GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds

The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Geoscientific Model Development Jg. 18; H. 4; S. 939 - 960
Hauptverfasser:	Oakley, David, Loiselet, Christelle, Coowar, Thierry, Labbe, Vincent, Callot, Jean-Paul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 19.02.2025 European Geosciences Union Copernicus Publications
Schlagworte:	Algorithms Analysis Artificial neural networks Automation Clustering Data analysis Data science Datasets Earth Sciences Fault lines Folds Geological data Geological mapping Geological maps Geologists Geology Identification Learning algorithms Machine learning Neural networks Sciences of the Universe Statistical analysis Statistical methods Structures Supervised learning Topography Unsupervised learning
ISSN:	1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X, 1991-959X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the HDBSCAN clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds. This clustering algorithm is chosen because it does not require the number of clusters to be known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets, where they both prove capable of identifying folds. We also find that distinguishing folds from similar map patterns produced by topography is a major issue that must be accounted for with both methods. The unsupervised method has advantages, including the explainability of its results, and provides clearly better results in one of the two real-world test datasets, while the supervised learning method is more fully automated and likely more easily extensible to other structures. Both methods demonstrate the ability of machine learning to interpret folds on geological maps and have potential for further development targeting a wider range of structures and datasets.
AbstractList	The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the HDBSCAN clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds. This clustering algorithm is chosen because it does not require the number of clusters to be known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets, where they both prove capable of identifying folds. We also find that distinguishing folds from similar map patterns produced by topography is a major issue that must be accounted for with both methods. The unsupervised method has advantages, including the explainability of its results, and provides clearly better results in one of the two real-world test datasets, while the supervised learning method is more fully automated and likely more easily extensible to other structures. Both methods demonstrate the ability of machine learning to interpret folds on geological maps and have potential for further development targeting a wider range of structures and datasets.
Audience	Academic
Author	Oakley, David Loiselet, Christelle Labbe, Vincent Callot, Jean-Paul Coowar, Thierry
Author_xml	– sequence: 1 givenname: David orcidid: 0000-0002-2749-2856 surname: Oakley fullname: Oakley, David – sequence: 2 givenname: Christelle surname: Loiselet fullname: Loiselet, Christelle – sequence: 3 givenname: Thierry surname: Coowar fullname: Coowar, Thierry – sequence: 4 givenname: Vincent surname: Labbe fullname: Labbe, Vincent – sequence: 5 givenname: Jean-Paul orcidid: 0000-0001-9385-3974 surname: Callot fullname: Callot, Jean-Paul
