Research Note: Near-surface layer replacement for sparse data: Is interpolation needed?

Near-surface problem is a common challenge faced by land seismic data processing, where often, due to near-surface anomalies, events of interest are obscured. One method to handle this challenge is near-surface layer replacement, which is a wavefield reconstruction process based on downward wavefield extrapolation with the near-surface velocity model and upward wavefield extrapolation with a replacement velocity model. This requires, in theory, that the original wavefield should be densely sampled. In reality, data acquisition is always sparse due to economic reasons, and as a result in the near-surface layer replacement data interpolation should be resorted to. For datasets with near-surface challenges, because of the complex event behaviour, a suitable interpolation scheme by itself is a challenging problem, and this, in turn, makes it difficult to carry out the near-surface layer replacement. In this research note, we first point out that the final objective of the near-surface layer replacement is not to obtain a newly reconstructed wavefield but to obtain a better final image. Next, based upon this finding, we propose a new thinking, interpolation-free near-surface layer replacement, which can handle complex datasets without any interpolation. Data volume expansion is the key idea, and with its help, the interpolation-free near-surface layer replacement is capable of preserving the valuable information of areas of interest in the original dataset. Two datasets, i.e., a two-dimensional synthetic dataset and a three-dimensional field dataset, are used to demonstrate this idea. One conclusion that can be drawn is that an attempt to interpolate data before layer replacement may deteriorate the final image after layer replacement, whereas interpolation-free near-surface layer replacement preserves all image details in the subsurface.