Advanced Time Imaging

Abstract

This thesis compromises different techniques how to reduce the signal-to-noise ratio of time-migrated images. Two different techniques are introduced, such as, a velocity refinement and migration deconvolution (deblurring and supervised convolutional neural network). The proposed methods are succesfully applied to data and reduce different types of noise. Furthermore, some of the techniques are extended and applied to a special 3D data set, called P-cable. This challenging acquisition requires a specialized processing, which is introduced.