An Efficient Deep Learning Image Condition for Locating Earthquakes Article Swipe
Wenhuan Kuang
,
Jie Zhang
,
Wei Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.05087
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2304.05087
Migration-based earthquake location methods may encounter the polarity reversal issue due to the non-explosive components of seismic source, leading to an unfocused migration image. Various methods have been proposed, yet producing an ideally focused migration source image is still a challenge. In this study, by taking advantage of the general pattern recognition ability of convolutional neural network, we propose a novel Deep Learning Image Condition (DLIC) to address this issue. The proposed DLIC measures the goodness of waveform alignments for both P- and S-waves after correcting their traveltime moveouts and it follows the geophysical principle of seismic imaging that the best-aligned waveforms utterly represent a best-imaged source location. Both a synthetic test and real data application are used to show the effectiveness and merits of the proposed DLIC. A test on synthetic data shows that the DLIC can effectively overcome the polarity reversal issues in the data. Real data application to southern California shows that the DLIC can greatly enhance the focusing of migrated source image over the widely used source scanning algorithm. Further tests show that the DLIC is applicable to continuous seismic data, to regions with few historical earthquakes, and has the potential to locate small earthquakes. The proposed DLIC shall benefit the migration-based source location methods.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2304.05087
- https://arxiv.org/pdf/2304.05087
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4365475042
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4365475042Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2304.05087Digital Object Identifier
- Title
-
An Efficient Deep Learning Image Condition for Locating EarthquakesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-11Full publication date if available
- Authors
-
Wenhuan Kuang, Jie Zhang, Wei ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2304.05087Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2304.05087Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2304.05087Direct OA link when available
- Concepts
-
Computer science, Image (mathematics), Waveform, Seismology, Convolutional neural network, Synthetic data, Polarity (international relations), Seismic migration, Deep learning, Pattern recognition (psychology), Artificial intelligence, Geology, Algorithm, Radar, Biology, Cell, Genetics, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4365475042 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2304.05087 |
| ids.doi | https://doi.org/10.48550/arxiv.2304.05087 |
| ids.openalex | https://openalex.org/W4365475042 |
| fwci | |
| type | preprint |
| title | An Efficient Deep Learning Image Condition for Locating Earthquakes |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10271 |
| topics[0].field.id | https://openalex.org/fields/19 |
| topics[0].field.display_name | Earth and Planetary Sciences |
| topics[0].score | 0.9991999864578247 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1908 |
| topics[0].subfield.display_name | Geophysics |
| topics[0].display_name | Seismic Imaging and Inversion Techniques |
| topics[1].id | https://openalex.org/T13018 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9965999722480774 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Seismology and Earthquake Studies |
| topics[2].id | https://openalex.org/T11757 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9958000183105469 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1908 |
| topics[2].subfield.display_name | Geophysics |
| topics[2].display_name | Seismic Waves and Analysis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6037137508392334 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C115961682 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6030933856964111 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[1].display_name | Image (mathematics) |
| concepts[2].id | https://openalex.org/C197424946 |
| concepts[2].level | 3 |
| concepts[2].score | 0.576499342918396 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1165717 |
| concepts[2].display_name | Waveform |
| concepts[3].id | https://openalex.org/C165205528 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5665919184684753 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q83371 |
| concepts[3].display_name | Seismology |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5577521324157715 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C160920958 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4752205014228821 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7662746 |
| concepts[5].display_name | Synthetic data |
| concepts[6].id | https://openalex.org/C2777361361 |
| concepts[6].level | 3 |
| concepts[6].score | 0.47052159905433655 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1112585 |
| concepts[6].display_name | Polarity (international relations) |
| concepts[7].id | https://openalex.org/C118496448 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44935864210128784 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2111620 |
| concepts[7].display_name | Seismic migration |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.44289061427116394 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4308347702026367 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.4299961030483246 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C127313418 |
