Modeling acoustic attenuation of discrete stochastic fractured media View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-12

AUTHORS

Guiwu Chen, Lei Song, Ray Ruichong Zhang

ABSTRACT

The acoustic response has many important roles in seismic exploration and nondestructive testing. It enables the development of fracture classification and sizing. In this paper, we combined Hudson’s effective medium scheme and finite-difference time-domain modeling method to simulate acoustic wave propagation in fractured media. Fractures are represented by discrete fracture networks, allowing for a state-of-the-art representation of natural fracture networks by a negative Exponential Law length distribution. The propagation of acoustic waves that are emitted by a point source and reflected from a fractured area in a 2D digital rock model are examined numerically with the purpose of developing an acoustic inference of fracture properties. In these fractured models, we vary the number and mean length of fractures to explore the relation between internal structure of rock and acoustic wave field characters. The modeling results indicate that acoustic wave field is more sensitive to the fracture number than to the mean of the fracture length. Moreover, a fracture-dependent attenuation analysis of the reflection records of discrete stochastic fractured models is obtained. The frequency- and time- dependent attenuation profiles feature two parts in frequency, (1) fracture-to-background at lower frequencies and (2) fracture-to-fracture at higher frequencies. Our results indicate that accounting for attenuation effects may not only allow for improving estimation of fracture number, but also provide information about geometrical characteristics of length distribution. Such an approach can be used to estimate nature fracture network properties with given acoustic records. More... »

PAGES

1-12

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40328-018-0237-9

DOI

http://dx.doi.org/10.1007/s40328-018-0237-9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1109824838


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/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "China University of Mining and Technology", 
          "id": "https://www.grid.ac/institutes/grid.411510.0", 
          "name": [
            "State Key Laboratory of Geomechanics and Deep Underground Engineering, China University of Mining and Technology, 221116, Xuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chen", 
        "givenName": "Guiwu", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "China University of Mining and Technology", 
          "id": "https://www.grid.ac/institutes/grid.411510.0", 
          "name": [
            "State Key Laboratory of Geomechanics and Deep Underground Engineering, China University of Mining and Technology, 221116, Xuzhou, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Song", 
        "givenName": "Lei", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Colorado School of Mines", 
          "id": "https://www.grid.ac/institutes/grid.254549.b", 
          "name": [
            "Department of Civil and Environmental Engineering, Colorado School of Mines, 80401, Golden, CO, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Ray Ruichong", 
        "id": "sg:person.013657746057.45", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013657746057.45"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s40328-015-0146-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003551949", 
          "https://doi.org/10.1007/s40328-015-0146-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1190/1.3463417", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026821150"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10704-006-0105-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027262561", 
          "https://doi.org/10.1007/s10704-006-0105-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0098-3004(02)00006-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1035885830"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/2002jb001824", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039842573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmst.2015.03.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042701211"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1190/1.1444865", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044815669"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1190/1.2732690", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046086025"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/1999jb900306", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049111811"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1190/1.1444505", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050815837"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.13168/agg.2015.0049", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1065005436"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.cageo.2017.02.002", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083546545"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1190/int-2016-0149.1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1083941684"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40328-017-0199-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084521081", 
          "https://doi.org/10.1007/s40328-017-0199-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40328-017-0199-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084521081", 
          "https://doi.org/10.1007/s40328-017-0199-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1742-2140/aa5af8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084601071"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.apacoust.2017.04.019", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085203283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmst.2017.05.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085446524"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2017jb014558", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1092139947"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/2017jb014566", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1100150495"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ijmst.2018.02.005", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1101238007"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-12", 
    "datePublishedReg": "2018-12-01", 
    "description": "The acoustic response has many important roles in seismic exploration and nondestructive testing. It enables the development of fracture classification and sizing. In this paper, we combined Hudson\u2019s effective medium scheme and finite-difference time-domain modeling method to simulate acoustic wave propagation in fractured media. Fractures are represented by discrete fracture networks, allowing for a state-of-the-art representation of natural fracture networks by a negative Exponential Law length distribution. The propagation of acoustic waves that are emitted by a point source and reflected from a fractured area in a 2D digital rock model are examined numerically with the purpose of developing an acoustic inference of fracture properties. In these fractured models, we vary the number and mean length of fractures to explore the relation between internal structure of rock and acoustic wave field characters. The modeling results indicate that acoustic wave field is more sensitive to the fracture number than to the mean of the fracture length. Moreover, a fracture-dependent attenuation analysis of the reflection records of discrete stochastic fractured models is obtained. The frequency- and time- dependent attenuation profiles feature two parts in frequency, (1) fracture-to-background at lower frequencies and (2) fracture-to-fracture at higher frequencies. Our results indicate that accounting for attenuation effects may not only allow for improving estimation of fracture number, but also provide information about geometrical characteristics of length distribution. Such an approach can be used to estimate nature fracture network properties with given acoustic records.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s40328-018-0237-9", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1136254", 
        "issn": [
          "2213-5812", 
          "2213-5820"
        ], 
        "name": "Acta Geodaetica et Geophysica", 
        "type": "Periodical"
      }
    ], 
    "name": "Modeling acoustic attenuation of discrete stochastic fractured media", 
    "pagination": "1-12", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "b38e841e1f23c4a06a22158ed5ee874d86ba99d502faf50d29a27edb5c5eb7fc"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s40328-018-0237-9"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1109824838"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s40328-018-0237-9", 
      "https://app.dimensions.ai/details/publication/pub.1109824838"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T16:56", 
    "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_8669_00000610.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1007%2Fs40328-018-0237-9"
  }
]
 

