Generalized internal multiple prediction for low relief structures

Abstract

Correlation- and convolution-based internal multiple prediction is one of the most common methods to predict and subsequently attenuate internal multiples. This technique requires the convolution of two seismic events from the deep section followed by the correlation with an event from the shallow part section to satisfy the so-called deep-shallow-deep (late-early-late) condition; otherwise, nonphysical seismic events can be predicted. To comply with this condition, seismic data are separated into shallow and deep portions according to a given phantom horizon. Internal multiples whose raypaths pass through the phantom horizon four times are predicted for one trial. In this way, only a portion of internal multiples are predicted. To address this limitation, a novel algorithm called the generalized internal multiple prediction (GIMP) algorithm is proposed. This algorithm is founded upon the adaptation of the deep-shallow correlation and deep-deep convolution techniques, constrained by specific integration boundaries. The GIMP method effectively accommodates the deep-shallow-deep (late-early-late) mode, without necessitating the segmentation of the seismic data set into shallow and deep segments via a user-defined phantom horizon. Notably, this approach is comprehensive in nature and has the capability to predict all potential internal multiples concurrently. Considering the computational cost, the current implementation focus of GIMP is primarily on 1D or 1.5D; however, GIMP is applicable to 2D and 3D scenarios.