Spatio-Temporal Analysis of Eearthquake Occurrences Using a Multiresolution Approach View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014

AUTHORS

Orietta Nicolis

ABSTRACT

Catalogs of earthquake occurrences are conveniently modeled as spatial-temporal marked point processes. The widely used Epidemic-Type Aftershock Sequence (ETAS) models has proven to be extremely useful in the description and modeling of earthquake occurrence times and locations. The basic idea of ETAS models is that the conditional intensity function is composed by a long-term background component and a short-term, time-dependent clustering component, which represents the aftershock activity. Many extensions have been proposed in order to incorporate some geophysical information (e.g. orientation of fault-rupture). In this work we propose a multiresolution approach based on directional wavelet transforms for mapping the background seismicity, or to estimate the moment rate in a seismic area. The aim is to identify anisotropic spatial patterns and estimate the background earthquake rate in ETAS models. Then we apply the proposed methodology to the earthquake catalogue of Chile and discuss the effects of spatial clustering for the past earthquake events. Finally, we produce hazard maps in order to identify the areas with highest seismic risk, which are in turn critical for many purposes, including civil engineering and insurances. More... »

PAGES

179-183

References to SciGraph publications

Book

TITLE

Mathematics of Planet Earth

ISBN

978-3-642-32407-9
978-3-642-32408-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-32408-6_42

DOI

http://dx.doi.org/10.1007/978-3-642-32408-6_42

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034630059


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": "University of Valpara\u00edso", 
          "id": "https://www.grid.ac/institutes/grid.412185.b", 
          "name": [
            "Departamento de Estadistica, Universidad de Valparaiso, Gran Breta\u00f1a 1111, Valparaiso, PA, Chile"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nicolis", 
        "givenName": "Orietta", 
        "id": "sg:person.013666355451.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013666355451.95"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1111/j.1365-246x.1991.tb04607.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006757343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1365-246x.1991.tb04607.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006757343"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/95gl02934", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015978981"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1785/0120090340", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022298436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cageo.2004.10.014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024159883"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1003403601725", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035882021", 
          "https://doi.org/10.1023/a:1003403601725"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1080/01621459.1988.10478560", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1058303537"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1198/016214502760046925", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1064197998"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/acssc.1997.679073", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095596750"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "Catalogs of earthquake occurrences are conveniently modeled as spatial-temporal marked point processes. The widely used Epidemic-Type Aftershock Sequence (ETAS) models has proven to be extremely useful in the description and modeling of earthquake occurrence times and locations. The basic idea of ETAS models is that the conditional intensity function is composed by a long-term background component and a short-term, time-dependent clustering component, which represents the aftershock activity. Many extensions have been proposed in order to incorporate some geophysical information (e.g. orientation of fault-rupture). In this work we propose a multiresolution approach based on directional wavelet transforms for mapping the background seismicity, or to estimate the moment rate in a seismic area. The aim is to identify anisotropic spatial patterns and estimate the background earthquake rate in ETAS models. Then we apply the proposed methodology to the earthquake catalogue of Chile and discuss the effects of spatial clustering for the past earthquake events. Finally, we produce hazard maps in order to identify the areas with highest seismic risk, which are in turn critical for many purposes, including civil engineering and insurances.", 
    "editor": [
      {
        "familyName": "Pardo-Ig\u00fazquiza", 
        "givenName": "Eulogio", 
        "type": "Person"
      }, 
      {
        "familyName": "Guardiola-Albert", 
        "givenName": "Carolina", 
        "type": "Person"
      }, 
      {
        "familyName": "Heredia", 
        "givenName": "Javier", 
        "type": "Person"
      }, 
      {
        "familyName": "Moreno-Merino", 
        "givenName": "Luis", 
        "type": "Person"
      }, 
      {
        "familyName": "Dur\u00e1n", 
        "givenName": "Juan Jos\u00e9", 
        "type": "Person"
      }, 
      {
        "familyName": "Vargas-Guzm\u00e1n", 
        "givenName": "Jose Antonio", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-32408-6_42", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-32407-9", 
        "978-3-642-32408-6"
      ], 
      "name": "Mathematics of Planet Earth", 
      "type": "Book"
    }, 
    "name": "Spatio-Temporal Analysis of Eearthquake Occurrences Using a Multiresolution Approach", 
    "pagination": "179-183", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-32408-6_42"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "a4f708f7308b55530b675995e7fbcb61c5d706acb0c4b02e0dc66a8f18dc20ec"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034630059"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-32408-6_42", 
      "https://app.dimensions.ai/details/publication/pub.1034630059"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T12:33", 
    "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/v90/0000000001_0000000264/records_8663_00000264.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-642-32408-6_42"
  }
]
 

