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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CC8TE
Repositorysid.inpe.br/sibgrapi/2021/09.03.03.20
Last Update2021:09.03.03.20.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.03.03.20.22
Metadata Last Update2022:06.14.00.00.21 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00046
Citation KeyCalhesKobMatMacOli:2021:SiHoPi
TitleSimplifying Horizon Picking Using Single-Class Semantic Segmentation Networks
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size6607 KiB
2. Context
Author1 Calhes, Danilo
2 Kobayashi, Felipe K.
3 Mattos, Andrea Britto
4 Macedo, Maysa Malfiza Garcia de
5 Oliveira, Dario Augusto Borges
Affiliation1 IBM 
2 Federal University of ABC 
3 IBM Research 
4 IBM Research 
5 IBM Research
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressmmacedo@br.ibm.com
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-03 03:20:22 :: mmacedo@br.ibm.com -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:22:24 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:21 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsseismic image
horizon picking
deep learning
image classification
AbstractSeismic image processing plays a significant role in geological exploration as it conditions much of the interpretation performance. The interpretation process comprises several tasks, and Horizon Picking is one of the most time-consuming. Thereat, several works proposed methods for picking horizons automatically, mostly focusing on increasing the accuracy of data-driven approaches, by employing, for instance, semantic segmentation networks. However, these works often rely on a training process that requires several annotated samples, which are known to be scarce in the seismic domain, due to the overwhelming effort associated with manually picking several horizons in a seismic cube. This paper aims to evaluate the simplification of the labeling process required for training, by using training samples composed of disconnected horizons tokens, therefore relaxing the requirement of annotating the full set of horizons from each training sample, as commonly observed in previous works employing semantic segmentation networks. We assessed two state-of-art neural networks for general-purpose domains (PSP-Net and Deeplab V3+) using public seismic data (Netherlands F3 Block dataset). Our results report a minor impact in the performance using our proposed incomplete token training scheme compared to the complete one, moreover, we report that these networks outperform the current state-of-art for horizon picking from small training sets. Thus, our approach proves to be advantageous for the interpreter, given that using partial results instead of providing a full annotation can reduce the user effort during the labeling process required for training the models.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > Simplifying Horizon Picking...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Simplifying Horizon Picking...
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source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CC8TE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CC8TE
Languageen
Target FileSibgrapi_2021___binary_horizon_picking-2.pdf
User Groupmmacedo@br.ibm.com
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 4
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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