Tomographic deconvolution of reflection tomograms

Tushar Gautam, Yicheng Zhou, Shihang Feng, Gerard T. Schuster

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a tomographic deconvolution procedure for highresolution imaging of velocity anomalies between reflecting interfaces. The key idea is to first invert reflection or transmission traveltimes for the background velocity model. A convolutional neural network (CNN) model is then trained to estimate the inverse to the blurred tomogram consisting of small scatterers in the background velocity model. We call this CNN a tomographic deconvolution operator because it deconvolves the blurring artifacts in traveltime slowness tomograms. This procedure is similar to that of migration deconvolution which deconvolves the migration butterfly artifacts in migration images. Results with synthetic examples show the effectiveness of this procedure in significantly sharpening the tomographic images of small scatterers.
Original languageEnglish (US)
Title of host publicationFirst International Meeting for Applied Geoscience & Energy Expanded Abstracts
PublisherSociety of Exploration Geophysicists
StatePublished - Sep 1 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-07
Acknowledgements: The research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia. We are grateful to the sponsors of the Center for Subsurface Imaging and Modeling Consortium for their financial support. For computer time, this research used the resources of the Supercomputing Laboratory at KAUST and the IT Research Computing Group. We thank them for providing the computational resources required for carrying out this work.


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