BackLink	https://brgm.hal.science/hal-04977994$$DView record in HAL
BookMark	eNptks1u1DAUhSNUJNrCmq0lVl1k6r_ENruoGtqRBooKrC2PY2c8SuKp7QDd9R14Q54Eh0HQkZAXvrr6zpGPfM6Kk9GPpiheI7iokKCX3dCWiJeCiBJDXD0rTpEQqBQ1JCdP5hfFWYw7CGvBanZa3F8vb983H9fL5u4DQAv8FrQmGZ3c2IGYwqTTFEwENvgBdMb3vnNa9WBQ-wi-ubTNk9660YDeqDDOqp-PP0DaGqBVNMDbpyrr-za-LJ5b1Ufz6s99Xnx5t_x8dVOub69XV8261LSuUokN4xa3XLSCCi00VpZDVZENpVWO0bZMCV4bzXUGKbXcalhTY7WgihBsyHmxOvi2Xu3kPrhBhQfplZO_Fz50UoXkdG-ktlZAggjboJpCxASEpmIQYY02WNSz18XBa6v6I6ubZi3nHaSCMSHoV5TZNwd2H_z9ZGKSOz-FMUeVBNUcMZgD_aM6lR_gRutTUHpwUcuGYw4pI5xkavEfKp_WDE7n_7cu748EF0eCzCTzPXVqilGuPt0ds5cHVgcfYzD2bzIE5VwpmSslEZe5UnKuFPkF6Hi81w
Cites_doi	10.1190/geo2011-0302.1 10.1190/geo2017-0590.1 10.5194/gmd-16-6987-2023 10.1016/j.jsg.2018.11.010 10.1145/325165.325247 10.1007/s11004-021-09945-x 10.1190/geo2020-0945.1 10.1007/978-3-642-37456-2_14 10.1002/2017TC004731 10.1190/geo2018-0646.1 10.1016/j.cageo.2020.104475 10.4095/328296 10.1109/MFI49285.2020.9235263 10.1007/s11004-022-10027-9 10.1016/j.cageo.2014.04.012 10.1007/s10596-011-9257-z 10.1130/GSAT01711A.1 10.5194/gmd-15-6841-2022 10.1109/TGRS.2012.2207727 10.1190/SEGJ2018-138.1 10.1190/geo2012-0411.1 10.1190/geo2021-0586.1 10.1007/s11004-016-9663-9 10.1126/science.aau0323 10.1016/j.earscirev.2021.103812 10.1145/2733381 10.1016/j.earscirev.2023.104509 10.1007/978-3-540-74375-0 10.1109/VS-GAMES.2016.7590336 10.1016/0191-8141(92)90066-6 10.1144/1354-079308-738 10.1190/INT-2018-0235.1 10.1130/GES02253.1 10.1145/342009.335388 10.1016/j.egypro.2014.10.391 10.1016/j.cageo.2013.10.008 10.1007/BF00337288 10.1007/978-3-642-28872-2_27 10.1016/j.cageo.2019.03.006 10.1007/s10596-022-10152-8 10.1109/TNNLS.2017.2704779 10.1515/geo-2022-0479 10.5194/gmd-14-5063-2021 10.5194/essd-14-381-2022 10.1190/geo2019-0375.1 10.1016/j.cageo.2021.104701 10.1016/0191-8141(87)90012-5 10.1007/978-3-319-24574-4_28 10.1016/j.jsg.2004.12.004 10.1016/j.cageo.2021.104724 10.1016/j.epsl.2016.09.040 10.21105/joss.00205 10.1016/j.earscirev.2014.06.008 10.1016/j.jsg.2015.03.003 10.1016/j.cageo.2019.104344 10.1016/j.cageo.2021.104776 10.1016/0098-3004(94)90057-4
ContentType	Journal Article
Copyright	COPYRIGHT 2025 Copernicus GmbH 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Attribution
Copyright_xml	– notice: COPYRIGHT 2025 Copernicus GmbH – notice: 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Attribution
DBID	AAYXX CITATION ISR 7TG 7TN 7UA 8FD 8FE 8FG ABJCF ABUWG AEUYN AFKRA AZQEC BENPR BFMQW BGLVJ BHPHI BKSAR C1K CCPQU DWQXO F1W H8D H96 HCIFZ KL. L.G L6V L7M M7S PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 1XC VOOES DOA
DOI	10.5194/gmd-18-939-2025
DatabaseName	CrossRef Gale In Context: Science Meteorological & Geoastrophysical Abstracts Oceanic Abstracts Water Resources Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials - QC ProQuest Central Continental Europe Database Technology Collection Natural Science Collection Earth, Atmospheric & Aquatic Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea ASFA: Aquatic Sciences and Fisheries Abstracts Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources SciTech Premium Collection Meteorological & Geoastrophysical Abstracts - Academic Aquatic Science & Fisheries Abstracts (ASFA) Professional ProQuest Engineering Collection Advanced Technologies Database with