Random sampling: new insights into the reconstruction of coarsely-sampled wavefields

Conclusions

We proposed to look at the seismic data interpolation problem from a denoising perspective. From this standpoint, we showed that, for the same amount of data collected, regular subsampling geometries generate coherent acquisition noise more difficult to remove than the incoherent noise created by random subsampling geometries. Hence, random subsampling leads to a more accurate reconstruction of the seismic wavefield than equivalent regular subsampling or any subsampling that generates structured acquisition noise. We believe this new insight may lead to new acquisition strategies. On land, for example, a regular sampling may lead to (severely) aliased ground-roll that needs to be interpolated to a finer grid in order to be removed. Our observations suggest one should randomly sample on the finer grid instead. This leads to a better interpolation and hence ground-roll removal.