Item talk:Q57797
From geokb
{
"USGS Publications Warehouse": {
"schema": {
"@context": "https://schema.org",
"@type": "CreativeWork",
"additionalType": "USGS Numbered Series",
"name": "The HayWired Earthquake Scenario",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "sir20175013",
"url": "https://pubs.usgs.gov/publication/sir20175013"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70187004
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.3133/sir20175013",
"url": "https://doi.org/10.3133/sir20175013"
}
],
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Scientific Investigations Report"
}
],
"datePublished": "2017",
"dateModified": "2022-04-22",
"abstract": "ForewordThe 1906 Great San Francisco earthquake (magnitude 7.8) and the 1989 Loma Prieta earthquake (magnitude 6.9) each motivated residents of the San Francisco Bay region to build countermeasures to earthquakes into the fabric of the region. Since Loma Prieta, bay-region communities, governments, and utilities have invested tens of billions of dollars in seismic upgrades and retrofits and replacements of older buildings and infrastructure. Innovation and state-of-the-art engineering, informed by science, including novel seismic-hazard assessments, have been applied to the challenge of increasing seismic resilience throughout the bay region. However, as long as people live and work in seismically vulnerable buildings or rely on seismically vulnerable transportation and utilities, more work remains to be done.With that in mind, the U.S. Geological Survey (USGS) and its partners developed the HayWired scenario as a tool to enable further actions that can change the outcome when the next major earthquake strikes. By illuminating the likely impacts to the present-day built environment, well-constructed scenarios can and have spurred officials and citizens to take steps that change the outcomes the scenario describes, whether used to guide more realistic response and recovery exercises or to launch mitigation measures that will reduce future risk.The HayWired scenario is the latest in a series of like-minded efforts to bring a special focus onto the impacts that could occur when the Hayward Fault again ruptures through the east side of the San Francisco Bay region as it last did in 1868. Cities in the east bay along the Richmond, Oakland, and Fremont corridor would be hit hardest by earthquake ground shaking, surface fault rupture, aftershocks, and fault afterslip, but the impacts would reach throughout the bay region and far beyond.\u00a0The HayWired\u00a0scenario name reflects our increased reliance on the Internet and telecommunications and also alludes to the interconnectedness of infrastructure, society, and our economy. How would this earthquake scenario, striking close to Silicon Valley, impact our interconnected world in ways and at a scale we have not experienced in any previous domestic earthquake?The area of present-day Contra Costa, Alameda, and Santa Clara Counties contended with a magnitude-6.8 earthquake in 1868 on the Hayward Fault. Although sparsely populated then, about 30 people were killed and extensive property damage resulted. The question of what an earthquake like that would do today has been examined before and is now revisited in the HayWired scenario. Scientists have documented a series of prehistoric earthquakes on the Hayward Fault and are confident that the threat of a future earthquake, like that modeled in the HayWired scenario, is real and could happen at any time. The team assembled to build this scenario has brought innovative new approaches to examining the natural hazards, impacts, and consequences of such an event. Such an earthquake would also be accompanied by widespread liquefaction and landslides, which are treated in greater detail than ever before. The team also considers how the now-prototype ShakeAlert earthquake early warning system could provide useful public alerts and automatic actions.Scientific Investigations Report 2017\u20135013 and accompanying data releases are the products of an effort led by the USGS, but this