Aerospace Engineering Database Earth, Atmospheric & Aquatic Science Database ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China Water Resources Abstracts Environmental Sciences and Pollution Management Earth, Atmospheric & Aquatic Science Collection ProQuest Central ProQuest One Applied & Life Sciences Aerospace Database ProQuest One Sustainability ProQuest Engineering Collection Meteorological & Geoastrophysical Abstracts Oceanic Abstracts Natural Science Collection ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Engineering Collection Engineering Database ProQuest One Academic Eastern Edition Earth, Atmospheric & Aquatic Science Database ProQuest Technology Collection Continental Europe Database ProQuest SciTech Collection Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ProQuest One Academic UKI Edition ASFA: Aquatic Sciences and Fisheries Abstracts Materials Science & Engineering Collection ProQuest One Academic Meteorological & Geoastrophysical Abstracts - Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Geology
EISSN	1991-9603 1991-962X 1991-959X
EndPage	960
ExternalDocumentID	oai_doaj_org_article_cff903137b164017900e57012c1b296e oai:HAL:hal-04977994v1 A828047383 10_5194_gmd_18_939_2025
GroupedDBID	5VS 8R4 8R5 AAFWJ AAYXX ABDBF ACUHS ADBBV AENEX AFPKN AHGZY ALMA_UNASSIGNED_HOLDINGS BCNDV CITATION ESX GROUPED_DOAJ H13 IAO IEA IEP ISR ITC KQ8 OK1 P2P Q2X RKB RNS TR2 TUS 7TG 7TN 7UA 8FD 8FE 8FG 8FH ABJCF ABUWG AEUYN AFKRA AZQEC BENPR BFMQW BGLVJ BHPHI BKSAR BPHCQ C1K CCPQU DWQXO F1W H8D H96 HCIFZ KL. L.G L6V L7M LK5 M7R M7S PCBAR PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PROAC PTHSS 1XC VOOES
ID	FETCH-LOGICAL-c465t-2e78f2d89d949c9c2af80a53b445603dd7a986ec8ce7844f8fc064efc94a332e3
IEDL.DBID	RKB
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001424435100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1991-9603 1991-959X 1991-962X
IngestDate	Fri Oct 03 12:41:43 EDT 2025 Tue Oct 14 20:33:15 EDT 2025 Fri Jul 25 12:22:58 EDT 2025 Mon Oct 20 22:45:33 EDT 2025 Mon Oct 20 16:53:20 EDT 2025 Thu Oct 16 15:37:28 EDT 2025 Sat Nov 29 08:18:19 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	4
Language	English
License	https://creativecommons.org/licenses/by/4.0 Attribution: http://creativecommons.org/licenses/by
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c465t-2e78f2d89d949c9c2af80a53b445603dd7a986ec8ce7844f8fc064efc94a332e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-2749-2856 0000-0001-9385-3974
OpenAccessLink	https://doaj.org/article/cff903137b164017900e57012c1b296e
PQID	3168170949
PQPubID	105726
PageCount	22
ParticipantIDs	doaj_primary_oai_doaj_org_article_cff903137b164017900e57012c1b296e hal_primary_oai_HAL_hal_04977994v1 proquest_journals_3168170949 gale_infotracmisc_A828047383 gale_infotracacademiconefile_A828047383 gale_incontextgauss_ISR_A828047383 crossref_primary_10_5194_gmd_18_939_2025
PublicationCentury	2000
PublicationDate	2025-02-19
PublicationDateYYYYMMDD	2025-02-19
PublicationDate_xml	– month: 02 year: 2025 text: 2025-02-19 day: 19
PublicationDecade	2020
PublicationPlace	Katlenburg-Lindau
PublicationPlace_xml	– name: Katlenburg-Lindau
PublicationTitle	Geoscientific Model Development
PublicationYear	2025
Publisher	Copernicus GmbH European Geosciences Union Copernicus Publications
