{"id":4635,"date":"2023-08-24T17:56:14","date_gmt":"2023-08-24T17:56:14","guid":{"rendered":"https:\/\/dailyai.com\/?p=4635"},"modified":"2023-08-24T19:25:08","modified_gmt":"2023-08-24T19:25:08","slug":"researchers-harness-satellite-data-to-create-accurate-tsunami-warning-system","status":"publish","type":"post","link":"https:\/\/dailyai.com\/de\/2023\/08\/researchers-harness-satellite-data-to-create-accurate-tsunami-warning-system\/","title":{"rendered":"Forscher nutzen Satellitendaten f\u00fcr ein pr\u00e4zises Tsunami-Warnsystem"},"content":{"rendered":"<p><b>Die Forscher nutzten KI, um Ver\u00e4nderungen in der Erdatmosph\u00e4re zu erkennen, die entstehen, wenn sich Tsunamiwellen im Meer bilden.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Dadurch k\u00f6nnte sich die Vorwarnzeit f\u00fcr K\u00fcstengemeinden, die von Tsunamis bedroht sind, erheblich verl\u00e4ngern.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Valentino Constantinou von der in Florida ans\u00e4ssigen Terran Orbital Corporation erkl\u00e4rt: \"Es gibt kein globales Netz zur Erkennung von Tsunamiwellen, und die Installation physischer Hardware, wie etwa Bojensysteme, ist teuer. Aber wir wissen, dass sich kleine Satellitenkonstellationen \u00fcberall ausbreiten\".<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Die Geschwindigkeit der von Erdbeben auf See erzeugten Signale wird durch die Dichte der geladenen Teilchen in der Ionosph\u00e4re der Erde beeinflusst, die sich etwa 300 bis 350 Kilometer \u00fcber der Oberfl\u00e4che befindet. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Wenn ein Tsunami Schockwellen erzeugt, die in die Atmosph\u00e4re aufsteigen, ver\u00e4ndern sie die Dichte dieser geladenen Teilchen, was zu leichten, aber nachweisbaren Ver\u00e4nderungen der von Satelliten empfangenen Signale f\u00fchrt.<\/span><\/p>\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.jpl.nasa.gov\/news\/scientists-look-to-skies-to-improve-tsunami-detection\">Fr\u00fchere Forschung<\/a> des Jet Propulsion Laboratory der NASA und der Sapienza-Universit\u00e4t Rom in Italien f\u00fchrte zu einer Berechnungsmethode zur Messung von Schwankungen der Dichte geladener Teilchen bei Tsunamis.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Darauf aufbauend wandelten Constantinou und sein Team die Daten, die urspr\u00fcnglich in eindimensionalen Formaten vorlagen, in zweidimensionale Bilder um. Diese Bilder wurden dann von KI-Modellen auf Anzeichen f\u00fcr Tsunamis analysiert.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Das Team trainierte sein KI-System anhand der Daten von drei spezifischen erdbebenbedingten Tsunamis: dem chilenischen Tsunami 2010, dem japanischen Tsunami 2011 und dem kanadischen Tsunami 2012.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sie testeten die Wirksamkeit des Modells an einem vierten Tsunami, der durch das Illapel-Erdbeben 2015 in Chile ausgel\u00f6st wurde. Die KI wurde daraufhin untersucht, wie gut sie tsunamibedingte Ver\u00e4nderungen vom normalen ionosph\u00e4rischen Hintergrundrauschen unterscheiden konnte.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Um Fehlalarme zu minimieren, filterten die Forscher St\u00f6rungen heraus, die von Bodenstationen bei der Kommunikation mit den dar\u00fcber liegenden Satelliten verursacht werden.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Laut Quentin Brissaud von NORSAR, einer norwegischen Stiftung f\u00fcr seismische Forschung, die nicht an der Studie beteiligt ist, lieferte diese Methode mit einer Erkennungsgenauigkeit von \u00fcber 90% \"ziemlich gute Ergebnisse\". Brissaud wies jedoch darauf hin, dass mehr Daten ben\u00f6tigt w\u00fcrden, um die Zuverl\u00e4ssigkeit dieses Systems f\u00fcr verschiedene Arten von Tsunamis zu ermitteln.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Quentin Brissaud stellt fest, dass \"die Seltenheit gro\u00dfer Tsunamis die Analyse und Vorhersage solcher Ereignisse erschwert\".