Enhancing 3-D Land Seismic Data Using Nonlinear Beamforming Based on the Efficiency-Improved Genetic Algorithm

Seismic data acquired in a desert environment often have a low signal-to-noise ratio, posing a significant challenge to seismic processing, imaging, and inversion. Nonlinear beamforming is one effective method that uses local second-order mathematical operators to describe seismic events and then enhance them. However, estimating the operator coefficients from input data is a nonlinear and compute-intensive optimization problem. We propose a new method for this estimation based on a recently developed efficiency-improved genetic algorithm. We demonstrate that it delivers an excellent balance between improved data quality and computational efficiency. We also introduce a “spatial consistency” feature to further speed up the algorithm by using available nonlinear-beamforming operators as initial trial solutions for the neighboring data ensembles. These findings are supported by applying the new approach to land seismic datasets from the desert environment.