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/s40328-018-0237-9'

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/s40328-018-0237-9'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s40328-018-0237-9'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s40328-018-0237-9'


 

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

133 TRIPLES      21 PREDICATES      45 URIs      17 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s40328-018-0237-9 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author N836c8f99fc3946c5aeb5567e5929e64f
4 schema:citation sg:pub.10.1007/s10704-006-0105-4
5 sg:pub.10.1007/s40328-015-0146-0
6 sg:pub.10.1007/s40328-017-0199-3
7 https://doi.org/10.1002/2017jb014558
8 https://doi.org/10.1002/2017jb014566
9 https://doi.org/10.1016/j.apacoust.2017.04.019
10 https://doi.org/10.1016/j.cageo.2017.02.002
11 https://doi.org/10.1016/j.ijmst.2015.03.019
12 https://doi.org/10.1016/j.ijmst.2017.05.003
13 https://doi.org/10.1016/j.ijmst.2018.02.005
14 https://doi.org/10.1016/s0098-3004(02)00006-7
15 https://doi.org/10.1029/1999jb900306
16 https://doi.org/10.1029/2002jb001824
17 https://doi.org/10.1088/1742-2140/aa5af8
18 https://doi.org/10.1190/1.1444505
19 https://doi.org/10.1190/1.1444865
20 https://doi.org/10.1190/1.2732690
21 https://doi.org/10.1190/1.3463417
22 https://doi.org/10.1190/int-2016-0149.1
23 https://doi.org/10.13168/agg.2015.0049
24 schema:datePublished 2018-12
25 schema:datePublishedReg 2018-12-01
26 schema:description The acoustic response has many important roles in seismic exploration and nondestructive testing. It enables the development of fracture classification and sizing. In this paper, we combined Hudson’s effective medium scheme and finite-difference time-domain modeling method to simulate acoustic wave propagation in fractured media. Fractures are represented by discrete fracture networks, allowing for a state-of-the-art representation of natural fracture networks by a negative Exponential Law length distribution. The propagation of acoustic waves that are emitted by a point source and reflected from a fractured area in a 2D digital rock model are examined numerically with the purpose of developing an acoustic inference of fracture properties. In these fractured models, we vary the number and mean length of fractures to explore the relation between internal structure of rock and acoustic wave field characters. The modeling results indicate that acoustic wave field is more sensitive to the fracture number than to the mean of the fracture length. Moreover, a fracture-dependent attenuation analysis of the reflection records of discrete stochastic fractured models is obtained. The frequency- and time- dependent attenuation profiles feature two parts in frequency, (1) fracture-to-background at lower frequencies and (2) fracture-to-fracture at higher frequencies. Our results indicate that accounting for attenuation effects may not only allow for improving estimation of fracture number, but also provide information about geometrical characteristics of length distribution. Such an approach can be used to estimate nature fracture network properties with given acoustic records.
27 schema:genre research_article
28 schema:inLanguage en
29 schema:isAccessibleForFree false
30 schema:isPartOf sg:journal.1136254
31 schema:name Modeling acoustic attenuation of discrete stochastic fractured media
32 schema:pagination 1-12
33 schema:productId N338fae75e2ee4d909b3bf33fe40cfff6
34 N34d3382f99754ee1b824fb306f78e8fb
35 Nef104b9f9ebf48bfb89dd216d11a7b71
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109824838
37 https://doi.org/10.1007/s40328-018-0237-9
38 schema:sdDatePublished 2019-04-10T16:56
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher Nca7cf49871344f02bfdbc10e2d1f6faf
41 schema:url https://link.springer.com/10.1007%2Fs40328-018-0237-9
42 sgo:license sg:explorer/license/
43 sgo:sdDataset articles
44 rdf:type schema:ScholarlyArticle
45 N338fae75e2ee4d909b3bf33fe40cfff6 schema:name readcube_id
46 schema:value b38e841e1f23c4a06a22158ed5ee874d86ba99d502faf50d29a27edb5c5eb7fc
47 rdf:type schema:PropertyValue
48 N34d3382f99754ee1b824fb306f78e8fb schema:name dimensions_id
49 schema:value pub.1109824838
50 rdf:type schema:PropertyValue
51 N620a4a9ca59c474c845dc81dd2bb1179 rdf:first sg:person.013657746057.45
52 rdf:rest rdf:nil
53 N7912377402c04787b82511545c548b8b schema:affiliation https://www.grid.ac/institutes/grid.411510.0
54 schema:familyName Chen
55 schema:givenName Guiwu
56 rdf:type schema:Person
57 N836c8f99fc3946c5aeb5567e5929e64f rdf:first N7912377402c04787b82511545c548b8b
58 rdf:rest Nb6fe01be11be4a69ba9e671a88af7bf0
