Gravitational signals may detect earthquakes, predict earthquakes, seismic waves, seismographs | Earthquake: Now the information will be available before the earthquake, and this technology will save thousands of lives

Predicting earthquakes: Researchers have created such computers to detect small gravitational signals, so that the signals can be used to predict large earthquakes instantly. According to the report published in the journal Science, this is a completely new method. University of California seismologist Richard Allen says that if we applied this algorithm, there would be a lot of confidence that major damage could be avoided.

Now a seismometer is used

Scientists typically monitor seismic waves or seismic waves, called seismometers, to detect earthquakes. The amount of advance warning they can give depends on the distance between the earthquake and the seismometer and the speed of seismic waves traveling less than 6 kilometers per second. This works well for small earthquakes, Allen says, but earthquakes larger than 7 tend to be the most difficult to detect.

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Researchers find the way

Recently, researchers involved in the discovery of gravitational waves realized that gravitational signals at the speed of light could also be used to monitor earthquakes. The surprising thing is that seismometers, says physicist Bernard Whiting of the University of Florida Signal, will also be located in.

Machine learning algorithms ready

Andrea Lisiardi, a postdoctoral researcher at the University of Côte d’Azur, and colleagues created a machine-learning algorithm to determine this pattern, and trained the model on hundreds of thousands of simulated earthquakes before testing it on a real-world dataset from Tohoku. According to the researchers, who published in Nature, the model accurately estimated the earthquake’s intensity at about 50 seconds.

Reliable estimates of major earthquakes

This technique can provide more reliable magnitude estimates for large earthquakes, which is important, especially for tsunami forecasts, which often take an additional 10 or 15 minutes to arrive. However, the technology is not yet operational. You do not process the data in real time. The model is set to be published in Japan. The algorithm needs to be trained separately to be used in different areas.

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