Machine learning implementation for a rapid earthquake early warning system

The occurrence of an earthquake is a random phenomenon, to which a rate can be attached, but the result are catastrophes. To reduce the losses, both social and economic, a comprehensive risk reduction program must be planned and applied. One important tool at the society disposal is an earthquake early warning system (EEWS) that provides information on the earthquake epicenter and other key parameters. Many issues arise in EEWS research, such as rapid estimation and accuracy of the earthquake source and parameters. This study presents a machine learning algorithm for EEWS, the algorithm is: neural network and deep learning. The algorithm works in a timely manner with various accuracy for the earthquake parameters.