Pre-computed tsunami inundation database and forecast simulation in Pelabuhan Ratu, Indonesia View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-08

AUTHORS

Urip Setiyono, Aditya Riadi Gusman, Kenji Satake, Yushiro Fujii

ABSTRACT

We built a pre-computed tsunami inundation database in Pelabuhan Ratu, one of tsunami-prone areas on the southern coast of Java, Indonesia, which can be employed for a rapid estimation of tsunami inundation during an event. The pre-computed tsunami waveforms and inundations are from a total of 340 scenarios ranging from 7.5 to 9.2 in moment magnitude scale (Mw), including simple fault models of 208 thrust faults and 44 tsunami earthquakes on the plate interface, as well as 44 normal faults and 44 reverse faults in the outer-rise region. Using our tsunami inundation forecasting algorithm (NearTIF), we could rapidly estimate the tsunami inundation in Pelabuhan Ratu for three different hypothetical earthquakes. The first hypothetical earthquake is a megathrust earthquake type (Mw 9.0) offshore Sumatra which is about 600 km from Pelabuhan Ratu to represent a worst-case event in the far-field. The second hypothetical earthquake (Mw 8.5) is based on a slip deficit rate estimation from geodetic measurements and represents a most likely large event. The third hypothetical earthquake is a tsunami earthquake type (Mw 8.1) which often occurs south of Java. We compared the tsunami inundation maps produced by the NearTIF algorithm with results of direct forward inundation modeling for the hypothetical earthquakes. The tsunami inundation maps produced from both methods are similar for the three cases. However, the tsunami inundation map from the inundation database can be obtained in much shorter time (1 min) than the one from a forward inundation modeling (40 min). These indicate that the NearTIF algorithm based on pre-computed inundation database is reliable and useful for tsunami warning purposes. This study also demonstrates that the NearTIF algorithm can work well, though the earthquake source is located outside the area of fault model database because it uses a time shifting procedure for the best-fit scenario searching. More... »

PAGES

3219-3235

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00024-017-1633-8

DOI

http://dx.doi.org/10.1007/s00024-017-1633-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090936608


