Earthquake Prediction: Earthquakes cause massive destruction in the world. It is difficult to estimate it accurately, due to which a lot of loss of life and property has to be suffered. However, now scientists have devised new techniques for less damage caused by earthquakes.
Earthquake Prediction: Researchers have created such computers to detect small gravitational signals, so that the use of signals can be used to predict large earthquakes immediately.
According to the report published in ‘Science’, this is a completely new method. University of California seismologist Richard Allen says that if we apply this algorithm, then there will be so much confidence that major damage can be avoided.
Now seismometer is used
Scientists usually 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 the seismic waves traveling less than 6 kilometers per second. Allen says this works well for small temblors, but earthquakes greater than 7 tend to be the most challenging to detect.
Researchers find 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 present in .
Machine-learning algorithms ready
Cote d’Azur University postdoc Andrea Licciardi and his colleagues have created a machine-learning algorithm to identify that pattern. They trained the model on hundreds of thousands of simulated earthquakes before testing them on real data set from Tohoku . According to the researchers reported in Nature, the model accurately estimated the intensity of the earthquake in about 50 seconds.
Reliable estimates in major earthquakes
This technique can provide more reliable size estimates of large-magnitude earthquakes, which is important, especially for tsunami predictions, which often take an additional 10 or 15 minutes to arrive.
However, the technology is not operational yet. It has not processed data in real time. The model is set to be deployed in Japan. The algorithm needs to be trained separately for use in different areas.