SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision Transformer Article Swipe
Shinya Katoh
,
Yoshihisa Iio
,
Hiromichi Nagao
,
Hiroshi Katao
,
Masayo Sawada
,
Kazuhide Tomisaka
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4291782/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4291782/v1
A seismic arrival time picking model, SegPhase, is introduced to automatically process a large amount of seismic data recorded by large dense seismic networks with different sampling frequencies and numbers of observed components. Three models were created to address different sampling frequencies and the number of observed components in each network. The model structure uses a hierarchical Vision Transformer structure, which has not previously been used in seismic arrival time picking models and shows superior performance compared to conventional models using convolutional layers. The performance of SegPhase models was verified in terms of the relationship between arrival time residuals, output probability values, epicentral distance, signal-to-noise ratio, and magnitude, and compared to the PhaseNet models. The SegPhase models had better picking performance and number of seismic detections. Moreover, when the SegPhase models are applied to continuous waveforms, the relationship between the number of detections, O-C values and hypocenter determination error, and the threshold of output probability values used in the analysis was then investigated. It was found that when the threshold was lowered, more arrival times were used for earthquake detection not only with lower output probability values but also with higher output probability. Therefore, lowering the threshold allows the Phase association to make better use of the arrival times that the model assumes to be highly accurate. Although lowering the threshold value increases the error, its effect does not significantly impact the overall result.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4291782/v1
- https://www.researchsquare.com/article/rs-4291782/latest.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396735113
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396735113Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-4291782/v1Digital Object Identifier
- Title
-
SegPhase: Development of Arrival Time Picking Models for Japan’s Seismic Network Using the Hierarchical Vision TransformerWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-05-08Full publication date if available
- Authors
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Shinya Katoh, Yoshihisa Iio, Hiromichi Nagao, Hiroshi Katao, Masayo Sawada, Kazuhide TomisakaList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-4291782/v1Publisher landing page
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https://www.researchsquare.com/article/rs-4291782/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-4291782/latest.pdfDirect OA link when available
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Transformer, Computer science, Real-time computing, Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.layers. | 83 |
| abstract_inverted_index.models. | 114 |
| abstract_inverted_index.numbers | 30 |
| abstract_inverted_index.overall | 233 |
| abstract_inverted_index.picking | 5, 71, 120 |
| abstract_inverted_index.process | 12 |
| abstract_inverted_index.result. | 234 |
| abstract_inverted_index.seismic | 2, 17, 23, 68, 125 |
| abstract_inverted_index.values, | 102 |
| abstract_inverted_index.Although | 218 |
| abstract_inverted_index.PhaseNet | 113 |
| abstract_inverted_index.SegPhase | 87, 116, 130 |
| abstract_inverted_index.analysis | 160 |
| abstract_inverted_index.compared | 77, 110 |
| abstract_inverted_index.lowered, | 172 |
| abstract_inverted_index.lowering | 195, 219 |
| abstract_inverted_index.network. | 51 |
| abstract_inverted_index.networks | 24 |
| abstract_inverted_index.observed | 32, 47 |
| abstract_inverted_index.recorded | 19 |
| abstract_inverted_index.sampling | 27, 41 |
| abstract_inverted_index.superior | 75 |
| abstract_inverted_index.verified | 90 |
| abstract_inverted_index.Moreover, | 127 |
| abstract_inverted_index.SegPhase, | 7 |
| abstract_inverted_index.accurate. | 217 |
| abstract_inverted_index.detection | 180 |
| abstract_inverted_index.different | 26, 40 |
| abstract_inverted_index.distance, | 104 |
| abstract_inverted_index.increases | 223 |
| abstract_inverted_index.structure | 54 |
| abstract_inverted_index.threshold | 152, 170, 197, 221 |
| abstract_inverted_index.Therefore, | 194 |
| abstract_inverted_index.components | 48 |
| abstract_inverted_index.continuous | 135 |
| abstract_inverted_index.earthquake | 179 |
| abstract_inverted_index.epicentral | 103 |
| abstract_inverted_index.hypocenter | 147 |
| abstract_inverted_index.introduced | 9 |
| abstract_inverted_index.magnitude, | 108 |
| abstract_inverted_index.previously | 64 |
| abstract_inverted_index.residuals, | 99 |
| abstract_inverted_index.structure, | 60 |
| abstract_inverted_index.waveforms, | 136 |
| abstract_inverted_index.Transformer | 59 |
| abstract_inverted_index.association | 201 |
| abstract_inverted_index.components. | 33 |
| abstract_inverted_index.detections, | 143 |
| abstract_inverted_index.detections. | 126 |
| abstract_inverted_index.frequencies | 28, 42 |
| abstract_inverted_index.performance | 76, 85, 121 |
| abstract_inverted_index.probability | 101, 155, 186 |
| abstract_inverted_index.conventional | 79 |
| abstract_inverted_index.hierarchical | 57 |
| abstract_inverted_index.probability. | 193 |
| abstract_inverted_index.relationship | 95, 138 |
| abstract_inverted_index.automatically | 11 |
| abstract_inverted_index.convolutional | 82 |
| abstract_inverted_index.determination | 148 |
| abstract_inverted_index.investigated. | 163 |
| abstract_inverted_index.significantly | 230 |
| abstract_inverted_index.signal-to-noise | 105 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.66625608 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |