A Deep Neural Network to identify foreshocks in real time

K. Vikraman

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arXiv · 1611.08655

A Deep Neural Network to identify foreshocks in real time

K. Vikraman

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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.

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K. Vikraman. 2016-11-26. A Deep Neural Network to identify foreshocks in real time. https://arxiv.org/abs/1611.08655

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