Cross-streamer wavefield interpolation using deep convolutional networks
Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these setti...
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ftoslouniv:oai:www.duo.uio.no:10852/77027 2023-05-15T15:38:56+02:00 Cross-streamer wavefield interpolation using deep convolutional networks Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. 2019-10-03T12:44:36Z http://hdl.handle.net/10852/77027 http://urn.nb.no/URN:NBN:no-80150 https://doi.org/10.1190/segam2019-3214009.1 EN eng SEG SEG technical program expanded abstracts NFR/287664 http://urn.nb.no/URN:NBN:no-80150 Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. . Cross-streamer wavefield interpolation using deep convolutional networks. SEG Technical Program Expanded Abstracts 2019. 2019. USA: SEG http://hdl.handle.net/10852/77027 1733442 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=SEG Technical Program Expanded Abstracts 2019&rft.spage=&rft.date=2019 5407 https://doi.org/10.1190/segam2019-3214009.1 URN:NBN:no-80150 Fulltext https://www.duo.uio.no/bitstream/handle/10852/77027/1/2019-Cross_Streamer_Wavefield_Interpolation.pdf 0916160009 Chapter Bokkapittel Peer reviewed PublishedVersion 2019 ftoslouniv https://doi.org/10.1190/segam2019-3214009.1 2020-06-21T08:54:42Z Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broad-band synthetic data and TopSeis field data from the Barents Sea. Book Part Barents Sea Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Barents Sea SEG Technical Program Expanded Abstracts 2019 2207 2211 |
institution |
Open Polar |
collection |
Universitet i Oslo: Digitale utgivelser ved UiO (DUO) |
op_collection_id |
ftoslouniv |
language |
English |
description |
Seismic exploration in complex geological settings and shallow geological targets has led to a demand for higher spatial and temporal resolution in the final migrated image. Seismic data from conventional marine acquisition lacks near offset and wide azimuth data, which limits imaging in these settings. In addition, large streamer separation introduce aliasing of spatial frequencies across the streamers. A new marine survey configuration, known as TopSeis, was introduced in 2017 in order to address the shallow-target problem. However, introduction of near offset data has shown to be challenging for interpolation and regularization, using conventional methods. In this paper, we investigate deep learning as a tool for interpolation beyond spatial aliasing across the streamers, in the shot domain. The proposed method is based on imaging techniques from single-image super resolution (SISR). The model architecture consist of a deep convolutional neural network (CNN) and a periodic resampling layer for upscaling to the non-aliased wavefield. We demonstrate the performance of proposed method on representative broad-band synthetic data and TopSeis field data from the Barents Sea. |
format |
Book Part |
author |
Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. |
spellingShingle |
Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. Cross-streamer wavefield interpolation using deep convolutional networks |
author_facet |
Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. |
author_sort |
Greiner, Thomas Larsen |
title |
Cross-streamer wavefield interpolation using deep convolutional networks |
title_short |
Cross-streamer wavefield interpolation using deep convolutional networks |
title_full |
Cross-streamer wavefield interpolation using deep convolutional networks |
title_fullStr |
Cross-streamer wavefield interpolation using deep convolutional networks |
title_full_unstemmed |
Cross-streamer wavefield interpolation using deep convolutional networks |
title_sort |
cross-streamer wavefield interpolation using deep convolutional networks |
publisher |
SEG |
publishDate |
2019 |
url |
http://hdl.handle.net/10852/77027 http://urn.nb.no/URN:NBN:no-80150 https://doi.org/10.1190/segam2019-3214009.1 |
geographic |
Barents Sea |
geographic_facet |
Barents Sea |
genre |
Barents Sea |
genre_facet |
Barents Sea |
op_source |
0916160009 |
op_relation |
SEG technical program expanded abstracts NFR/287664 http://urn.nb.no/URN:NBN:no-80150 Greiner, Thomas Larsen Kolbjørnsen, Odd Lie, Jan Erik Harris Nilsen, Espen Kjeldsrud Evensen, Andreas Gelius, Leiv-J. . Cross-streamer wavefield interpolation using deep convolutional networks. SEG Technical Program Expanded Abstracts 2019. 2019. USA: SEG http://hdl.handle.net/10852/77027 1733442 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.btitle=SEG Technical Program Expanded Abstracts 2019&rft.spage=&rft.date=2019 5407 https://doi.org/10.1190/segam2019-3214009.1 URN:NBN:no-80150 Fulltext https://www.duo.uio.no/bitstream/handle/10852/77027/1/2019-Cross_Streamer_Wavefield_Interpolation.pdf |
op_doi |
https://doi.org/10.1190/segam2019-3214009.1 |
container_title |
SEG Technical Program Expanded Abstracts 2019 |
container_start_page |
2207 |
op_container_end_page |
2211 |
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1766370360921423872 |