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0404", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geophysics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Meteorological, Climatological, And Geophysical Agency", 
          "id": "https://www.grid.ac/institutes/grid.493867.7", 
          "name": [
            "Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Setiyono", 
        "givenName": "Urip", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Tokyo", 
          "id": "https://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Earthquake Research Institute (ERI), The University of Tokyo, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gusman", 
        "givenName": "Aditya Riadi", 
        "id": "sg:person.016372103151.91", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016372103151.91"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Tokyo", 
          "id": "https://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Earthquake Research Institute (ERI), The University of Tokyo, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Satake", 
        "givenName": "Kenji", 
        "id": "sg:person.015332735501.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015332735501.69"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Building Research Institute", 
          "id": "https://www.grid.ac/institutes/grid.471551.3", 
          "name": [
            "International Institute of Seismology and Earthquake Engineering, Building Research Institute, Tsukuba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fujii", 
        "givenName": "Yushiro", 
        "id": "sg:person.015760717711.92", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015760717711.92"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.epsl.2014.06.010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002999855"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/nhess-9-1381-2009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003944588"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/nhess-10-641-2010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003963631"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-9201(72)90058-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006216000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0031-9201(72)90058-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006216000"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/96gl00736", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006749829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5194/nhess-10-2611-2010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007232312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2010gl046498", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007332703"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2007gl031357", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007750182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-013-0680-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008094863", 
          "https://doi.org/10.1007/s00024-013-0680-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2014jb010958", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011204918"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2011jb008750", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011221371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-012-0536-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1011292356", 
          "https://doi.org/10.1007/s00024-012-0536-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-014-0964-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013184237", 
          "https://doi.org/10.1007/s00024-014-0964-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2015gl067100", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018752702"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2014gl062577", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021807092"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2007gl029404", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023229623"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl028005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023665608"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2016gl068786", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023703117"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/96gl01479", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024226580"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-10202-3_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025287571", 
          "https://doi.org/10.1007/978-3-319-10202-3_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1785/0120080324", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042071539"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1785/0120010148", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042526878"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2006gl028049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048230437"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/jb092ib01p00421", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050810085"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40623-016-0445-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051494702", 
          "https://doi.org/10.1186/s40623-016-0445-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/s40623-016-0445-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051494702", 
          "https://doi.org/10.1186/s40623-016-0445-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2328/jnds.22.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051697487"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2328/jnds.22.25", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051697487"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00874397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051755303", 
          "https://doi.org/10.1007/bf00874397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00874397", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051755303", 
          "https://doi.org/10.1007/bf00874397"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00024-015-1049-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052277323", 
          "https://doi.org/10.1007/s00024-015-1049-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2011jb008524", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052614605"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1094342015584090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063977364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1177/1094342015584090", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063977364"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.20965/jdr.2012.p0019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068820406"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.20965/jdr.2014.p0358", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1068820640"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-08", 
    "datePublishedReg": "2017-08-01", 
    "description": "We built a pre-computed tsunami inundation database in Pelabuhan Ratu, one of tsunami-prone areas on the southern coast of Java, Indonesia, which can be employed for a rapid estimation of tsunami inundation during an event. The pre-computed tsunami waveforms and inundations are from a total of 340 scenarios ranging from 7.5 to 9.2 in moment magnitude scale (Mw), including simple fault models of 208 thrust faults and 44 tsunami earthquakes on the plate interface, as well as 44 normal faults and 44 reverse faults in the outer-rise region. Using our tsunami inundation forecasting algorithm (NearTIF), we could rapidly estimate the tsunami inundation in Pelabuhan Ratu for three different hypothetical earthquakes. The first hypothetical earthquake is a megathrust earthquake type (Mw 9.0) offshore Sumatra which is about 600 km from Pelabuhan Ratu to represent a worst-case event in the far-field. The second hypothetical earthquake (Mw 8.5) is based on a slip deficit rate estimation from geodetic measurements and represents a most likely large event. The third hypothetical earthquake is a tsunami earthquake type (Mw 8.1) which often occurs south of Java. We compared the tsunami inundation maps produced by the NearTIF algorithm with results of direct forward inundation modeling for the hypothetical earthquakes. The tsunami inundation maps produced from both methods are similar for the three cases. However, the tsunami inundation map from the inundation database can be obtained in much shorter time (1 min) than the one from a forward inundation modeling (40 min). These indicate that the NearTIF algorithm based on pre-computed inundation database is reliable and useful for tsunami warning purposes. This study also demonstrates that the NearTIF algorithm can work well, though the earthquake source is located outside the area of fault model database because it uses a time shifting procedure for the best-fit scenario searching.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s00024-017-1633-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136817", 
        "issn": [
          "0033-4553", 
          "1420-9136"
        ], 
        "name": "Pure and Applied Geophysics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "174"
      }
    ], 
    "name": "Pre-computed tsunami inundation database and forecast simulation in Pelabuhan Ratu, Indonesia", 
    "pagination": "3219-3235", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "4641d3bc72176a392f5cad8c5b291574111c060aa0f7428cd6a7ac4ec41bb3a8"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00024-017-1633-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1090936608"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00024-017-1633-8", 
      "https://app.dimensions.ai/details/publication/pub.1090936608"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T09:58", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000347_0000000347/records_89812_00000003.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs00024-017-1633-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00024-017-1633-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00024-017-1633-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00024-017-1633-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00024-017-1633-8'


 

This table displays all metadata directly associated to this object as RDF triples.

190 TRIPLES      21 PREDICATES      59 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00024-017-1633-8 schema:about anzsrc-for:04
2 anzsrc-for:0404
3 schema:author N417da7be520d447291b361ac8ec6d2d7
4 schema:citation sg:pub.10.1007/978-3-319-10202-3_11
5 sg:pub.10.1007/bf00874397
6 sg:pub.10.1007/s00024-012-0536-y
7 sg:pub.10.1007/s00024-013-0680-z
8 sg:pub.10.1007/s00024-014-0964-y
9 sg:pub.10.1007/s00024-015-1049-2
10 sg:pub.10.1186/s40623-016-0445-x
11 https://doi.org/10.1002/2014gl062577
12 https://doi.org/10.1002/2014jb010958
13 https://doi.org/10.1002/2015gl067100
14 https://doi.org/10.1002/2016gl068786
15 https://doi.org/10.1016/0031-9201(72)90058-1
16 https://doi.org/10.1016/j.epsl.2014.06.010
17 https://doi.org/10.1029/2006gl028005
18 https://doi.org/10.1029/2006gl028049
19 https://doi.org/10.1029/2007gl029404
20 https://doi.org/10.1029/2007gl031357
21 https://doi.org/10.1029/2010gl046498
22 https://doi.org/10.1029/2011jb008524
23 https://doi.org/10.1029/2011jb008750
24 https://doi.org/10.1029/96gl00736
25 https://doi.org/10.1029/96gl01479
26 https://doi.org/10.1029/jb092ib01p00421
27 https://doi.org/10.1177/1094342015584090
28 https://doi.org/10.1785/0120010148
29 https://doi.org/10.1785/0120080324
30 https://doi.org/10.20965/jdr.2012.p0019
31 https://doi.org/10.20965/jdr.2014.p0358
32 https://doi.org/10.2328/jnds.22.25
33 https://doi.org/10.5194/nhess-10-2611-2010
34 https://doi.org/10.5194/nhess-10-641-2010
35 https://doi.org/10.5194/nhess-9-1381-2009
36 schema:datePublished 2017-08
37 schema:datePublishedReg 2017-08-01
38 schema:description We built a pre-computed tsunami inundation database in Pelabuhan Ratu, one of tsunami-prone areas on the southern coast of Java, Indonesia, which can be employed for a rapid estimation of tsunami inundation during an event. The pre-computed tsunami waveforms and inundations are from a total of 340 scenarios ranging from 7.5 to 9.2 in moment magnitude scale (Mw), including simple fault models of 208 thrust faults and 44 tsunami earthquakes on the plate interface, as well as 44 normal faults and 44 reverse faults in the outer-rise region. Using our tsunami inundation forecasting algorithm (NearTIF), we could rapidly estimate the tsunami inundation in Pelabuhan Ratu for three different hypothetical earthquakes. The first hypothetical earthquake is a megathrust earthquake type (Mw 9.0) offshore Sumatra which is about 600 km from Pelabuhan Ratu to represent a worst-case event in the far-field. The second hypothetical earthquake (Mw 8.5) is based on a slip deficit rate estimation from geodetic measurements and represents a most likely large event. The third hypothetical earthquake is a tsunami earthquake type (Mw 8.1) which often occurs south of Java. We compared the tsunami inundation maps produced by the NearTIF algorithm with results of direct forward inundation modeling for the hypothetical earthquakes. The tsunami inundation maps produced from both methods are similar for the three cases. However, the tsunami inundation map from the inundation database can be obtained in much shorter time (1 min) than the one from a forward inundation modeling (40 min). These indicate that the NearTIF algorithm based on pre-computed inundation database is reliable and useful for tsunami warning purposes. This study also demonstrates that the NearTIF algorithm can work well, though the earthquake source is located outside the area of fault model database because it uses a time shifting procedure for the best-fit scenario searching.
39 schema:genre research_article
40 schema:inLanguage en
41 schema:isAccessibleForFree false
42 schema:isPartOf N7865f5d0ece54338a4772469c6ddc35a
43 Nfe356168e52941648c32e849a3283823
44 sg:journal.1136817
45 schema:name Pre-computed tsunami inundation database and forecast simulation in Pelabuhan Ratu, Indonesia
46 schema:pagination 3219-3235
47 schema:productId N77e4fda12b4e489b8364912ab964bbc0
48 Ncc2cafad847f4962bae8a779f0992930
49 Nd1a34de6a4c34f6b8c2a01e33ac69c52
50 schema:sameAs https://app.dimensions.ai/details/publication/pub.1090936608
51 https://doi.org/10.1007/s00024-017-1633-8
52 schema:sdDatePublished 2019-04-11T09:58
53 schema:sdLicense https://scigraph.springernature.com/explorer/license/
54 schema:sdPublisher Nd51df3c3522a431880929237aa2d122c
55 schema:url https://link.springer.com/10.1007%2Fs00024-017-1633-8
56 sgo:license sg:explorer/license/
57 sgo:sdDataset articles
58 rdf:type schema:ScholarlyArticle
59 N34824b1fcc6047808d2c2dd9846d5adf schema:affiliation https://www.grid.ac/institutes/grid.493867.7
60 schema:familyName Setiyono
61 schema:givenName Urip
62 rdf:type schema:Person
63 N417da7be520d447291b361ac8ec6d2d7 rdf:first N34824b1fcc6047808d2c2dd9846d5adf
64 rdf:rest Nfa45214882e94064923cb721a80ca430
65 N77e4fda12b4e489b8364912ab964bbc0 schema:name readcube_id
66 schema:value 4641d3bc72176a392f5cad8c5b291574111c060aa0f7428cd6a7ac4ec41bb3a8
67 rdf:type schema:PropertyValue
68 N7865f5d0ece54338a4772469c6ddc35a schema:issueNumber 8
69 rdf:type schema:PublicationIssue
70 N890753533e734c029684670e3ff5f018 rdf:first sg:person.015332735501.69
71 rdf:rest Nf7a8827fd1b84220a4b3efa533eb1afc
72 Ncc2cafad847f4962bae8a779f0992930 schema:name doi
73 schema:value 10.1007/s00024-017-1633-8
74 rdf:type schema:PropertyValue
75 Nd1a34de6a4c34f6b8c2a01e33ac69c52 schema:name dimensions_id
76 schema:value pub.1090936608
77 rdf:type schema:PropertyValue
78 Nd51df3c3522a431880929237aa2d122c schema:name Springer Nature - SN SciGraph project
79 rdf:type schema:Organization
80 Nf7a8827fd1b84220a4b3efa533eb1afc rdf:first sg:person.015760717711.92
81 rdf:rest rdf:nil
82 Nfa45214882e94064923cb721a80ca430 rdf:first sg:person.016372103151.91
83 rdf:rest N890753533e734c029684670e3ff5f018
84 Nfe356168e52941648c32e849a3283823 schema:volumeNumber 174
85 rdf:type schema:PublicationVolume
86 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
87 schema:name Earth Sciences
88 rdf:type schema:DefinedTerm
89 anzsrc-for:0404 schema:inDefinedTermSet anzsrc-for:
90 schema:name Geophysics
91 rdf:type schema:DefinedTerm
92 sg:journal.1136817 schema:issn 0033-4553
93 1420-9136
94 schema:name Pure and Applied Geophysics
95 rdf:type schema:Periodical
96 sg:person.015332735501.69 schema:affiliation https://www.grid.ac/institutes/grid.26999.3d
97 schema:familyName Satake
98 schema:givenName Kenji
99 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015332735501.69
100 rdf:type schema:Person
101 sg:person.015760717711.92 schema:affiliation https://www.grid.ac/institutes/grid.471551.3
102 schema:familyName Fujii
103 schema:givenName Yushiro
104 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015760717711.92
105 rdf:type schema:Person
106 sg:person.016372103151.91 schema:affiliation https://www.grid.ac/institutes/grid.26999.3d
107 schema:familyName Gusman
108 schema:givenName Aditya Riadi
109 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016372103151.91
110 rdf:type schema:Person
111 sg:pub.10.1007/978-3-319-10202-3_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025287571
112 https://doi.org/10.1007/978-3-319-10202-3_11
113 rdf:type schema:CreativeWork
114 sg:pub.10.1007/bf00874397 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051755303
115 https://doi.org/10.1007/bf00874397
116 rdf:type schema:CreativeWork
117 sg:pub.10.1007/s00024-012-0536-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1011292356
118 https://doi.org/10.1007/s00024-012-0536-y
119 rdf:type schema:CreativeWork
120 sg:pub.10.1007/s00024-013-0680-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1008094863
121 https://doi.org/10.1007/s00024-013-0680-z
122 rdf:type schema:CreativeWork
123 sg:pub.10.1007/s00024-014-0964-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1013184237
124 https://doi.org/10.1007/s00024-014-0964-y
125 rdf:type schema:CreativeWork
126 sg:pub.10.1007/s00024-015-1049-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052277323
127 https://doi.org/10.1007/s00024-015-1049-2
128 rdf:type schema:CreativeWork
129 sg:pub.10.1186/s40623-016-0445-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051494702
130 https://doi.org/10.1186/s40623-016-0445-x
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1002/2014gl062577 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021807092