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.10.1007/978-3-642-32408-6_42'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-32408-6_42'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-32408-6_42'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-32408-6_42'


 

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

115 TRIPLES      23 PREDICATES      35 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-32408-6_42 schema:about anzsrc-for:04
2 anzsrc-for:0404
3 schema:author N6c33959dbcb542b7b0d82ca03d2c1c06
4 schema:citation sg:pub.10.1023/a:1003403601725
5 https://doi.org/10.1016/j.cageo.2004.10.014
6 https://doi.org/10.1029/95gl02934
7 https://doi.org/10.1080/01621459.1988.10478560
8 https://doi.org/10.1109/acssc.1997.679073
9 https://doi.org/10.1111/j.1365-246x.1991.tb04607.x
10 https://doi.org/10.1198/016214502760046925
11 https://doi.org/10.1785/0120090340
12 schema:datePublished 2014
13 schema:datePublishedReg 2014-01-01
14 schema:description Catalogs of earthquake occurrences are conveniently modeled as spatial-temporal marked point processes. The widely used Epidemic-Type Aftershock Sequence (ETAS) models has proven to be extremely useful in the description and modeling of earthquake occurrence times and locations. The basic idea of ETAS models is that the conditional intensity function is composed by a long-term background component and a short-term, time-dependent clustering component, which represents the aftershock activity. Many extensions have been proposed in order to incorporate some geophysical information (e.g. orientation of fault-rupture). In this work we propose a multiresolution approach based on directional wavelet transforms for mapping the background seismicity, or to estimate the moment rate in a seismic area. The aim is to identify anisotropic spatial patterns and estimate the background earthquake rate in ETAS models. Then we apply the proposed methodology to the earthquake catalogue of Chile and discuss the effects of spatial clustering for the past earthquake events. Finally, we produce hazard maps in order to identify the areas with highest seismic risk, which are in turn critical for many purposes, including civil engineering and insurances.
15 schema:editor N0cf6187005e640ce9bf142dd163c09e7
16 schema:genre chapter
17 schema:inLanguage en
18 schema:isAccessibleForFree false
19 schema:isPartOf Ndd96d0e898ea4543a03e82b0850686a6
20 schema:name Spatio-Temporal Analysis of Eearthquake Occurrences Using a Multiresolution Approach
21 schema:pagination 179-183
22 schema:productId Nb7e7e2de3126405e90e3026269b00653
23 Ne9b77d2afe684b0e9f2057fe421c9655
24 Ned7c1ff898cb4401b15f44c3c9f7baad
25 schema:publisher N6d60f8960704463fb280cbfd6e5369c4
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034630059
27 https://doi.org/10.1007/978-3-642-32408-6_42
28 schema:sdDatePublished 2019-04-15T12:33
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher N768abd8a9958431c87165e4d0070e78c
31 schema:url http://link.springer.com/10.1007/978-3-642-32408-6_42
32 sgo:license sg:explorer/license/
33 sgo:sdDataset chapters
34 rdf:type schema:Chapter
35 N0cf6187005e640ce9bf142dd163c09e7 rdf:first N5cf2d1e0da4640bdb3ee75b1fb3efd78
36 rdf:rest N8c32433b076b464d8b6854ad9023452f
37 N0f2ddca5a0e045a2bbe601371870fb14 schema:familyName Vargas-Guzmán
38 schema:givenName Jose Antonio
39 rdf:type schema:Person
40 N35511238a5274b60b20c570f41108de1 rdf:first Nbfd251f4d11d4b1d95fc6ccb3c124da3
41 rdf:rest N43aa1dc4107745d5a9dc9a9d7960e5c4
42 N43aa1dc4107745d5a9dc9a9d7960e5c4 rdf:first N98d4cb5e5e39444488326dcd6b68eed9
43 rdf:rest Na7d30b8f2272471eb3617975dacb4599
44 N5880b98c81114b54b93567d1b3ef5ab1 schema:familyName Durán
45 schema:givenName Juan José
46 rdf:type schema:Person
