmatic approach to estimate unknown local traveltime operators from input data. However, its efficiency still has much room for improvement when the solution space is big. We accelerate the 2+2+1 method using graphics processing unit (GPU) computing with the Compute Unified Device Architecture (CUDA) programming language. We detail the CPU- and GPU-based 2+2+1 search algorithms and demonstrate the efficiency improvement using synthetic and field data examples. Compared to a standard multi-core CPU implementation, our new GPU implementation achieves almost the same quality results at only ∼10% run-time cost.