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Improved V-detector algorithm based on bagging for earthquake prediction with faults.

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Title: Improved V-detector algorithm based on bagging for earthquake prediction with faults.
Authors: Peng, Lu1,2 (AUTHOR), Liang, Yiwen1 (AUTHOR) yangxiaoguo003@gmail.com, Yang, He3 (AUTHOR)
Source: Journal of Supercomputing. Nov2024, Vol. 80 Issue 16, p24605-24637. 33p.
Subjects: Bootstrap aggregation (Algorithms), Earthquake magnitude, Fault zones, Machine learning, Research personnel
Abstract: With the highly nonlinear relationship between various seismic feature indicators and earthquakes, the researchers can hardly construct an earthquake model. Meanwhile, the lack of samples for destructive earthquakes also leads to inaccurate medium-to-short-term earthquake magnitude predictions. Therefore, this study proposes a novel model for earthquake prediction, named variant detector bagging algorithm (V-detector-bagging). First, we adopt the geological distribution of earthquakes and faults as a criterion to select the appropriate history catalog data area, and the seismic indicators are calculated through the Gutenberg–Richter laws and Panakkat indicators. Then, we propose the V-detector-bagging algorithm, which combines the V-detector algorithm with the bagging method. The proposed algorithm converts the self-tolerance process into a cycle process, during this process, different selves guide samples to spread widely, generate various detectors covering more wide nonself areas, fill holes that are not covered by detectors in nonself areas, and reduce the false negative rate. Thus, the V-detector-bagging algorithm improves the detection performance of the V-detector. Finally, through experimental validation analysis, the proposed algorithm ranked first in the detection rate on Sichuan and Xinjiang catalog data compared to the popular machine learning methods used in predicting earthquakes and the original V-detector algorithm, with Xinjiang yielding the best results. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 179142445
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  Label: Title
  Group: Ti
  Data: Improved V-detector algorithm based on bagging for earthquake prediction with faults.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Peng%2C+Lu%22">Peng, Lu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Yiwen%22">Liang, Yiwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yangxiaoguo003@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+He%22">Yang, He</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Supercomputing%22">Journal of Supercomputing</searchLink>. Nov2024, Vol. 80 Issue 16, p24605-24637. 33p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Bootstrap+aggregation+%28Algorithms%29%22">Bootstrap aggregation (Algorithms)</searchLink><br /><searchLink fieldCode="DE" term="%22Earthquake+magnitude%22">Earthquake magnitude</searchLink><br /><searchLink fieldCode="DE" term="%22Fault+zones%22">Fault zones</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Research+personnel%22">Research personnel</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the highly nonlinear relationship between various seismic feature indicators and earthquakes, the researchers can hardly construct an earthquake model. Meanwhile, the lack of samples for destructive earthquakes also leads to inaccurate medium-to-short-term earthquake magnitude predictions. Therefore, this study proposes a novel model for earthquake prediction, named variant detector bagging algorithm (V-detector-bagging). First, we adopt the geological distribution of earthquakes and faults as a criterion to select the appropriate history catalog data area, and the seismic indicators are calculated through the Gutenberg–Richter laws and Panakkat indicators. Then, we propose the V-detector-bagging algorithm, which combines the V-detector algorithm with the bagging method. The proposed algorithm converts the self-tolerance process into a cycle process, during this process, different selves guide samples to spread widely, generate various detectors covering more wide nonself areas, fill holes that are not covered by detectors in nonself areas, and reduce the false negative rate. Thus, the V-detector-bagging algorithm improves the detection performance of the V-detector. Finally, through experimental validation analysis, the proposed algorithm ranked first in the detection rate on Sichuan and Xinjiang catalog data compared to the popular machine learning methods used in predicting earthquakes and the original V-detector algorithm, with Xinjiang yielding the best results. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11227-024-06323-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 33
        StartPage: 24605
    Subjects:
      – SubjectFull: Bootstrap aggregation (Algorithms)
        Type: general
      – SubjectFull: Earthquake magnitude
        Type: general
      – SubjectFull: Fault zones
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Research personnel
        Type: general
    Titles:
      – TitleFull: Improved V-detector algorithm based on bagging for earthquake prediction with faults.
        Type: main
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            NameFull: Peng, Lu
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            NameFull: Liang, Yiwen
      – PersonEntity:
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            NameFull: Yang, He
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          Dates:
            – D: 01
              M: 11
              Text: Nov2024
              Type: published
              Y: 2024
          Identifiers:
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              Value: 80
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              Value: 16
          Titles:
            – TitleFull: Journal of Supercomputing
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