47 N5ccb62780b774c09bfcb0427bd2a4536 schema:familyName Guardiola-Albert
48 schema:givenName Carolina
49 rdf:type schema:Person
50 N5cf2d1e0da4640bdb3ee75b1fb3efd78 schema:familyName Pardo-Igúzquiza
51 schema:givenName Eulogio
52 rdf:type schema:Person
53 N6c33959dbcb542b7b0d82ca03d2c1c06 rdf:first sg:person.013666355451.95
54 rdf:rest rdf:nil
55 N6d60f8960704463fb280cbfd6e5369c4 schema:location Berlin, Heidelberg
56 schema:name Springer Berlin Heidelberg
57 rdf:type schema:Organisation
58 N768abd8a9958431c87165e4d0070e78c schema:name Springer Nature - SN SciGraph project
59 rdf:type schema:Organization
60 N8c32433b076b464d8b6854ad9023452f rdf:first N5ccb62780b774c09bfcb0427bd2a4536
61 rdf:rest N35511238a5274b60b20c570f41108de1
62 N98d4cb5e5e39444488326dcd6b68eed9 schema:familyName Moreno-Merino
63 schema:givenName Luis
64 rdf:type schema:Person
65 N99a6cd68285c45e3affd1ecad6da823a rdf:first N0f2ddca5a0e045a2bbe601371870fb14
66 rdf:rest rdf:nil
67 Na7d30b8f2272471eb3617975dacb4599 rdf:first N5880b98c81114b54b93567d1b3ef5ab1
68 rdf:rest N99a6cd68285c45e3affd1ecad6da823a
69 Nb7e7e2de3126405e90e3026269b00653 schema:name readcube_id
70 schema:value a4f708f7308b55530b675995e7fbcb61c5d706acb0c4b02e0dc66a8f18dc20ec
71 rdf:type schema:PropertyValue
72 Nbfd251f4d11d4b1d95fc6ccb3c124da3 schema:familyName Heredia
73 schema:givenName Javier
74 rdf:type schema:Person
75 Ndd96d0e898ea4543a03e82b0850686a6 schema:isbn 978-3-642-32407-9
76 978-3-642-32408-6
77 schema:name Mathematics of Planet Earth
78 rdf:type schema:Book
79 Ne9b77d2afe684b0e9f2057fe421c9655 schema:name doi
80 schema:value 10.1007/978-3-642-32408-6_42
81 rdf:type schema:PropertyValue
82 Ned7c1ff898cb4401b15f44c3c9f7baad schema:name dimensions_id
83 schema:value pub.1034630059
84 rdf:type schema:PropertyValue
85 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
86 schema:name Earth Sciences
87 rdf:type schema:DefinedTerm
88 anzsrc-for:0404 schema:inDefinedTermSet anzsrc-for:
89 schema:name Geophysics
90 rdf:type schema:DefinedTerm
91 sg:person.013666355451.95 schema:affiliation https://www.grid.ac/institutes/grid.412185.b
92 schema:familyName Nicolis
93 schema:givenName Orietta
94 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013666355451.95
95 rdf:type schema:Person
96 sg:pub.10.1023/a:1003403601725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035882021
97 https://doi.org/10.1023/a:1003403601725
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1016/j.cageo.2004.10.014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024159883
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1029/95gl02934 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015978981
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1080/01621459.1988.10478560 schema:sameAs https://app.dimensions.ai/details/publication/pub.1058303537
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1109/acssc.1997.679073 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095596750
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1111/j.1365-246x.1991.tb04607.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006757343
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1198/016214502760046925 schema:sameAs https://app.dimensions.ai/details/publication/pub.1064197998
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1785/0120090340 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022298436
112 rdf:type schema:CreativeWork
113 https://www.grid.ac/institutes/grid.412185.b schema:alternateName University of Valparaíso
114 schema:name Departamento de Estadistica, Universidad de Valparaiso, Gran Bretaña 1111, Valparaiso, PA, Chile
115 rdf:type schema:Organization
 




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


...