Seismic speckle noise is the primary factor causing severe reflection distortions caused by small-scale near-surface scattering. As in optics and acoustics speckle, deterministic velocity model-building techniques cannot recover these heterogeneities which are much smaller than a wavelength. Conventional processing techniques struggle to perform if multiplicative noise remains unsuppressed. Although local and global stacking mitigates the effects of speckle noise, it leads to a severe loss of higher frequencies reducing the vertical resolution of the seismic data. The foundation for attacking speckle noise is a recently proposed mathematical model that includes two concurrent random multiplicative noise types: type 1 defining residual statics and type 2 describing random frequency-dependent phase perturbations that mimic small-scale near-surface scattering. Using this model, we have developed seismic time-frequency masking to suppress speckle noise on prestack data. The time-dependent and nonsurface-consistent nature of scattering noise dictates window-based approaches borrowed from local stacking techniques. Separate manipulation of phase and amplitude spectra is achieved through modified time-frequency masking inspired by speech processing. A beamformed data set from local stacking is used as a guide for designing phase and amplitude masks that are directly applied to raw data in the time-frequency domain. The phase mask allows the restoration of coherency by repairing phase distortions caused by near-surface scattering. The amplitude mask corrects power spectrum (PS) distortions caused by multiplicative and additive noises. An amplitude mask is implemented using a data-driven minimum statistic approach that estimates the noise PS in each local window. The minimum statistics approach is adapted from speech processing using beamformed data as an initial signal estimate and as a guide to designing an improved amplitude mask. Synthetic and field data examples suggest significant improvements in coherency and signal-to-noise ratio after the suppression of multiplicative noise, which makes the data processable by conventional techniques afterward.
