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T-wave generation and propagation: a comparison between data and spectral element modeling.

Guillaume Jamet1, Claude Guennou, Laurent Guillon

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Summary

This study simulates earthquake-generated T-waves using SPECFEM2D, modeling source radiation, seismic propagation, and seafloor conversion. Results show good agreement with real-world hydrophone data, validating the code for T-wave analysis.

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Area of Science:

  • Geophysics
  • Acoustics
  • Seismology

Background:

  • T-waves are crucial underwater acoustic signals originating from earthquakes.
  • Accurate modeling of T-wave generation and propagation presents significant challenges.
  • Understanding T-waves aids in seismic hazard assessment and oceanographic studies.

Purpose of the Study:

  • To present the first realistic simulations of earthquake-generated T-waves using the SPECFEM2D code.
  • To incorporate key phenomena including source radiation, seismic propagation, and seismic-to-acoustic conversion.
  • To validate simulation results against observed hydrophone data from the Mid-Atlantic Ridge.

Main Methods:

  • Utilized the spectral element code SPECFEM2D for numerical simulations.
  • Modeled seismic wave propagation in the crust and acoustic wave propagation in the water column.
  • Incorporated non-planar seafloor bathymetry for seismic-to-acoustic conversion.
  • Compared simulated T-wave signals with hydrophone recordings from the Mid-Atlantic Ridge.

Main Results:

  • Simulated T-wave signals demonstrated good agreement with observed data in terms of relative amplitudes, arrival times, and durations.
  • Variations in water sound-speed profiles, sub-seafloor seismic velocities, and source frequencies were tested.
  • Discrepancies in spectrograms and early arrivals were attributed to simplified source signals and environmental models.

Conclusions:

  • The SPECFEM2D code effectively models earthquake-generated T-waves.
  • The study validates the code's capability to handle complex phenomena like seafloor acoustic conversion.
  • Further refinement of source signals and environmental models can improve simulation accuracy.