Translations:Model-driven predictive deconvolution/1/en

Model-driven predictive deconvolution (Robinson, 1998) is a method that uses a model both for theory and for application to empirical data. Model-driven deconvolution employs a strategy for effective use of both spike deconvolution and gap deconvolution in conjunction with each other. It is assumed that the given trace x already has undergone significant processing operations such as surface-consistent deconvolution and/or signature deconvolution to remove everything except the minimum-delay reverberation train b. However, it also is assumed that these previous processing steps have not been done perfectly, so an unwanted nonminimum-delay orphan signal u also remains on the given trace. Thus, the given trace x consists of the white reflectivity $\varepsilon$, colored by the unwanted minimum-delay reverberation train b and the unwanted nonminimum-delay orphan u. The seismic wavelet w is the convolution of the reverberation train b and the unwanted orphan u; that is, the wavelet is the nonminimum-delay signal