Publisher_xml	– name: Copernicus GmbH – name: European Geosciences Union – name: Copernicus Publications
References	ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref74 ref33 ref32 ref2 ref1 ref39 ref38 ref71 ref70 ref73 ref72 ref24 ref68 ref23 ref67 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61
References_xml	– ident: ref19 doi: 10.1190/geo2011-0302.1 – ident: ref49 doi: 10.1190/geo2017-0590.1 – ident: ref1 – ident: ref40 doi: 10.5194/gmd-16-6987-2023 – ident: ref37 doi: 10.1016/j.jsg.2018.11.010 – ident: ref66 – ident: ref62 doi: 10.1145/325165.325247 – ident: ref39 doi: 10.1007/s11004-021-09945-x – ident: ref20 – ident: ref30 doi: 10.1190/geo2020-0945.1 – ident: ref43 – ident: ref16 doi: 10.1007/978-3-642-37456-2_14 – ident: ref27 – ident: ref36 doi: 10.1002/2017TC004731 – ident: ref72 doi: 10.1190/geo2018-0646.1 – ident: ref11 doi: 10.1016/j.cageo.2020.104475 – ident: ref14 doi: 10.4095/328296 – ident: ref34 – ident: ref53 doi: 10.1109/MFI49285.2020.9235263 – ident: ref70 doi: 10.1007/s11004-022-10027-9 – ident: ref67 doi: 10.1016/j.cageo.2014.04.012 – ident: ref13 – ident: ref61 – ident: ref31 doi: 10.1007/s10596-011-9257-z – ident: ref9 doi: 10.1130/GSAT01711A.1 – ident: ref7 doi: 10.5194/gmd-15-6841-2022 – ident: ref15 doi: 10.1109/TGRS.2012.2207727 – ident: ref33 doi: 10.1190/SEGJ2018-138.1 – ident: ref23 doi: 10.1190/geo2012-0411.1 – ident: ref57 doi: 10.1190/geo2021-0586.1 – ident: ref47 – ident: ref69 doi: 10.1007/s11004-016-9663-9 – ident: ref6 doi: 10.1126/science.aau0323 – ident: ref58 doi: 10.1016/j.earscirev.2021.103812 – ident: ref17 doi: 10.1145/2733381 – ident: ref5 doi: 10.1016/j.earscirev.2023.104509 – ident: ref54 doi: 10.1007/978-3-540-74375-0 – ident: ref64 doi: 10.1109/VS-GAMES.2016.7590336 – ident: ref21 doi: 10.1016/0191-8141(92)90066-6 – ident: ref18 doi: 10.1144/1354-079308-738 – ident: ref65 doi: 10.1190/INT-2018-0235.1 – ident: ref3 doi: 10.1130/GES02253.1 – ident: ref29 – ident: ref12 doi: 10.1145/342009.335388 – ident: ref60 – ident: ref71 doi: 10.1016/j.egypro.2014.10.391 – ident: ref22 – ident: ref24 doi: 10.1016/j.cageo.2013.10.008 – ident: ref48 doi: 10.1007/BF00337288 – ident: ref26 doi: 10.1007/978-3-642-28872-2_27 – ident: ref32 – ident: ref51 doi: 10.1016/j.cageo.2019.03.006 – ident: ref74 doi: 10.1007/s10596-022-10152-8 – ident: ref35 doi: 10.1109/TNNLS.2017.2704779 – ident: ref41 doi: 10.1515/geo-2022-0479 – ident: ref44 doi: 10.5194/gmd-14-5063-2021 – ident: ref45 doi: 10.5194/essd-14-381-2022 – ident: ref73 doi: 10.1190/geo2019-0375.1 – ident: ref38 doi: 10.1016/j.cageo.2021.104701 – ident: ref59 – ident: ref68 doi: 10.1016/0191-8141(87)90012-5 – ident: ref63 doi: 10.1007/978-3-319-24574-4_28 – ident: ref46 – ident: ref28 doi: 10.1016/j.jsg.2004.12.004 – ident: ref2 doi: 10.1016/j.cageo.2021.104724 – ident: ref50 doi: 10.1016/j.epsl.2016.09.040 – ident: ref55 doi: 10.21105/joss.00205 – ident: ref10 doi: 10.1016/j.earscirev.2014.06.008 – ident: ref52 – ident: ref8 doi: 10.1016/j.jsg.2015.03.003 – ident: ref25 doi: 10.1016/j.cageo.2019.104344 – ident: ref4 doi: 10.1016/j.cageo.2021.104776 – ident: ref42 doi: 10.1016/0098-3004(94)90057-4 – ident: ref56
SSID	ssj0069767 ssj0069768
Score	2.3711655
Snippet	The increasing availability of large geological datasets and modern methods of data analysis facilitate a data science approach to geology in which inferences...
