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Exploring the link between microseism and sea ice in Antarctica by using machine learning.

Andrea Cannata1,2, Flavio Cannavò3, Salvatore Moschella4

  • 1Università degli Studi di Catania, Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Sezione di Scienze della Terra, Corso Italia 57, I-95129, Catania, Italy. andrea.cannata@unict.it.

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Antarctic microseism amplitudes decrease with sea ice concentration, especially beyond 1,000 km. Machine learning reconstructs sea ice distribution using seismic data when satellite imagery is unavailable, aiding climate studies.

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Area of Science:

  • Solid Earth geophysics
  • Ocean-ice-seismology
  • Climate science

Background:

  • Microseisms, continuous seismic signals, link ocean wave energy to Earth's solid.
  • Antarctic microseism patterns differ due to sea ice, which blocks oceanic wave excitation during winter.

Purpose of the Study:

  • Quantitatively investigate the relationship between Antarctic coastal microseism and sea ice concentration.
  • Assess the influence of sea ice on microseism amplitudes with increasing distance.
  • Develop a machine learning algorithm for reconstructing sea ice distribution using microseism data.

Main Methods:

  • Analysis of microseism amplitudes recorded along Antarctic coasts.
  • Correlation analysis between seismic data and sea ice concentration.
  • Development and application of a machine learning algorithm for spatio-temporal sea ice reconstruction.

Main Results:

  • Microseism's sensitivity to sea ice concentration decreases significantly with distance from seismic stations, diminishing above 1,000 km.
  • A novel algorithm successfully reconstructs Antarctic sea ice distribution using microseism amplitudes.
  • The developed technique shows potential for reconstructing sea ice in both Arctic and Antarctic regions.

Conclusions:

  • Sea ice significantly modulates microseism amplitudes in Antarctica, with influence decreasing beyond 1,000 km.
  • Machine learning applied to microseism data offers a valuable tool for sea ice monitoring, especially during periods lacking satellite coverage.
  • This seismic-based sea ice reconstruction method has broad applications, particularly in climate change research.