View in EDS

Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing.

Saved in:
Bibliographic Details
Title: Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing.
Authors: Paitz, Patrick1 (AUTHOR) patrick.paitz@wsl.ch, Lindner, Nadja2 (AUTHOR), Edme, Pascal2 (AUTHOR), Huguenin, Pierre3 (AUTHOR), Hohl, Michael3 (AUTHOR), Sovilla, Betty3 (AUTHOR), Walter, Fabian1 (AUTHOR), Fichtner, Andreas2 (AUTHOR)
Source: Journal of Geophysical Research. Earth Surface. May2023, Vol. 128 Issue 5, p1-18. 18p.
Subject Terms: *Avalanches, *Infrastructure (Economics), Gaussian mixture models, Fiber optic cables, Strain rate, Group velocity, Acoustic wave propagation, Sound waves
Geographic Terms: Switzerland
Abstract: Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements are crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber‐optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber‐optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche‐induced ground deformations using physical properties extracted from the frequency‐wavenumber and frequency‐velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche‐generated seismic near‐field, the seismo‐acoustic far‐field, and the mass movement propagating on top of the fiber. By analyzing the mass‐movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo‐acoustic far‐field can be detected before the avalanche reaches the fiber‐optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements. Plain Language Summary: Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Therefore, it is important to be able to reliably measure and detect these hazardous events. We show a successful example to measure and characterize avalanches recorded with a Distributed Acoustic Sensing device that measures deformation along a fiber optic cable. We apply unsupervised machine learning to our avalanche recordings and are able to identify consistent properties between 12 avalanches. Ultimately, our results indicate that DAS might be a useful tool for detecting hazardous mass movements. Key Points: Distributed Acoustic Sensing measurements near the interface between avalanche and the subsurface reveal flow dynamicsStrain rate measurements of seismo‐acoustic waves are registered up to 30 s before avalanches reach the sensorsInternal group velocities larger than the propagation speed suggest the presence of complex internal structures [ABSTRACT FROM AUTHOR]
Copyright of Journal of Geophysical Research. Earth Surface is the property of Wiley-Blackwell 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.)
Database: GreenFILE
FullText Text:
  Availability: 0
Header DbId: 8gh
DbLabel: GreenFILE
An: 163948966
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Paitz%2C+Patrick%22">Paitz, Patrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> patrick.paitz@wsl.ch</i><br /><searchLink fieldCode="AR" term="%22Lindner%2C+Nadja%22">Lindner, Nadja</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Edme%2C+Pascal%22">Edme, Pascal</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huguenin%2C+Pierre%22">Huguenin, Pierre</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hohl%2C+Michael%22">Hohl, Michael</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sovilla%2C+Betty%22">Sovilla, Betty</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Walter%2C+Fabian%22">Walter, Fabian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fichtner%2C+Andreas%22">Fichtner, Andreas</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Earth+Surface%22">Journal of Geophysical Research. Earth Surface</searchLink>. May2023, Vol. 128 Issue 5, p1-18. 18p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Avalanches%22">Avalanches</searchLink><br />*<searchLink fieldCode="DE" term="%22Infrastructure+%28Economics%29%22">Infrastructure (Economics)</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+mixture+models%22">Gaussian mixture models</searchLink><br /><searchLink fieldCode="DE" term="%22Fiber+optic+cables%22">Fiber optic cables</searchLink><br /><searchLink fieldCode="DE" term="%22Strain+rate%22">Strain rate</searchLink><br /><searchLink fieldCode="DE" term="%22Group+velocity%22">Group velocity</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustic+wave+propagation%22">Acoustic wave propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Sound+waves%22">Sound waves</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Switzerland%22">Switzerland</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements are crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber‐optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber‐optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche‐induced ground deformations using physical properties extracted from the frequency‐wavenumber and frequency‐velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche‐generated seismic near‐field, the seismo‐acoustic far‐field, and the mass movement propagating on top of the fiber. By analyzing the mass‐movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo‐acoustic far‐field can be detected before the avalanche reaches the fiber‐optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements. Plain Language Summary: Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Therefore, it is important to be able to reliably measure and detect these hazardous events. We show a successful example to measure and characterize avalanches recorded with a Distributed Acoustic Sensing device that measures deformation along a fiber optic cable. We apply unsupervised machine learning to our avalanche recordings and are able to identify consistent properties between 12 avalanches. Ultimately, our results indicate that DAS might be a useful tool for detecting hazardous mass movements. Key Points: Distributed Acoustic Sensing measurements near the interface between avalanche and the subsurface reveal flow dynamicsStrain rate measurements of seismo‐acoustic waves are registered up to 30 s before avalanches reach the sensorsInternal group velocities larger than the propagation speed suggest the presence of complex internal structures [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Geophysical Research. Earth Surface is the property of Wiley-Blackwell 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=8gh&AN=163948966
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1029/2022JF007011
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1
    Subjects:
      – SubjectFull: Avalanches
        Type: general
      – SubjectFull: Infrastructure (Economics)
        Type: general
      – SubjectFull: Gaussian mixture models
        Type: general
      – SubjectFull: Fiber optic cables
        Type: general
      – SubjectFull: Strain rate
        Type: general
      – SubjectFull: Group velocity
        Type: general
      – SubjectFull: Acoustic wave propagation
        Type: general
      – SubjectFull: Sound waves
        Type: general
      – SubjectFull: Switzerland
        Type: general
    Titles:
      – TitleFull: Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Paitz, Patrick
      – PersonEntity:
          Name:
            NameFull: Lindner, Nadja
      – PersonEntity:
          Name:
            NameFull: Edme, Pascal
      – PersonEntity:
          Name:
            NameFull: Huguenin, Pierre
      – PersonEntity:
          Name:
            NameFull: Hohl, Michael
      – PersonEntity:
          Name:
            NameFull: Sovilla, Betty
      – PersonEntity:
          Name:
            NameFull: Walter, Fabian
      – PersonEntity:
          Name:
            NameFull: Fichtner, Andreas
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2023
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 21699003
          Numbering:
            – Type: volume
              Value: 128
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Journal of Geophysical Research. Earth Surface
              Type: main
ResultId 1