body of work was created through the combined efforts of a large team including partners who have come together to form the HayWired Coalition (see chapter A). Use of the HayWired scenario has already begun. More than a full year of intensive partner engagement, beginning in April 2017, is being directed toward producing the most in-depth look ever at the impacts and consequences of a large earthquake on the Hayward Fault. With the HayWired scenario, our hope is to encourage and support the active ongoing engagement of the entire community of the San Francisco Bay region by providing the scientific, engineering, and economic and social science inputs for use in exercises and planning well into the future.As HayWired volumes are published, they will be made available at https://doi.org/10.3133/sir20175013.",
"description": "3 Volumes",
"publisher": {
"@type": "Organization",
"name": "U.S. Geological Survey"
},
"editor": [
{
"@type": "Person",
"name": "Wein, Anne M. awein@usgs.gov",
"givenName": "Anne M.",
"familyName": "Wein",
"email": "awein@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0002-5516-3697",
"url": "https://orcid.org/0000-0002-5516-3697"
},
"affiliation": [
{
"@type": "Organization",
"name": "Western Geographic Science Center",
"url": "https://www.usgs.gov/centers/western-geographic-science-center"
}
]
},
{
"@type": "Person",
"name": "Detweiler, Shane T. shane@usgs.gov",
"givenName": "Shane T.",
"familyName": "Detweiler",
"email": "shane@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0001-5699-011X",
"url": "https://orcid.org/0000-0001-5699-011X"
},
"affiliation": [
{
"@type": "Organization",
"name": "Earthquake Science Center",
"url": "https://www.usgs.gov/centers/earthquake-science-center"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Earthquake Science Center",
"url": "https://www.usgs.gov/centers/earthquake-science-center"
}
],
"spatialCoverage": [
{
"@type": "Place",
"additionalType": "country",
"name": "United States",
"url": "https://geonames.org/4074035"
},
{
"@type": "Place",
"additionalType": "state",
"name": "California"
},
{
"@type": "Place",
"additionalType": "unknown",
"name": "Hayward Fault"
},
{
"@type": "Place",
"geo": [
{
"@type": "GeoShape",
"additionalProperty": {
"@type": "PropertyValue",
"name": "GeoJSON",
"value": {
"type": "FeatureCollection",
"features": [
{
"type": "Feature",
"properties": {},
"geometry": {
"type": "Polygon",
"coordinates": [
[
[
-123,
37
],
[
-121,
37
],
[
-121,
38.65
],
[
-123,
38.65
],
[
-123,
37
]
]
]
}
}
]
}
}
},
{
"@type": "GeoCoordinates",
"latitude": 37.82500000000001,
"longitude": -121.99999999999999
}
]
}
]
}
},
"OpenAlex": {
"abstract_inverted_index": null,
"apc_list": null,
"apc_paid": null,
"authorships": [],
"best_oa_location": null,
"biblio": {
"volume": null,
"issue": null,
"first_page": null,
"last_page": null
},
"citation_normalized_percentile": {
"value": 0.889309,
"is_in_top_1_percent": false,
"is_in_top_10_percent": false
},
"cited_by_api_url": "https://api.openalex.org/works?filter=cites:W4238928708",
"cited_by_count": 10,
"cited_by_percentile_year": {
"min": 86,
"max": 87
},
"concepts": [
{
"id": "https://openalex.org/c127313418",
"wikidata": "https://www.wikidata.org/wiki/Q1069",
"display_name": "Geology",
"level": 0,
"score": 0.6841097,
"qid": "Q158984"
},
{
"id": "https://openalex.org/c165205528",
"wikidata": "https://www.wikidata.org/wiki/Q83371",
"display_name": "Seismology",
"level": 1,
"score": 0.5141573,
"qid": "Q159027"
}
],
"corresponding_author_ids": [],
"corresponding_institution_ids": [],
"countries_distinct_count": 0,
"counts_by_year": [
{
"year": 2024,
"cited_by_count": 1
},
{
"year": 2023,
"cited_by_count": 3
},
{
"year": 2022,
"cited_by_count": 1
},
{
"year": 2021,
"cited_by_count": 2
},
{
"year": 2020,
"cited_by_count": 2
},
{
"year": 2019,
"cited_by_count": 1
}
],
"created_date": "2022-05-12",
"datasets": [],
"display_name": "The HayWired Earthquake Scenario",
"doi": "https://doi.org/10.3133/sir20175013",
"fwci": 0.663,
"grants": [],
"has_fulltext": false,
"id": "https://openalex.org/W4238928708",
"ids": {
