Separation of signal and coherent noise by migration filtering

A key issue in wavefield separation is to find a domain where the signal an d coherent noise are well separated from one another. A new wavefield separ ation algorithm, called migration filtering, separates data arrivals accord ing to their path of propagation and their actual moveout characteristics. This is accomplished by using forward modeling operators to compute the sig nal and the coherent noise arrivals. A linearized least-squares inversion s cheme yields model estimates for both components: the predicted signal comp onent is constructed by forward modeling the signal model estimate. Synthet ic and field data examples demonstrate that migration filtering improves se paration of P-wave reflections and surface waves, P-wave reflections and tu be waves, P-wave diffractions, and S-wave diffractions. The main benefits o f the migration filtering method compared to conventional filtering methods are better wavefield separation capability, the capability of mixing any t wo conventional transforms for wavefield separation under a general inversi on framework, and the capability of mitigating the signal and coherent nois e crosstalk by using regularization. The limitations of the method may incl ude more than an order of magnitude increase in computation costs compared to conventional transforms and the difficulty of selecting the proper model ing operators for some wave modes.