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Soil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Data

2025-04Journal article Open access
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datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg15:Proteger a Vida Terrestre
dc.contributor.authorCarvalho, João
dc.contributor.authorDias, Ruben
dc.contributor.authorBorges, José Fernando
dc.contributor.authorQuental, Lídia
dc.contributor.authorCaldeira, Bento
dc.date.accessioned2025-05-14T15:47:35Z
dc.date.available2025-05-14T15:47:35Z
dc.date.issued2025-04
dc.description.abstractABSTRACT: The Lower Tagus Valley (LTV) region has the highest population density in Portugal, with over 3.7 million people living in the region. It has been struck in the past by several historical earthquakes, which caused significant economic and human losses. For a proper seismic hazard evaluation, the area needs detailed V-s30 and soil classification maps. Previously available maps are based on proxies, or an insufficient number of velocity measurements followed by coarse geological generalizations. The focus of this work is to significantly improve the available maps. For this purpose, more than 90 new S-wave seismic velocities measurements obtained from seismic refraction and seismic noise measurements, doubling the number used in previously available maps, are used to update available V-s30 and soil classification maps. The data points are also generalized to the available geological maps using local lithostratigraphic studies and, for the first time, satellite images of this area. The results indicate that lithological and thickness changes within each geological formation prevent a simple generalization of geophysical data interpretation based solely on geological mapping. The maps presented here are the first attempt to produce maps at a scale larger than 1:1,000,000 in Portugal, with direct shear wave velocity measurements. A tentative approach to produce more detailed maps using machine learning was also carried out, presenting promising results. This approach may be used in the future to reduce the number of shear wave measurements necessary to produce detailed maps at a finer scale.eng
dc.identifier.citationCarvalho, J., Dias, R., Borges, J., Quental, L., & Caldeira, B. (2025). Soil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Data. In: Remote Sensing, vol. 17(8), article 1376. https://doi.org/10.3390/rs17081376
dc.identifier.doi10.3390/rs17081376
dc.identifier.eissn2072-4292
dc.identifier.urihttp://hdl.handle.net/10400.9/5682
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationNEFITAG - Strong ground motion and near field effects in the Lower Tagus Valley Region
dc.relationSite Condition Evaluation for National Seismic Hazard Estimation (SCENE)
dc.relation.hasversionhttps://www.mdpi.com/2072-4292/17/8/1376
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectGeology
dc.subjectRemote sensing
dc.subjectSeismic hazard
dc.subjectMultispectral
dc.subjectMachine learning
dc.subjectLower Tagus Valley
dc.titleSoil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Dataeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleNEFITAG - Strong ground motion and near field effects in the Lower Tagus Valley Region
oaire.awardTitleSite Condition Evaluation for National Seismic Hazard Estimation (SCENE)
oaire.awardURIhttp://hdl.handle.net/10400.9/5680
oaire.awardURIhttp://hdl.handle.net/10400.9/5681
oaire.citation.issue8
oaire.citation.titleRemote Sensing
oaire.citation.volume17
oaire.fundingStream5876-PPCDTI
oaire.fundingStream3599-PPCDT
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCarvalho
person.familyNameDias
person.familyNameQuental
person.familyNameCaldeira
person.givenNameJoão
person.givenNameRuben
person.givenNameLídia
person.givenNameBento
person.identifier.ciencia-id1D16-E24A-F4E4
person.identifier.ciencia-id0F10-032A-E243
person.identifier.ciencia-id681B-D4C1-6572
person.identifier.ciencia-idFC1D-41D8-5F1F
person.identifier.orcid0000-0003-2227-3918
person.identifier.orcid0000-0003-1654-809X
person.identifier.orcid0000-0003-0407-3955
person.identifier.orcid0000-0003-4745-6972
person.identifier.ridM-6005-2013
person.identifier.scopus-author-id54793398700
person.identifier.scopus-author-id22833857700
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relation.isAuthorOfPublication.latestForDiscovery363e9adf-6808-4881-bbe7-41a6fe7da470
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