"openalex": "https://openalex.org/W4238928708",
"doi": "https://doi.org/10.3133/sir20175013"
},
"indexed_in": [
"crossref"
],
"institutions_distinct_count": 0,
"is_paratext": false,
"is_retracted": false,
"keywords": [
{
"id": "https://openalex.org/keywords/earthquake-detection",
"display_name": "Earthquake Detection",
"score": 0.604069
},
{
"id": "https://openalex.org/keywords/seismic-phase-picking",
"display_name": "Seismic Phase Picking",
"score": 0.582261
},
{
"id": "https://openalex.org/keywords/real-time-seismology",
"display_name": "Real-Time Seismology",
"score": 0.578433
},
{
"id": "https://openalex.org/keywords/seismic-signals",
"display_name": "Seismic Signals",
"score": 0.571427
},
{
"id": "https://openalex.org/keywords/seismic-event-classification",
"display_name": "Seismic Event Classification",
"score": 0.526134
}
],
"language": "en",
"locations": [
{
"is_oa": false,
"landing_page_url": "https://doi.org/10.3133/sir20175013",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S2764832999",
"display_name": "Scientific investigations report",
"issn_l": "2328-0328",
"issn": [
"2328-0328",
"2328-031X",
"2332-5607"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": false,
"host_organization": "https://openalex.org/P4310319934",
"host_organization_name": "United States Geological Survey",
"host_organization_lineage": [
"https://openalex.org/P4310319934",
"https://openalex.org/P4310316088"
],
"host_organization_lineage_names": [
"United States Geological Survey",
"United States Department of the Interior"
],
"type": "journal"
},
"license": null,
"license_id": null,
"version": null,
"is_accepted": false,
"is_published": false
}
],
"locations_count": 1,
"mesh": [],
"ngrams_url": "https://api.openalex.org/works/W4238928708/ngrams",
"open_access": {
"is_oa": false,
"oa_status": "closed",
"oa_url": null,
"any_repository_has_fulltext": false
},
"primary_location": {
"is_oa": false,
"landing_page_url": "https://doi.org/10.3133/sir20175013",
"pdf_url": null,
"source": {
"id": "https://openalex.org/S2764832999",
"display_name": "Scientific investigations report",
"issn_l": "2328-0328",
"issn": [
"2328-0328",
"2328-031X",
"2332-5607"
],
"is_oa": false,
"is_in_doaj": false,
"is_core": false,
"host_organization": "https://openalex.org/P4310319934",
"host_organization_name": "United States Geological Survey",
"host_organization_lineage": [
"https://openalex.org/P4310319934",
"https://openalex.org/P4310316088"
],
"host_organization_lineage_names": [
"United States Geological Survey",
"United States Department of the Interior"
],
"type": "journal"
},
"license": null,
"license_id": null,
"version": null,
"is_accepted": false,
"is_published": false
},
"primary_topic": {
"id": "https://openalex.org/T13018",
"display_name": "Machine Learning for Earthquake Early Warning Systems",
"score": 0.5227,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
},
"publication_date": "2017-01-01",
"publication_year": 2017,
"referenced_works": [],
"referenced_works_count": 0,
"related_works": [
"https://openalex.org/W3141829427",
"https://openalex.org/W3113596969",
"https://openalex.org/W2933907707",
"https://openalex.org/W2772196783",
"https://openalex.org/W2355282060",
"https://openalex.org/W2167517487",
"https://openalex.org/W2092739438",
"https://openalex.org/W2035325200",
"https://openalex.org/W2022035173",
"https://openalex.org/W2004013317"
],
"sustainable_development_goals": [
{
"id": "https://metadata.un.org/sdg/13",
"score": 0.77,
"display_name": "Climate action"
}
],
"title": "The HayWired Earthquake Scenario",
"topics": [
{
"id": "https://openalex.org/T13018",
"display_name": "Machine Learning for Earthquake Early Warning Systems",
"score": 0.5227,
"subfield": {
"id": "https://openalex.org/subfields/1702",
"display_name": "Artificial Intelligence"
},
"field": {
"id": "https://openalex.org/fields/17",
"display_name": "Computer Science"
},
"domain": {
"id": "https://openalex.org/domains/3",
"display_name": "Physical Sciences"
}
}
],
"type": "article",
"type_crossref": "journal-article",
"updated_date": "2024-08-11T08:38:52.181057",
"versions": [],
"qid": "Q57797"
}
}
