Multimodel adaptive subtraction with regularized parameter selection via generalized information criterion

ABSTRACTAdaptive subtraction remains a critical part of today’s seismic data processing. Many existing adaptive subtraction methods can be formulated as a parameter estimation problem, and all of them, although fundamentally different, have a common restriction that the dimension of the unknown parameters must be determined in advance. We have developed an adaptive subtraction framework called multimodel adaptive subtraction (MMAS) that aims to relax this restriction as well as regularize the estimation of the parameters through a generalized information criterion combined with a nonconcave penalized likelihood function. MMAS is a fully general framework that can be applied to many existing adaptive subtraction methods. As an example, we determined how MMAS can be applied to the popular least-squares adaptive subtraction (LSAS) method, and we call the resulting algorithm the multimodel LSAS (MMASLS); furthermore, we studied its computational complexity and developed an efficient implementation for MMASLS....