Image-space surface-related multiple prediction

Brad Artman and Ken Matson

brad@geo.stanford.edu

ABSTRACT

The first step in surface-related multiple elimination (SRME) is prediction of the multiples from the recorded seismic data. Adaptive or pattern-based subtraction techniques are often then employed to eliminate the multiples from the data to isolate primary reflections. The malleable framework of shot-profile depth migration can be easily modified to produce the migration of the conventional surface-related multiple prediction (SRMP) during the course of a shot-profile migration with the (small) additional cost of an extra imaging condition. There may be several advantages to removing the multiples in the image space as the kinematics of events are simpler and the image-space volume is smaller than the data space for subtraction.

Image-space multiple prediction (IS-SRMP) takes advantage of the commutability of convolution and extrapolation. Casting the multiple prediction problem in terms of a migration imaging condition immediately suggests a deconvolutional variant. A deconvolutional implementation (dividing the multiple model by a power spectrum) increases the bandwidth of the IS-SRMP result to a similar range as the conventional image which aids in subtraction.