Fault detection in seismic data using graph convolutional network
Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph represe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Palo, Patitapaban [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| Übergeordnetes Werk: |
Enthalten in: The journal of supercomputing - Springer US, 1987, 79(2023), 11 vom: 18. März, Seite 12737-12765 |
|---|---|
| Übergeordnetes Werk: |
volume:79 ; year:2023 ; number:11 ; day:18 ; month:03 ; pages:12737-12765 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11227-023-05173-8 |
|---|
| Katalog-ID: |
OLC2143780516 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2143780516 | ||
| 003 | DE-627 | ||
| 005 | 20240118091717.0 | ||
| 007 | tu | ||
| 008 | 240118s2023 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11227-023-05173-8 |2 doi | |
| 035 | |a (DE-627)OLC2143780516 | ||
| 035 | |a (DE-He213)s11227-023-05173-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 004 |a 620 |q VZ |
| 100 | 1 | |a Palo, Patitapaban |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Fault detection in seismic data using graph convolutional network |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. | ||
| 650 | 4 | |a Graph convolutional network (GCN) | |
| 650 | 4 | |a Graph neural network (GNN) | |
| 650 | 4 | |a Seismic fault interpretation | |
| 700 | 1 | |a Routray, Aurobinda |4 aut | |
| 700 | 1 | |a Mahadik, Rahul |4 aut | |
| 700 | 1 | |a Singh, Sanjai |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t The journal of supercomputing |d Springer US, 1987 |g 79(2023), 11 vom: 18. März, Seite 12737-12765 |w (DE-627)13046466X |w (DE-600)740510-8 |w (DE-576)018667775 |x 0920-8542 |7 nnns |
| 773 | 1 | 8 | |g volume:79 |g year:2023 |g number:11 |g day:18 |g month:03 |g pages:12737-12765 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11227-023-05173-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-MAT | ||
| 951 | |a AR | ||
| 952 | |d 79 |j 2023 |e 11 |b 18 |c 03 |h 12737-12765 | ||
| author_variant |
p p pp a r ar r m rm s s ss |
|---|---|
| matchkey_str |
article:09208542:2023----::aldtcinnesidtuigrpcno |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s11227-023-05173-8 doi (DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Palo, Patitapaban verfasserin aut Fault detection in seismic data using graph convolutional network 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation Routray, Aurobinda aut Mahadik, Rahul aut Singh, Sanjai aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 11 vom: 18. März, Seite 12737-12765 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 https://doi.org/10.1007/s11227-023-05173-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 11 18 03 12737-12765 |
| spelling |
10.1007/s11227-023-05173-8 doi (DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Palo, Patitapaban verfasserin aut Fault detection in seismic data using graph convolutional network 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation Routray, Aurobinda aut Mahadik, Rahul aut Singh, Sanjai aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 11 vom: 18. März, Seite 12737-12765 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 https://doi.org/10.1007/s11227-023-05173-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 11 18 03 12737-12765 |
| allfields_unstemmed |
10.1007/s11227-023-05173-8 doi (DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Palo, Patitapaban verfasserin aut Fault detection in seismic data using graph convolutional network 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation Routray, Aurobinda aut Mahadik, Rahul aut Singh, Sanjai aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 11 vom: 18. März, Seite 12737-12765 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 https://doi.org/10.1007/s11227-023-05173-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 11 18 03 12737-12765 |
| allfieldsGer |
10.1007/s11227-023-05173-8 doi (DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Palo, Patitapaban verfasserin aut Fault detection in seismic data using graph convolutional network 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation Routray, Aurobinda aut Mahadik, Rahul aut Singh, Sanjai aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 11 vom: 18. März, Seite 12737-12765 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 https://doi.org/10.1007/s11227-023-05173-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 11 18 03 12737-12765 |
| allfieldsSound |
10.1007/s11227-023-05173-8 doi (DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p DE-627 ger DE-627 rakwb eng 004 620 VZ Palo, Patitapaban verfasserin aut Fault detection in seismic data using graph convolutional network 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation Routray, Aurobinda aut Mahadik, Rahul aut Singh, Sanjai aut Enthalten in The journal of supercomputing Springer US, 1987 79(2023), 11 vom: 18. März, Seite 12737-12765 (DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 0920-8542 nnns volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 https://doi.org/10.1007/s11227-023-05173-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT AR 79 2023 11 18 03 12737-12765 |
| language |
English |
| source |
Enthalten in The journal of supercomputing 79(2023), 11 vom: 18. März, Seite 12737-12765 volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 |
| sourceStr |
Enthalten in The journal of supercomputing 79(2023), 11 vom: 18. März, Seite 12737-12765 volume:79 year:2023 number:11 day:18 month:03 pages:12737-12765 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
The journal of supercomputing |
| authorswithroles_txt_mv |
Palo, Patitapaban @@aut@@ Routray, Aurobinda @@aut@@ Mahadik, Rahul @@aut@@ Singh, Sanjai @@aut@@ |
| publishDateDaySort_date |
2023-03-18T00:00:00Z |
| hierarchy_top_id |
13046466X |
| dewey-sort |
14 |
| id |
OLC2143780516 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2143780516</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118091717.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-023-05173-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2143780516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-023-05173-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Palo, Patitapaban</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fault detection in seismic data using graph convolutional network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph convolutional network (GCN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph neural network (GNN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seismic fault interpretation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Routray, Aurobinda</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahadik, Rahul</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Sanjai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">79(2023), 11 vom: 18. März, Seite 12737-12765</subfield><subfield code="w">(DE-627)13046466X</subfield><subfield code="w">(DE-600)740510-8</subfield><subfield code="w">(DE-576)018667775</subfield><subfield code="x">0920-8542</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:79</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:11</subfield><subfield code="g">day:18</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:12737-12765</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11227-023-05173-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">79</subfield><subfield code="j">2023</subfield><subfield code="e">11</subfield><subfield code="b">18</subfield><subfield code="c">03</subfield><subfield code="h">12737-12765</subfield></datafield></record></collection>
