MSGID: 1:317/3 6448a8f4   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Creating a tsunami early warning system using artificial intelligence   
    Real-time classification of underwater earthquakes based on acoustic   
   signals enables earlier, more reliable disaster preparation    
      
     Date:   
         April 25, 2023   
     Source:   
         American Institute of Physics   
     Summary:   
         Researchers develop an early warning system that combines acoustic   
         technology with AI to immediately classify earthquakes and determine   
         potential tsunami risk. They propose using underwater microphones,   
         called hydrophones, to measure the acoustic radiation produced   
         by the earthquake, which carries information about the tectonic   
         event and travels significantly faster than tsunami waves. The   
         computational model triangulates the source of the earthquake   
         and AI algorithms classify its slip type and magnitude. It then   
         calculates important properties like effective length and width,   
         uplift speed, and duration, which dictate the size of the tsunami.   
      
      
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   FULL STORY   
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   Tsunamis are incredibly destructive waves that can destroy coastal   
   infrastructure and cause loss of life. Early warnings for such natural   
   disasters are difficult because the risk of a tsunami is highly dependent   
   on the features of the underwater earthquake that triggers it.   
      
   In Physics of Fluids, by AIP Publishing, researchers from the University   
   of California, Los Angeles and Cardiff University in the U.K. developed an   
   early warning system that combines state-of-the-art acoustic technology   
   with artificial intelligence to immediately classify earthquakes and   
   determine potential tsunami risk.   
      
   Underwater earthquakes can trigger tsunamis if a large amount of water is   
   displaced, so determining the type of earthquake is critical to assessing   
   the tsunami risk.   
      
   "Tectonic events with a strong vertical slip element are more likely to   
   raise or lower the water column compared to horizontal slip elements,"   
   said co-author Bernabe Gomez. "Thus, knowing the slip type at the   
   early stages of the assessment can reduce false alarms and enhance the   
   reliability of the warning systems through independent cross-validation."   
   In these cases, time is of the essence, and relying on deep ocean wave   
   buoys to measure water levels often leaves insufficient evacuation   
   time. Instead, the researchers propose measuring the acoustic radiation   
   (sound) produced by the earthquake, which carries information about   
   the tectonic event and travels significantly faster than tsunami   
   waves. Underwater microphones, called hydrophones, record the acoustic   
   waves and monitor tectonic activity in real time.   
      
   "Acoustic radiation travels through the water column much faster than   
   tsunami waves. It carries information about the originating source and its   
   pressure field can be recorded at distant locations, even thousands of   
   kilometers away from the source. The derivation of analytical solutions   
   for the pressure field is a key factor in the real-time analysis,"   
   co-author Usama Kadri said.   
      
   The computational model triangulates the source of the earthquake from the   
   hydrophones and AI algorithms classify its slip type and magnitude. It   
   then calculates important properties like effective length and width,   
   uplift speed, and duration, which dictate the size of the tsunami.   
      
   The authors tested their model with available hydrophone data and found   
   it almost instantaneously and successfully described the earthquake   
   parameters with low computational demand. They are improving the model by   
   factoring in more information to increase the tsunami characterization's   
   accuracy.   
      
   Their work predicting tsunami risk is part of a larger project to enhance   
   hazard warning systems. The tsunami classification is a back-end aspect   
   of a software that can improve the safety of offshore platforms and ships.   
      
       * RELATED_TOPICS   
             o Earth_&_Climate   
                   # Tsunamis # Natural_Disasters # Earthquakes # Water   
             o Computers_&_Math   
                   # Computer_Modeling # Computational_Biology #   
                   Mathematical_Modeling # Software   
       * RELATED_TERMS   
             o Tsunami o Megatsunami o Earthquake o Great_Chilean_Earthquake   
             o Seismic_wave o Earthquake_liquefaction o   
             2004_Indian_Ocean_earthquake o 2005_Kashmir_earthquake   
      
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   Story Source: Materials provided by American_Institute_of_Physics. Note:   
   Content may be edited for style and length.   
      
      
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   Journal Reference:   
      1. Bernabe Gomez and Usama Kadri. Numerical validation of an effective   
         slender fault source solution for past tsunami scenarios. Physics   
         of Fluids, 2023 DOI: 10.1063/5.0144360   
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   Link to news story:   
   https://www.sciencedaily.com/releases/2023/04/230425111152.htm   
      
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