Tomographic deconvolution of reflection tomograms

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

doi:10.1190/segam2021-3595023.1

Tomographic deconvolution of reflection tomograms

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English (US)
Title of host publication	First International Meeting for Applied Geoscience & Energy Expanded Abstracts
Publisher	Society of Exploration Geophysicists
DOIs	https://doi.org/10.1190/segam2021-3595023.1
State	Published - Sep 1 2021

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title = "Tomographic deconvolution of reflection tomograms",

abstract = "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.",

author = "Tushar Gautam and Yicheng Zhou and Shihang Feng and Schuster, {Gerard T.}",

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.",

year = "2021",

month = sep,

day = "1",

doi = "10.1190/segam2021-3595023.1",

language = "English (US)",

booktitle = "First International Meeting for Applied Geoscience & Energy Expanded Abstracts",

publisher = "Society of Exploration Geophysicists",

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TY - GEN

T1 - Tomographic deconvolution of reflection tomograms

AU - Gautam, Tushar

AU - Zhou, Yicheng

AU - Feng, Shihang

AU - Schuster, Gerard T.

N1 - 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.

PY - 2021/9/1

Y1 - 2021/9/1

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/670964

UR - https://library.seg.org/doi/10.1190/segam2021-3595023.1

U2 - 10.1190/segam2021-3595023.1

DO - 10.1190/segam2021-3595023.1

M3 - Conference contribution

BT - First International Meeting for Applied Geoscience & Energy Expanded Abstracts

PB - Society of Exploration Geophysicists

ER -

Tomographic deconvolution of reflection tomograms

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