Full-wavefield regularization to enhance signal-to-noise ratio in sparse low-fold land data for better reservoir attribute extraction

Abstract

Sparse land seismic data, when acquired in areas with sand and detrital materials overlying faster velocity strata, are often marred by intense near-surface noise moving at slow speeds. This noise persists, even with the fine source and receiver spacing of high-channel-count 3D surveys, leading to aliasing issues. Traditional velocity-based filtering, whether in the frequency-wavenumber (f-k) or radon domains, is ineffective at removing this noise during data processing. Addressing the aliasing of slow near-surface noise through proper regularization before denoising can significantly enhance data quality. We introduce a novel approach called full-wavefield regularization and present its application for the first time on a sparse 3D low-fold offshore seismic data set. This technique aims to regularize both the seismic signals and coherent noise wave trains to a spacing where aliasing is not an issue. Consequently, the reflection and surface-wave components can be easily distinguished, and the noise can be eliminated. The results depict a marked improvement in the signal-to-noise ratio. This makes the data set usable and improves the computation of high-quality seismic attributes such as coherence and curvature, which supports better interpretation of the seismic data.