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<p>The Earth’s ionosphere is susceptible to disturbances from terrestrial events, including tsunamis, which generate upward-propagating waves. Monitoring these disturbances using signals from global navigation satellites (GNSS) offers a novel way to detect these hazards. The GUARDIAN (GNSS Upper Atmospheric Real-time Disaster Information and Alert Network) System processes real-time satellite data to measure ionospheric changes. We introduce a machine learning-based extension, “Scout”, which implements automated detections for natural hazards. We demonstrate its effectiveness using the July 2025 Mw = 8.8 Kamchatka earthquake and subsequent Pacific-wide tsunami. Crucially, the system detected the ionospheric signature of the incoming tsunami 30&#xa0;min before it reached the coast of Hawai’i (USA). This result highlights the potential for automated, satellite-based ionospheric monitoring to enhance existing early warning systems by providing crucial additional lead time for life-saving actions.</p>

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GNSS-based automated detection of the 2025 Kamchatka tsunami 30 minutes before landfall using GUARDIAN

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Camille Martire,
  • Wenwen Lu,
  • Panagiotis Vergados,
  • Siddharth Krishnamoorthy,
  • Béla Szilágyi,
  • Michele Vallisneri,
  • Larry J. Romans,
  • Attila Komjáthy,
  • Yoaz E. Bar-Sever

摘要

The Earth’s ionosphere is susceptible to disturbances from terrestrial events, including tsunamis, which generate upward-propagating waves. Monitoring these disturbances using signals from global navigation satellites (GNSS) offers a novel way to detect these hazards. The GUARDIAN (GNSS Upper Atmospheric Real-time Disaster Information and Alert Network) System processes real-time satellite data to measure ionospheric changes. We introduce a machine learning-based extension, “Scout”, which implements automated detections for natural hazards. We demonstrate its effectiveness using the July 2025 Mw = 8.8 Kamchatka earthquake and subsequent Pacific-wide tsunami. Crucially, the system detected the ionospheric signature of the incoming tsunami 30 min before it reached the coast of Hawai’i (USA). This result highlights the potential for automated, satellite-based ionospheric monitoring to enhance existing early warning systems by providing crucial additional lead time for life-saving actions.