<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Eine der Herausforderungen bei der Einrichtung eines globalen Tsunami-Warnsystems ist die Notwendigkeit des internationalen Datenaustauschs. Constantinou erkl\u00e4rt: \"Die Daten befinden sich oft im Besitz von Regierungen oder kommerziellen Partnern, die die Satelliten betreiben. Es gibt keine zentrale Stelle, die die Daten f\u00fcr ein globales System sammelt.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Mehr \u00fcber die Studie<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Diese <\/span><a href=\"https:\/\/arxiv.org\/pdf\/2308.04611.pdf\"><span style=\"font-weight: 400;\">Studie<\/span><\/a><span style=\"font-weight: 400;\"> kombiniert Satellitendaten mit maschinellem Lernen (ML), um durch Tsunamis ausgel\u00f6ste Ver\u00e4nderungen in der Erdatmosph\u00e4re zu analysieren.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Auch wenn die Erkennung von Tsunamis in letzter Zeit Fortschritte gemacht hat, sind viele K\u00fcstengebiete noch immer nicht ausreichend von Fr\u00fchwarnsystemen abgedeckt.\u00a0<\/span><\/p>\n<p>Und so funktioniert es:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Forscher haben ein KI-gest\u00fctztes System entwickelt, um fr\u00fche Anzeichen von Tsunamis zu erkennen, indem sie \"Traveling Ionospheric Disturbances\" (TIDs) in der Ionosph\u00e4re der Erde \u00fcberwachen. Diese St\u00f6rungen wirken sich auf den Gesamtelektronengehalt (TEC) aus, der \u00fcber das globale Satellitennavigationssystem (GNSS) ermittelt werden kann.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Bei diesem Ansatz werden Daten von mehreren Satellitensystemen wie GPS, Galileo, GLONASS und BeiDou genutzt. Es bietet eine Echtzeitabdeckung der offenen Ozeane und ist eine wertvolle Erg\u00e4nzung f\u00fcr geografische Gebiete, die f\u00fcr herk\u00f6mmliche, auf Bojen basierende Warnsysteme nicht zug\u00e4nglich sind.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Das KI-Modell verwendet Faltungsneuronale Netze (Convolutional Neural Networks, CNN), die mit historischen Daten der Tsunamis von Maule 2010, Tohoku 2011 und Haida-Gwaii 2012 trainiert wurden. Sp\u00e4ter wurde es mit Daten des Illapel-Tsunamis von 2015 validiert und erreichte eine Genauigkeit von 91,7%.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Das System verwendet eine Falsch-Positiv-Minderungsstrategie (FPM), die die Rate der Fehlalarme erheblich reduziert.<\/span><\/li>\n<\/ul>\n<p>Viele KI-Systeme werden zur Vorhersage von Umweltkatastrophen eingesetzt, darunter <a href=\"https:\/\/dailyai.com\/de\/2023\/08\/googles-ai-flood-detection-system-predicts-floods-four-days-in-advance\/\">Googles Hochwasser-Hub<\/a>die k\u00fcrzlich auf weitere Regionen ausgeweitet wurde.<\/p>","protected":false},"excerpt":{"rendered":"<p>Forscher nutzten KI, um Ver\u00e4nderungen in der Erdatmosph\u00e4re zu erkennen, die entstehen, wenn sich Tsunamiwellen im Meer bilden.  Dadurch k\u00f6nnte die Warnzeit f\u00fcr K\u00fcstengemeinden, die von Tsunamis bedroht sind, erheblich verl\u00e4ngert werden. Valentino Constantinou von der in Florida ans\u00e4ssigen Terran Orbital Corporation erkl\u00e4rt: \"Es gibt kein globales Netz zur Erkennung von Tsunami-Wellen, und die Installation physischer Hardware, wie etwa Bojen-basierte Systeme, ist teuer. Aber wir wissen, dass sich kleine Satellitenkonstellationen \u00fcberall ausbreiten\". Die Geschwindigkeit der von Erdbeben auf See erzeugten Signale wird durch die Dichte der geladenen Teilchen in der Ionosph\u00e4re der Erde beeinflusst, die sich etwa 300 bis 350 Kilometer \u00fcber der Oberfl\u00e4che befindet.<\/p>","protected":false},"author":2,"featured_media":4636,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[337,105],"class_list":["post-4635","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-climate-science","tag-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Researchers harness satellite data to create accurate tsunami warning system | DailyAI<\/title>\n<meta name=\"description\" content=\"Researchers have proven that AI can identify changes in Earth&#039;s atmosphere generated when tsunami waves 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