SourceID	doaj hal proquest gale crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database
StartPage	939
SubjectTerms	Algorithms Analysis Artificial neural networks Automation Clustering Data analysis Data science Datasets Earth Sciences Fault lines Folds Geological data Geological mapping Geological maps Geologists Geology Identification Learning algorithms Machine learning Neural networks Sciences of the Universe Statistical analysis Statistical methods Structures Supervised learning Topography Unsupervised learning
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwELagAokLAgpioSCrQqKXqEnsxDa3FG1bpGWpCkh7s5yxva3U7pZmW4kb78Ab8iSdSbLVbi9ceomiZBLZM-P5ScbfMPZBuSAVlCIBhweZgkjqOmaJE9IX6GN1Di3O7EiNx3oyMUcrrb6oJqyDB-4YtwsxGsIXVDUG9qQ-aRoKhWYVsjo3ZSDrmyqzTKY6G1yik23bqlBdjynMpAP1wWhF7k7PfZLhIhcGNYQ6ZK_4oxa2_9Y4Pzyh2sg7Jrr1O_vP2NM-YORVN9Dn7EGYvWCPD9qGvL832a-D4bev1dFoWB2POWain7gP9F8APRLvsGGvMKHmtIuET8PS0vFzd9Fw-gaLZ1RNGXjfPmLK__35yzEq5ID-jc_j6lNxfuabl-zn_vDH58Okb6OQgCyLRZIHpWPutfFGGjCQu6hTV4haYvCUCu-VM7oMoAEJpYw6AsYpIYKRTog8iFdsYzafhdeMF0giShlV5pQMZayL2mN8mQvAqCMvYMB2lsy0Fx1ahsUsg_huke820xb5bonvA7ZHzL4lI5jr9gIK3_bCt_8T_oBtk6gsAVnMqFJm6q6axn75fmwrTCVTqTABH7CPPVGcLy4duH7jAU6JsK_WKLfWKHGlwdrtbdSItREfViNL1zDPUsoYeZ3hO5YKY3tz0FjqDpYpzKTNm_uY9lv2hFhIxeOZ2WIbqE3hHXsE14vT5vJ9uxJuAAWAByI priority: 102 providerName: Directory of Open Access Journals – databaseName: Engineering Database dbid: M7S link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwELZoAakX_lEXCrIqJLhETWInjrmggLYt0rJULUh7s5yJHZDoZrvZVuLGO_CGPAkzWW_Z5cCFS5Q4kyjOjOfHHn_D2AtlnVSQiwgsHmQMIqoqn0RWyDpDG1uk0OPMjtR4XEwm-iRMuHUhrXKlE3tFXbdAc-QHVGApURiM6Dezi4iqRtHqaiihscVuEkpC0qfuna00cY6mVq1f9PviKNVH5-lkifODDow8aM7rKMFxLzQKDRXNXjNRPZL_tb7e-kLpkn9p7d4UHd79307cY3eCE8rLpdTcZzfc9AG7fdQX-f3-kF0cDT9-KE9Gw_J0zDG6fc1rR2sNaOX4Em_2EoN0TjtTeONW2pOf21nHaV4XzyhD0_FQkqLhv3785OhpckCbyVu__pRvv9XdI_b5cPjp3XEUSjNEIPNsEaVOFT6tC11jX0BDan0R20xUEh2yWNS1srrIHRSAhFL6wgP6Ps6DllaI1InHbHvaTt0u4xmSiFx6lVglXe6rrKrRZ00FoCeTZjBgr1bcMLMlAofByIUYZ5BxJikMMs4Q4wbsLXHrmoygs_uGdt6YMBINeK8JsFJVGCmSPopjlym005BUqc7dgO0Trw2BY0wp-6axl11n3p-dmhLD01gqDOoH7GUg8u1ibsGGzQzYJcLT2qDc26DE0Qsbt_dRpDa--LgcGWrD2E0preVVgu9YiZMJKqYzf2Tpyb9vP2U79HMo1TzRe2wb5cQ9Y7fgavG1mz_vR8xvarQZmg priority: 102 providerName: ProQuest
Title	GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds
URI	https://www.proquest.com/docview/3168170949 https://brgm.hal.science/hal-04977994 https://doaj.org/article/cff903137b164017900e57012c1b296e