| concepts[11].level | 0 |
| concepts[11].score | 0.3851483464241028 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[11].display_name | Geology |
| concepts[12].id | https://openalex.org/C11413529 |
| concepts[12].level | 1 |
| concepts[12].score | 0.3437495231628418 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[12].display_name | Algorithm |
| concepts[13].id | https://openalex.org/C554190296 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[13].display_name | Radar |
| concepts[14].id | https://openalex.org/C86803240 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[14].display_name | Biology |
| concepts[15].id | https://openalex.org/C1491633281 |
| concepts[15].level | 2 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7868 |
| concepts[15].display_name | Cell |
| concepts[16].id | https://openalex.org/C54355233 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[16].display_name | Genetics |
| concepts[17].id | https://openalex.org/C76155785 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[17].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6037137508392334 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/image |
| keywords[1].score | 0.6030933856964111 |
| keywords[1].display_name | Image (mathematics) |
| keywords[2].id | https://openalex.org/keywords/waveform |
| keywords[2].score | 0.576499342918396 |
| keywords[2].display_name | Waveform |
| keywords[3].id | https://openalex.org/keywords/seismology |
| keywords[3].score | 0.5665919184684753 |
| keywords[3].display_name | Seismology |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.5577521324157715 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/synthetic-data |
| keywords[5].score | 0.4752205014228821 |
| keywords[5].display_name | Synthetic data |
| keywords[6].id | https://openalex.org/keywords/polarity |
| keywords[6].score | 0.47052159905433655 |
| keywords[6].display_name | Polarity (international relations) |
| keywords[7].id | https://openalex.org/keywords/seismic-migration |
| keywords[7].score | 0.44935864210128784 |
| keywords[7].display_name | Seismic migration |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.44289061427116394 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.4308347702026367 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.4299961030483246 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/geology |
| keywords[11].score | 0.3851483464241028 |
| keywords[11].display_name | Geology |
| keywords[12].id | https://openalex.org/keywords/algorithm |
| keywords[12].score | 0.3437495231628418 |
| keywords[12].display_name | Algorithm |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2304.05087 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2304.05087 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2304.05087 |
| locations[1].id | doi:10.48550/arxiv.2304.05087 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2304.05087 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5089627010 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4765-0823 |
| authorships[0].author.display_name | Wenhuan Kuang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Kuang, Wenhuan |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5100604078 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0530-3954 |
| authorships[1].author.display_name | Jie Zhang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhang, Jie |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5000814233 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0195-2666 |
| authorships[2].author.display_name | Wei Zhang |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Zhang, Wei |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2304.05087 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Efficient Deep Learning Image Condition for Locating Earthquakes |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10271 |
| primary_topic.field.id | https://openalex.org/fields/19 |
| primary_topic.field.display_name | Earth and Planetary Sciences |
| primary_topic.score | 0.9991999864578247 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1908 |
| primary_topic.subfield.display_name | Geophysics |
| primary_topic.display_name | Seismic Imaging and Inversion Techniques |
| related_works | https://openalex.org/W2380219014, https://openalex.org/W1974895211, https://openalex.org/W2176409448, https://openalex.org/W2129841057, https://openalex.org/W3040712279, https://openalex.org/W2364769705, https://openalex.org/W2056136368, https://openalex.org/W2374664672, https://openalex.org/W4367555392, https://openalex.org/W2400337198 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2304.05087 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2304.05087 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2304.05087 |
| primary_location.id | pmh:oai:arXiv.org:2304.05087 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2304.05087 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2304.05087 |
| publication_date | 2023-04-11 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 128 |
| abstract_inverted_index.a | 39, 59, 104, 109 |
| abstract_inverted_index.In | 41 |
| abstract_inverted_index.P- | 81 |
| abstract_inverted_index.an | 20, 31 |
| abstract_inverted_index.by | 44 |
| abstract_inverted_index.in | 144 |
| abstract_inverted_index.is | 37, 179 |
| abstract_inverted_index.it | 90 |
| abstract_inverted_index.of | 15, 47, 53, 76, 95, 124, 162 |
| abstract_inverted_index.on | 130 |
