A Deep Neural Network to identify foreshocks in real time
Geophysics
2016-11-29 v1 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1611.08655,
title = {A Deep Neural Network to identify foreshocks in real time},
author = {K. Vikraman},
journal= {arXiv preprint arXiv:1611.08655},
year = {2016}
}
Comments
Paper on earthquake prediction based on deep learning approach. 6 figures, two tables and 4 pages in total