… is afforded by sparse seismic wavefield sampling will … of sparse sampling theory for seismic
acquisition. Our final goal is to develop practical guidelines for the design of sparse sampling …

… seismic interpolation problem, let us denote by m the original seismic wavefield, d the sampled
data, and L the sampling … Our purpose is to restore m from the sampled data d. Since d is …

… Similarly, wavefields reconstructed in unknown velocity using data recorded … -earthquake
location. The similarity of random wavefield fluctuations due to model fluctuations and sparse …

… reconstruction scheme which uses sparse, irregularlysampled data, containing pressure …
-sampled, dense data. Here, we employ a prior-knowledge-based, datadependent sampling …

… seismic data is sparse under a transform, then we can restore the complete data from sub-sampled …
compressive sensing theory in seismology is the design of new sampling methods in …

… In Figure 4, we show that curvelets and wavelets can sparsely represent seismic data, but,
as evidenced by the presented examples, sparsity is not sufficient to ensure high-quality …

… In this paper, we demonstrated that curvelet transforms sparsely represent uniformly-sampled
seismic data. This property was used to perform denoising and coherent signal separation, …

… We start by formulating the seismic data interpolation problem as a sparse recovery problem
and then present an overview of the recovery guarantees of ℓ 1 minimization and weighted …

… surface wavefield can be sparsely represented by a plane-wave basis (ie sparsity in the
wavenumber domain). Meanwhile, the spatially random sampling function |$\Psi $| by seismic …

… sampling patterns with semioptimal sparse Fourier recovery conditions. The double-weave
3D seismic … ) provides just such a regular sampling scheme suitable for Fourier reconstruction …