Removing data aliasing artifacts

Inverse theory offers an attractive framework for designing solvers that will enforce sparseness in the radon domain Nichols (1994); Sacchi and Ulrych (1995). These solvers impose focusing in the model space by adding a regularization term in the objective function, which then penalizes small values. This section presents two methods that address data aliasing. One method is generally used in the time domain, and the other is specifically designed to work in the frequency domain Herrmann et al. (2000). For data examples of the time domain method, the reader can refer to Guitton (2000) and Nichols (1994). Synthetic and real data examples of the frequency domain method follow my description of it.