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1002/2014jb010958 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011204918
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1002/2015gl067100 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018752702
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1002/2016gl068786 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023703117
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1016/0031-9201(72)90058-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006216000
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1016/j.epsl.2014.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002999855
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1029/2006gl028005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023665608
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1029/2006gl028049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048230437
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1029/2007gl029404 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023229623
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1029/2007gl031357 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007750182
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1029/2010gl046498 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007332703
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1029/2011jb008524 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052614605
155 rdf:type schema:CreativeWork
156 https://doi.org/10.1029/2011jb008750 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011221371
157 rdf:type schema:CreativeWork
158 https://doi.org/10.1029/96gl00736 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006749829
159 rdf:type schema:CreativeWork
160 https://doi.org/10.1029/96gl01479 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024226580
161 rdf:type schema:CreativeWork
162 https://doi.org/10.1029/jb092ib01p00421 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050810085
163 rdf:type schema:CreativeWork
164 https://doi.org/10.1177/1094342015584090 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063977364
165 rdf:type schema:CreativeWork
166 https://doi.org/10.1785/0120010148 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042526878
167 rdf:type schema:CreativeWork
168 https://doi.org/10.1785/0120080324 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042071539
169 rdf:type schema:CreativeWork
170 https://doi.org/10.20965/jdr.2012.p0019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068820406
171 rdf:type schema:CreativeWork
172 https://doi.org/10.20965/jdr.2014.p0358 schema:sameAs https://app.dimensions.ai/details/publication/pub.1068820640
173 rdf:type schema:CreativeWork
174 https://doi.org/10.2328/jnds.22.25 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051697487
175 rdf:type schema:CreativeWork
176 https://doi.org/10.5194/nhess-10-2611-2010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007232312
177 rdf:type schema:CreativeWork
178 https://doi.org/10.5194/nhess-10-641-2010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003963631
179 rdf:type schema:CreativeWork
180 https://doi.org/10.5194/nhess-9-1381-2009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003944588
181 rdf:type schema:CreativeWork
182 https://www.grid.ac/institutes/grid.26999.3d schema:alternateName University of Tokyo
183 schema:name Earthquake Research Institute (ERI), The University of Tokyo, Tokyo, Japan
184 rdf:type schema:Organization
185 https://www.grid.ac/institutes/grid.471551.3 schema:alternateName Building Research Institute
186 schema:name International Institute of Seismology and Earthquake Engineering, Building Research Institute, Tsukuba, Japan
187 rdf:type schema:Organization
188 https://www.grid.ac/institutes/grid.493867.7 schema:alternateName Meteorological, Climatological, And Geophysical Agency
189 schema:name Agency for Meteorology, Climatology and Geophysics (BMKG), Jakarta, Indonesia
190 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...