High-resolution seismic imaging requires noise attenuation to achieve signal-to-noise ratio (S/N) improvements without compromising data bandwidth. Amplitude versus offset analysis requires good amplitude fidelity in premigration processes. Any nonreflected wavefield energy in the data will degrade the seismic image quality. Despite significant progress over the years, preserving low-frequency signals without compromising the S/N and avoiding the smearing of aliased signal are still a challenge for conventional methods. This problem is compounded when additional interference noise is added with simultaneous source acquisition. Because noise characteristics vary from shot to shot and receiver to receiver, we need a method that is robust and effective. In addition, we also want the method to be efficient and easy to use from a practical perspective. We have recently developed an approach using a wavelet transform to deterministically separate the primary signal from the noise, including simultaneous source interference. The goals are (1) improving the S/N without compromising bandwidth, (2) preserving the low-frequency and near-offset primaries without compromising the S/N, and (3) preserving the local primary wavefield while attenuating noise. For distance-separated simultaneous source acquisition, the goal is preserving long-offset primaries while removing interference. This wavelet denoising flow consists of a linear transformation and filtering using the complex wavelet transform (CWT). For reflection signals, normal moveout (NMO) is used. NMO transforms the low-velocity surface waves and the interference noise to where it is easily identified and rejected with a dip filter in the multidimensional CWT domain. Land field data examples have demonstrated significantly improved S/Ns and low-frequency signal preservation in migrated images after wavelet denoising. Since the numerical implementation of the CWT is as fast as a fast Fourier transform, this flow is able to suppress noise and interference simultaneously on the 3D land data much faster than the other inversion methods.
