Выпуск: 6 , Том: 18 , Год издания: 2021

Modern land seismic data are typically acquired using high spatial trace density with small source and receiver arrays or point sources and sensors. These datasets are challenging to process due to their massive size and relatively low signal-to-noise ratio caused by scattered near-surface noise. Therefore, prestack data enhancement becomes a critical step in the processing flow. Nonlinear beamforming had proved very powerful for 3D land data. However, it requires computationally intensive estimations of local coherency on dense spatial/temporal grids in 3D prestack data cubes. We present an analysis of various estimation methods focusing on a trade-off between computational efficiency and enhanced data quality. We demonstrate that the popular sequential "2 + 2 + 1" scheme is highly efficient but may lead to unreliable estimation and poor enhancement for data with a low signal-to-noise ratio. We propose an alternative algorithm called "dip + curvatures" that remains stable for such challenging data. We supplement the new strategy with an additional interpolation procedure in spatial and time dimensions to reduce the computational cost. We demonstrate that the "dip + curvatures" strategy coupled with an interpolation scheme approaches the "2 + 2 + 1" method's efficiency while it significantly outperforms it in enhanced data quality. We conclude that the new algorithm strikes a practical trade-off between the performance of the algorithm and the quality of the enhanced data. These conclusions are supported by synthetic and real 3D land seismic data from challenging desert environments with complex near surface.