|
| author |
Palo, Patitapaban |
| spellingShingle |
Palo, Patitapaban ddc 004 misc Graph convolutional network (GCN) misc Graph neural network (GNN) misc Seismic fault interpretation Fault detection in seismic data using graph convolutional network |
| authorStr |
Palo, Patitapaban |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)13046466X |
| format |
Article |
| dewey-ones |
004 - Data processing & computer science 620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0920-8542 |
| topic_title |
004 620 VZ Fault detection in seismic data using graph convolutional network Graph convolutional network (GCN) Graph neural network (GNN) Seismic fault interpretation |
| topic |
ddc 004 misc Graph convolutional network (GCN) misc Graph neural network (GNN) misc Seismic fault interpretation |
| topic_unstemmed |
ddc 004 misc Graph convolutional network (GCN) misc Graph neural network (GNN) misc Seismic fault interpretation |
| topic_browse |
ddc 004 misc Graph convolutional network (GCN) misc Graph neural network (GNN) misc Seismic fault interpretation |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
The journal of supercomputing |
| hierarchy_parent_id |
13046466X |
| dewey-tens |
000 - Computer science, knowledge & systems 620 - Engineering |
| hierarchy_top_title |
The journal of supercomputing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)13046466X (DE-600)740510-8 (DE-576)018667775 |
| title |
Fault detection in seismic data using graph convolutional network |
| ctrlnum |
(DE-627)OLC2143780516 (DE-He213)s11227-023-05173-8-p |
| title_full |
Fault detection in seismic data using graph convolutional network |
| author_sort |
Palo, Patitapaban |
| journal |
The journal of supercomputing |
| journalStr |
The journal of supercomputing |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
12737 |
| author_browse |
Palo, Patitapaban Routray, Aurobinda Mahadik, Rahul Singh, Sanjai |
| container_volume |
79 |
| class |
004 620 VZ |
| format_se |
Aufsätze |
| author-letter |
Palo, Patitapaban |
| doi_str_mv |
10.1007/s11227-023-05173-8 |
| dewey-full |
004 620 |
| title_sort |
fault detection in seismic data using graph convolutional network |
| title_auth |
Fault detection in seismic data using graph convolutional network |
| abstract |
Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT |
| container_issue |
11 |
| title_short |
Fault detection in seismic data using graph convolutional network |
| url |
https://doi.org/10.1007/s11227-023-05173-8 |
| remote_bool |
false |
| author2 |
Routray, Aurobinda Mahadik, Rahul Singh, Sanjai |
| author2Str |
Routray, Aurobinda Mahadik, Rahul Singh, Sanjai |
| ppnlink |
13046466X |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11227-023-05173-8 |
| up_date |
2024-07-03T18:02:22.568Z |
| _version_ |
1803581907347177472 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2143780516</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118091717.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11227-023-05173-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2143780516</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11227-023-05173-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Palo, Patitapaban</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Fault detection in seismic data using graph convolutional network</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Generally, seismic data have discontinuity or planar fracture anomalies in the volume of rocks, commonly known as seismic faults. A large number of methods exist to interpret seismic faults, nevertheless automating the process prevails to be a practical challenge. We propose a graph representation-based approach for interpreting faults in seismic data using the graph convolutional network (GCN). We extract 2D patches of data centered around seismic data points from the 3D seismic volumes for training. Then we represent these patches in the graph domain using the k-nearest neighbor graphs followed by the application of GCN. After training the patches in the networks, we classify the patches to identify the faults. We consider both synthetic and real data for training and testing. The seismic amplitude values, the seismic attribute values, and the successive difference values are used in the networks. We compare our implementation with the state-of-the-art method, the convolutional neural networks (CNN). The results show good accuracy when applied to both synthetic and real data. Additionally, the GCN method is more time efficient than that of CNN. We process the 3D seismic section by parallel processing the 2D patches.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph convolutional network (GCN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph neural network (GNN)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seismic fault interpretation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Routray, Aurobinda</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mahadik, Rahul</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Singh, Sanjai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">The journal of supercomputing</subfield><subfield code="d">Springer US, 1987</subfield><subfield code="g">79(2023), 11 vom: 18. März, Seite 12737-12765</subfield><subfield code="w">(DE-627)13046466X</subfield><subfield code="w">(DE-600)740510-8</subfield><subfield code="w">(DE-576)018667775</subfield><subfield code="x">0920-8542</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:79</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:11</subfield><subfield code="g">day:18</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:12737-12765</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11227-023-05173-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">79</subfield><subfield code="j">2023</subfield><subfield code="e">11</subfield><subfield code="b">18</subfield><subfield code="c">03</subfield><subfield code="h">12737-12765</subfield></datafield></record></collection>
|
| score |
7.400979 |