Article by: Maurizio Di Paolo Emilio
Taiwanese startup P-Waver announced during CES a new data analysis system to design an Earthquake Early Warning (EEW) model to give an advanced warning …
P-Waver is a startup from the National Seismic Engineering Research Center in Taiwan National Laboratory. Pei-Yang Lin, founder of P-Waver, and his team during CES announced a new data analysis system to design an earthquake early warning (EEW) model for the Taiwan government to give an advance warning on Formosa Island (Taiwan).
During a Taiwan Tech Arena (TTA) with more than 100 startups, the P-Waver team highlighted how critical data analysis can be in determining system efficiency, along with the latest in electronics. The team service also covers structural safety monitoring systems and earthquake disaster prevention consultants as well as smart security solutions for people in buildings or at home through IoT devices or control systems. A P-waver spokesperson said this lays out their vision to enhance the security of residents and the entire business. EEW technology has a strong impact on people’s lives and the financial losses that earthquakes can cause.
Seismic Science Technology
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The increasing urbanization and especially the heavy dependence on the complex telecommunications and transportation infrastructure has led to a careful study of the earthquake early warning system by sending alerts to the population. The Earthquake Early Warning System (EEW) sends a real-time alert to people before an earthquake arrives. The development of such a system is an essential step in reducing fear of the unknown and the unpredictable nature of earthquakes, while at the same time improving people’s safety.
When an earthquake occurs, seismic waves, including compressive or longitudinal (P), transverse (S), and surface (R and L) waves, are radiated outward from the epicenter. The faster and weaker P-wave travels to nearby sensors, generating alarm signals to carry out protection operations before the slower but stronger S-waves and surface waves arrive.
Operation of the ShakeAlert system used in Taiwan [Source USGS]
Sensors, big data, analytics, portals, and, more generally, all technological tools related to IoT can be suitable solutions in case of catastrophic events like earthquake. The development of artificial intelligence systems and the increasing amount of data available are helping scientists to develop models that can more accurately simulate the movements of the Earth’s crust. Several studies combine artificial intelligence and neural networks to find relationships between large amounts of data to buy time in a seismic event.
Artificial intelligence and big data for earthquake early warning methods
During an earthquake, the health, transportation, security and energy industries are likely to be severely affected. High-speed rail can benefit from the EEW system by starting early stops to protect people.
P-Waver built its EEW system based on seismic data from the Central Meteorological Office in Taiwan and AI technology of more than 250,000 earthquake patterns. The systems can save 5-15 seconds of warning time for the area 30-100 km from the epicenter to take preventive measures. In order to prevent false alarms, P-Waver implemented multiple sensors to capture the primary wave. The team rates the ranking accuracy at 98%. The system can predict p-waves in 1-3 seconds, without false alarm, by setting and calibrating the multiple backup sensor.
P-Waver has further defined its vision for potential collaboration, and one opportunity is to expand innovative EEW technology for smart city development. As P-waver explained, “Their ambition is to approach High Tech customers, and smart city developers who are not only in the United States, such as Intel, Micron, Tesla, Google, Honeywell, among others, but also partners in another region to work on implementing the city business together. . “
With more seismic data in databases and increasing computing power, seismologists are turning to big data and artificial intelligence techniques to understand and improve complex simulation models of seismic activity. Other researchers use machine learning algorithms to examine seismic data to better identify aftershocks and volcanic seismic activities, then monitor tectonic earthquakes that indicate deformation at plate boundaries where earthquakes can occur.
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