Volume	18
WOSCitedRecordID	wos001424435100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAGF databaseName: Copernicus Publications customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069767 issn: 1991-9603 databaseCode: RKB dateStart: 20080101 isFulltext: true titleUrlDefault: http://publications.copernicus.org/open-access_journals/open_access_journals_a_z.html providerName: Copernicus Gesellschaft – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069767 issn: 1991-9603 databaseCode: DOA dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Continental Europe Database customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069768 issn: 1991-9603 databaseCode: BFMQW dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/conteurope providerName: ProQuest – providerCode: PRVPQU databaseName: Earth, Atmospheric & Aquatic Science Database customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069768 issn: 1991-9603 databaseCode: PCBAR dateStart: 20080101 isFulltext: true titleUrlDefault: https://search.proquest.com/eaasdb providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069768 issn: 1991-9603 databaseCode: M7S dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069768 issn: 1991-9603 databaseCode: BENPR dateStart: 20080101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1991-9603 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0069768 issn: 1991-9603 databaseCode: PIMPY dateStart: 20080101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9NAEF5BAakX3qiBEq0qJLhYtb1rr5ebi9KHlAYrBSmcVuvxbqjUJqVOK3HjP_AP-0s6YztVwoUDXKxoM7bs_WbnYc9-w9g7ZZ1UkIoALB5kCCIoSx8FVsgqQR-bxdDwzA7VaJRNJrpYafVFNWEtPXA7cbvgvSZ-QVViYE_qE4YuUWhWISpjnTrat46mlvJ06uHW2uAUnWzTVoXqenSiJy2pD0Yrcnd6XgURLnKhUUOoQ_aKP2po---M8_3vVBv5h4lu_M7-k3-446fscRds8rw95Rm752bP2aODppnvzxfsx8Hg83FeDAf5eMQxi_3IK0ffFNCb8ZZX9gqTcU47UPjULa0kP7cXNaf3t_iLKjEd71pPTPnNr98cI0oO6Bv53K-e5ednVf2Sfd0ffPl0GHQtGAKQabIIYqcyH1eZrrTUoCG2PgttIkqJgVcoqkpZnaUOMkBBKX3mAWMc50FLK0TsxCu2MZvP3BbjCYqIVHoVWSVd6sukrDA2jQVgxBIn0GMflkCYi5Zpw2CGQpgZxMxEmUHMDGHWY3s063diRJHdDCAMpoPB_A2GHtshmA2RYMyoymZqr-raHJ2MTY5paCgVJu899r4T8vPFpQXbbVrARyLerDXJ7TVJXKWw9vcOatPaHR_mQ0NjmKMppbW8jvAaS2UznSmpDXUWixRm4fr1_3jsN2yTppAKzyO9zTZQm9xb9hCuF6f1ZZ892BuMinG_eTXRp0LYExwrjo6Lb_1mhd0Cg5odwg
linkProvider	Copernicus Gesellschaft
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEF61KQgu_CMCBVYVCC5Wbe_a60VCVYC0iZqEqBQpnJb1ejcg0TiN06LeeIe-Bw_FkzDj2CXhwK0HLpZ_xivv-vN8M-vZGUKeCW25MDHzjIYN9w3z0tQFnmY8i4Bjk9CUeWZ7YjBIRiM5XCM_67UwGFZZ68RSUWe5wTnybSywFAhwRuTO9NjDqlH4d7UuobGAxb49-w4uW_G6-w7e7_Mw3G0fvu14VVUBz_A4mnuhFYkLs0Rm0JaRJtQu8XXEUg62hM-yTGiZxNYkBgQ5d4kzQNvWGck1Y6Fl0O462eAI9gbZGHb7w0-17o-B3MXyQbkSD4OLZByOFpmFwGTi2-OjzAtA0zAJMMUy3UukWNYOuGCI9S8YoPkXT5Tkt3vzfxu2W-RGZWbT1uK7uE3W7OQOubpXljE-u0uO99rv-61hr906GFDw31_RzOLfFOBxusioezKzBcW1N3Rsa36gR3paUJy5hj2MQbW0Kroxpr9-nFOwpakBq4Dmbvkul3_Linvk46X09z5pTPKJfUBoBCIs5k4EWnAbuzRKM7DKQ2bAVgsj0yQv67evposcIwp8MwSKAqCoIFEAFIVAaZI3iI4LMUwOXp7IZ2NV6RplnJOYklOk4AujxvV9GwmwREyQhjK2TbKF2FKY_mOC8UVjfVIUqvvhQLXAAfe5YAlrkheVkMvnM210tVwDuoQZw1YkN1ckQT-ZlctbAOGVJ-60egrPgXcqhJT8NIA2aviqSokW6g92H_778lNyrXPY76led7D_iFzHgcLA-kBukgZgxj4mV8zp_Gsxe1J9r5R8vmys_wZh1XjH
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtNAEF61KSAu_CMCBVYVCC5WbO_au4uEUEqTNmoaQgGR22KvdwMSjdM4LeqNd-BteByehBnHLgkHbj1wsez1eOWfz_PN7M7OEPJEJJYLEzPPJLDhvmFemrrASxjPIuBYGZoyz2xfDAZyNFLDNfKzXguDYZW1TiwVdZYbHCNvYYGlQIAzolquCosY7nRfTY89rCCFM611OY0FRPbt2Tdw34qXvR341k_DsNt5_3rPqyoMeIbH0dwLrZAuzKTKoF-jTJg46ScRSznYFT7LMpEoGVsjDQhy7qQzQOHWGcUTxkLLoN91siHjWPoNsrHdPXj7seaBGIheLB-Uq_Iw0EjF4WiRZQjMJ94aH2VeAFqHKYAsluxeIsiyjsA5W6x_xmDNvzijJMLu9f_5Fd4g1yrzm7YX_8tNsmYnt8jl3bK88dltcrzbeXPQHvY77cMBBb_-Bc0szrIAv9NFpt2TmS0orsmhY1vzBj1KpgXFEW3Yw9hUS6tiHGP66_sPCjY2NWAt0NwtX-Xyr1lxh3y4kOe9SxqTfGLvERqBCIu5E0EiuI1dGqUZWOshM2DDhZFpkuc1EvR0kXtEg8-GoNEAGh1IDaDRCJom2UaknIth0vCyIZ-NdaWDtHFOYapOkYKPjJrY920kwEIxQRqq2DbJFuJMY1qQCaJjnJwUhe69O9RtcMx9LphkTfKsEnL5fJaYpFrGAY-EmcRWJDdXJEFvmZXTWwDnlTvea_c1toHXKoRS_DSAPmoo60q5FvoPju__-_RjcgUArvu9wf4DchXfE8bbB2qTNAAy9iG5ZE7nX4rZo-rXpeTTRUP9N1l8gWc
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=GEOMAPLEARN+1.2%3A+detecting+structures+from+geological+maps+with+machine+learning+%E2%80%93+the+case+of+geological+folds&rft.jtitle=Geoscientific+model+development&rft.au=D.+Oakley&rft.au=D.+Oakley&rft.au=C.+Loiselet&rft.au=T.+Coowar&rft.date=2025-02-19&rft.pub=Copernicus+Publications&rft.issn=1991-959X&rft.eissn=1991-9603&rft.volume=18&rft.spage=939&rft.epage=960&rft_id=info:doi/10.5194%2Fgmd-18-939-2025&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_cff903137b164017900e57012c1b296e
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1991-9603&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1991-9603&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1991-9603&client=summon