| abstract_inverted_index.to | 11, 19, 66, 118, 150, 181, 185, 195 |
| abstract_inverted_index.we | 57 |
| abstract_inverted_index.The | 70, 199 |
| abstract_inverted_index.and | 82, 89, 112, 122, 191 |
| abstract_inverted_index.are | 116 |
| abstract_inverted_index.can | 137, 157 |
| abstract_inverted_index.due | 10 |
| abstract_inverted_index.few | 188 |
| abstract_inverted_index.for | 79 |
| abstract_inverted_index.has | 192 |
| abstract_inverted_index.may | 4 |
| abstract_inverted_index.the | 6, 12, 48, 74, 92, 99, 120, 125, 135, 140, 145, 155, 160, 167, 177, 193, 204 |
| abstract_inverted_index.yet | 29 |
| abstract_inverted_index.Both | 108 |
| abstract_inverted_index.DLIC | 72, 136, 156, 178, 201 |
| abstract_inverted_index.Deep | 61 |
| abstract_inverted_index.Real | 147 |
| abstract_inverted_index.been | 27 |
| abstract_inverted_index.both | 80 |
| abstract_inverted_index.data | 114, 132, 148 |
| abstract_inverted_index.have | 26 |
| abstract_inverted_index.over | 166 |
| abstract_inverted_index.real | 113 |
| abstract_inverted_index.show | 119, 175 |
| abstract_inverted_index.test | 111, 129 |
| abstract_inverted_index.that | 98, 134, 154, 176 |
| abstract_inverted_index.this | 42, 68 |
| abstract_inverted_index.used | 117, 169 |
| abstract_inverted_index.with | 187 |
| abstract_inverted_index.DLIC. | 127 |
| abstract_inverted_index.Image | 63 |
| abstract_inverted_index.after | 84 |
| abstract_inverted_index.data, | 184 |
| abstract_inverted_index.data. | 146 |
| abstract_inverted_index.image | 36, 165 |
| abstract_inverted_index.issue | 9 |
| abstract_inverted_index.novel | 60 |
| abstract_inverted_index.shall | 202 |
| abstract_inverted_index.shows | 133, 153 |
| abstract_inverted_index.small | 197 |
| abstract_inverted_index.still | 38 |
| abstract_inverted_index.tests | 174 |
| abstract_inverted_index.their | 86 |
| abstract_inverted_index.(DLIC) | 65 |
| abstract_inverted_index.image. | 23 |
| abstract_inverted_index.issue. | 69 |
| abstract_inverted_index.issues | 143 |
| abstract_inverted_index.locate | 196 |
| abstract_inverted_index.merits | 123 |
| abstract_inverted_index.neural | 55 |
| abstract_inverted_index.source | 35, 106, 164, 170, 206 |
| abstract_inverted_index.study, | 43 |
| abstract_inverted_index.taking | 45 |
| abstract_inverted_index.widely | 168 |
| abstract_inverted_index.Further | 173 |
| abstract_inverted_index.S-waves | 83 |
| abstract_inverted_index.Various | 24 |
| abstract_inverted_index.ability | 52 |
| abstract_inverted_index.address | 67 |
| abstract_inverted_index.benefit | 203 |
| abstract_inverted_index.enhance | 159 |
| abstract_inverted_index.focused | 33 |
| abstract_inverted_index.follows | 91 |
| abstract_inverted_index.general | 49 |
| abstract_inverted_index.greatly | 158 |
| abstract_inverted_index.ideally | 32 |
| abstract_inverted_index.imaging | 97 |
| abstract_inverted_index.leading | 18 |
| abstract_inverted_index.methods | 3, 25 |
| abstract_inverted_index.pattern | 50 |
| abstract_inverted_index.propose | 58 |
| abstract_inverted_index.regions | 186 |
| abstract_inverted_index.seismic | 16, 96, 183 |
| abstract_inverted_index.source, | 17 |
| abstract_inverted_index.utterly | 102 |
| abstract_inverted_index.Learning | 62 |
| abstract_inverted_index.focusing | 161 |
| abstract_inverted_index.goodness | 75 |
| abstract_inverted_index.location | 2, 207 |
| abstract_inverted_index.measures | 73 |
| abstract_inverted_index.methods. | 208 |
| abstract_inverted_index.migrated | 163 |
| abstract_inverted_index.moveouts | 88 |
| abstract_inverted_index.network, | 56 |
| abstract_inverted_index.overcome | 139 |
| abstract_inverted_index.polarity | 7, 141 |
| abstract_inverted_index.proposed | 71, 126, 200 |
| abstract_inverted_index.reversal | 8, 142 |
| abstract_inverted_index.scanning | 171 |
| abstract_inverted_index.southern | 151 |
| abstract_inverted_index.waveform | 77 |
| abstract_inverted_index.Condition | 64 |
| abstract_inverted_index.advantage | 46 |
| abstract_inverted_index.encounter | 5 |
| abstract_inverted_index.location. | 107 |
| abstract_inverted_index.migration | 22, 34 |
| abstract_inverted_index.potential | 194 |
| abstract_inverted_index.principle | 94 |
| abstract_inverted_index.producing | 30 |
| abstract_inverted_index.proposed, | 28 |
| abstract_inverted_index.represent | 103 |
| abstract_inverted_index.synthetic | 110, 131 |
| abstract_inverted_index.unfocused | 21 |
| abstract_inverted_index.waveforms | 101 |
| abstract_inverted_index.California | 152 |
| abstract_inverted_index.algorithm. | 172 |
| abstract_inverted_index.alignments | 78 |
| abstract_inverted_index.applicable | 180 |
| abstract_inverted_index.challenge. | 40 |
| abstract_inverted_index.components | 14 |
| abstract_inverted_index.continuous | 182 |
| abstract_inverted_index.correcting | 85 |
| abstract_inverted_index.earthquake | 1 |
| abstract_inverted_index.historical | 189 |
| abstract_inverted_index.traveltime | 87 |
| abstract_inverted_index.application | 115, 149 |
| abstract_inverted_index.best-imaged | 105 |
| abstract_inverted_index.effectively | 138 |
| abstract_inverted_index.geophysical | 93 |
| abstract_inverted_index.recognition | 51 |
| abstract_inverted_index.best-aligned | 100 |
| abstract_inverted_index.earthquakes, | 190 |
| abstract_inverted_index.earthquakes. | 198 |
| abstract_inverted_index.convolutional | 54 |
| abstract_inverted_index.effectiveness | 121 |
| abstract_inverted_index.non-explosive | 13 |
| abstract_inverted_index.Migration-based | 0 |
| abstract_inverted_index.migration-based | 205 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile |