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Seguimiento instantáneo del crecimiento sísmico con señales de elastogravedad
Andrea Licciardi1, Quentin Bletery2, Bertrand Rouet-Leduc3,4
1Université Côte d'Azur, IRD, CNRS, Observatoire de la Côte d'Azur, Géoazur, Sophia Antipolis, France. andrea.licciardi@geoazur.unice.fr.
Nature
|May 11, 2022
Resumen
Las señales rápidas de elastogravedad (PEGS) ofrecen estimaciones de magnitud de terremotos en tiempo real, superando las limitaciones de los sistemas de ondas sísmicas tradicionales. Este avance permite una alerta temprana de tsunami más rápida mediante el análisis de señales que anteriormente se consideraban ruido.
Área de la Ciencia:
- * Sismología y Geofísica
- * Ciencias del terremoto
- * Sistemas de alerta temprana de tsunamis
Sus antecedentes:
- * Los sistemas de alerta temprana estándar basados en ondas sísmicas luchan con la estimación rápida de la magnitud de los terremotos grandes (M > 8).
- * Los métodos geodésicos ofrecen mejoras, pero se enfrentan a incertidumbres y latencia debido a los tiempos de viaje de las ondas sísmicas.
- * Las señales rápidas de elastogravedad (PEGS), que viajan a la velocidad de la luz, presentan una solución potencial para el monitoreo en tiempo real.
Objetivo del estudio:
- * Evaluar la viabilidad operativa del uso de PEGS para la estimación de magnitud de terremotos en tiempo real.
- * Desarrollar y probar un modelo de aprendizaje profundo para el seguimiento instantáneo de la evolución de la ruptura sísmica.
- * Para demostrar el potencial de PEGS en la transformación de la alerta temprana de tsunamis.
Principales métodos:
- * Desarrollo de un algoritmo de aprendizaje profundo entrenado en datos sísmicos sintéticos y reales.
- * Utilización de señales rápidas de elastogravedad (PEGS) registradas por sismómetros de banda ancha en Japón.
- * Análisis de las porciones del sismógrafo típicamente ignoradas como ruido.
Principales resultados:
- * Demostró que las PEGS se pueden utilizar para el seguimiento instantáneo del crecimiento de los terremotos en tiempo real.
- * El modelo de aprendizaje profundo rastreó con éxito las funciones de tiempo de la fuente del terremoto en datos reales.
- * Validación del uso de PEGS para el monitoreo "en tiempo real" de la evolución de la ruptura de un gran terremoto.
Conclusiones:
- * Los PEGS proporcionan un método nuevo y eficaz para la evaluación inmediata de la magnitud de los terremotos.
- * El enfoque de aprendizaje profundo desarrollado aprovecha los datos sísmicos no utilizados anteriormente para obtener información crítica.
- Esta tecnología tiene el potencial de mejorar significativamente las capacidades de alerta temprana de tsunamis.
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