A Deep Neural Network to identify foreshocks in real time
This provides a highly reliable method for earthquake prediction, specifically for events preceded by foreshocks, which is incremental as it builds on deep learning approaches.
The paper tackles the problem of identifying foreshocks in real time to predict major earthquakes, achieving 99% accuracy in classifying seismic waveforms as foreshocks, mainshocks, or aftershocks.
Foreshock events provide valuable insight to predict imminent major earthquakes. However, it is difficult to identify them in real time. In this paper, I propose an algorithm based on deep learning to instantaneously classify a seismic waveform as a foreshock, mainshock or an aftershock event achieving a high accuracy of 99% in classification. As a result, this is by far the most reliable method to predict major earthquakes that are preceded by foreshocks. In addition, I discuss methods to create an earthquake dataset that is compatible with deep networks.