Numerous applications in seismic image analysis require matching two or more images. Examples include time-lapse and multicomponent image registration, migration deconvolution, full waveform inversion, adaptive subtraction of multiples, etc. Some applications benefit from separating the matching procedure into components, such as scaling, shifting, and smoothing. I review different techniques for seismic image matching and compare them using synthetic and field data examples.

This presentation is part of Minisymposium “MS67 - Advances and new directions in seismic imaging and inversion (3 parts)”
organized by: Mauricio Sacchi (University of Alberta) , Sergey Fomel (University of Texas, Austin) , Laurent Demanet (MIT ) .

Authors:

Sergey Fomel (University of Texas, Austin)

Sarah Greer (University of Texas at Austin)

Keywords:

deep learning, image deblurring, image registration, inverse problems, nonlinear optimization