{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361699995388360960.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1785/0120100262"}},{"identifier":{"@type":"URI","@value":"https://syndication.highwire.org/content/doi/10.1785/0120100262"}}],"dc:title":[{"@value":"A Terrain-Based Site-Conditions Map of California with Implications for the Contiguous United States"}],"description":[{"notation":[{"@value":"Abstract  We present an approach based on geomorphometry to predict material properties and characterize site conditions using the  V   S 30  parameter (time‐averaged shear‐wave velocity to a depth of 30 m). Our framework consists of an automated terrain classification scheme based on taxonomic criteria (slope gradient, local convexity, and surface texture) that systematically identifies 16 terrain types from 1‐km spatial resolution (30 arcsec) Shuttle Radar Topography Mission digital elevation models (SRTMDEMs). Using 853  V   S 30  values from California, we apply a simulation‐based statistical method to determine the mean  V   S 30  for each terrain type in California. We then compare the  V   S 30  values with models based on individual proxies, such as mapped surface geology and topographic slope, and show that our systematic terrain‐based approach consistently performs better than semiempirical estimates based on individual proxies. To further evaluate our model, we apply our California‐based estimates to terrains of the contiguous United States. Comparisons of our estimates with 325  V   S 30  measurements outside of California, as well as estimates based on the topographic slope model, indicate our method to be statistically robust and more accurate. Our approach thus provides an objective and robust method for extending estimates of  V   S 30  for regions where  in situ  measurements are sparse or not readily available."}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380004236854062730","@type":"Researcher","foaf:name":[{"@value":"A. Yong"}]},{"@id":"https://cir.nii.ac.jp/crid/1381699995388360961","@type":"Researcher","foaf:name":[{"@value":"S. E. Hough"}]},{"@id":"https://cir.nii.ac.jp/crid/1381699995388360960","@type":"Researcher","foaf:name":[{"@value":"J. Iwahashi"}]},{"@id":"https://cir.nii.ac.jp/crid/1381699995388360963","@type":"Researcher","foaf:name":[{"@value":"A. Braverman"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"00371106"}],"prism:publicationName":[{"@value":"Bulletin of the Seismological Society of America"}],"dc:publisher":[{"@value":"Seismological Society of America (SSA)"}],"prism:publicationDate":"2012-02-01","prism:volume":"102","prism:number":"1","prism:startingPage":"114","prism:endingPage":"128"},"reviewed":"false","url":[{"@id":"https://syndication.highwire.org/content/doi/10.1785/0120100262"}],"createdAt":"2012-02-15","modifiedAt":"2017-11-03","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360009142817776896","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"A Probabilistic Framework to Model Distributions of VS30"}]},{"@id":"https://cir.nii.ac.jp/crid/2050307417125248384","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Classification of topography for ground vulnerability assessment of alluvial plains and mountains of Japan using 30 m DEM"}]},{"@id":"https://cir.nii.ac.jp/crid/2051433317007351552","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Global terrain classification using 280 m DEMs : segmentation, clustering, and reclassification"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1785/0120100262"},{"@type":"OPENAIRE","@value":"doi_dedup___::02764cb74d27bb4258cf2635474edee8"},{"@type":"CROSSREF","@value":"10.1186/s40645-017-0157-2_references_DOI_LvvkUxMOr6Olcojv94mIVMDGTEP"},{"@type":"CROSSREF","@value":"10.1785/0120200281_references_DOI_LvvkUxMOr6Olcojv94mIVMDGTEP"},{"@type":"CROSSREF","@value":"10.1186/s40645-020-00398-0_references_DOI_LvvkUxMOr6Olcojv94mIVMDGTEP"}]}