59 N8c3df72b85ff4092a08cb48909c07bb9 schema:affiliation https://www.grid.ac/institutes/grid.411510.0
60 schema:familyName Song
61 schema:givenName Lei
62 rdf:type schema:Person
63 Nb6fe01be11be4a69ba9e671a88af7bf0 rdf:first N8c3df72b85ff4092a08cb48909c07bb9
64 rdf:rest N620a4a9ca59c474c845dc81dd2bb1179
65 Nca7cf49871344f02bfdbc10e2d1f6faf schema:name Springer Nature - SN SciGraph project
66 rdf:type schema:Organization
67 Nef104b9f9ebf48bfb89dd216d11a7b71 schema:name doi
68 schema:value 10.1007/s40328-018-0237-9
69 rdf:type schema:PropertyValue
70 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
71 schema:name Mathematical Sciences
72 rdf:type schema:DefinedTerm
73 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
74 schema:name Statistics
75 rdf:type schema:DefinedTerm
76 sg:journal.1136254 schema:issn 2213-5812
77 2213-5820
78 schema:name Acta Geodaetica et Geophysica
79 rdf:type schema:Periodical
80 sg:person.013657746057.45 schema:affiliation https://www.grid.ac/institutes/grid.254549.b
81 schema:familyName Zhang
82 schema:givenName Ray Ruichong
83 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013657746057.45
84 rdf:type schema:Person
85 sg:pub.10.1007/s10704-006-0105-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027262561
86 https://doi.org/10.1007/s10704-006-0105-4
87 rdf:type schema:CreativeWork
88 sg:pub.10.1007/s40328-015-0146-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003551949
89 https://doi.org/10.1007/s40328-015-0146-0
90 rdf:type schema:CreativeWork
91 sg:pub.10.1007/s40328-017-0199-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084521081
92 https://doi.org/10.1007/s40328-017-0199-3
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1002/2017jb014558 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092139947
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1002/2017jb014566 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100150495
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1016/j.apacoust.2017.04.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085203283
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1016/j.cageo.2017.02.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083546545
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1016/j.ijmst.2015.03.019 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042701211
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/j.ijmst.2017.05.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085446524
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1016/j.ijmst.2018.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1101238007
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1016/s0098-3004(02)00006-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035885830
109 rdf:type schema:CreativeWork
110 https://doi.org/10.1029/1999jb900306 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049111811
111 rdf:type schema:CreativeWork
112 https://doi.org/10.1029/2002jb001824 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039842573
113 rdf:type schema:CreativeWork
114 https://doi.org/10.1088/1742-2140/aa5af8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084601071
115 rdf:type schema:CreativeWork
116 https://doi.org/10.1190/1.1444505 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050815837
117 rdf:type schema:CreativeWork
118 https://doi.org/10.1190/1.1444865 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044815669
119 rdf:type schema:CreativeWork
120 https://doi.org/10.1190/1.2732690 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046086025
121 rdf:type schema:CreativeWork
122 https://doi.org/10.1190/1.3463417 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026821150
123 rdf:type schema:CreativeWork
124 https://doi.org/10.1190/int-2016-0149.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083941684
125 rdf:type schema:CreativeWork
126 https://doi.org/10.13168/agg.2015.0049 schema:sameAs https://app.dimensions.ai/details/publication/pub.1065005436
127 rdf:type schema:CreativeWork
128 https://www.grid.ac/institutes/grid.254549.b schema:alternateName Colorado School of Mines
129 schema:name Department of Civil and Environmental Engineering, Colorado School of Mines, 80401, Golden, CO, USA
130 rdf:type schema:Organization
131 https://www.grid.ac/institutes/grid.411510.0 schema:alternateName China University of Mining and Technology
132 schema:name State Key Laboratory of Geomechanics and Deep Underground Engineering, China University of Mining and Technology, 221116, Xuzhou, China
133 rdf:type schema:Organization
 




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


...