A Proposed Earthquake Warning System Based on Ionospheric Anomalies Derived From GNSS Measurements and Artificial Neural Networks

Abstract

The Total Electron Content (TEC) derived from Global Navigation Satellite System (GNSS) data processing has been used as a tool for monitoring earthquakes. The purpose of this study is to bring an alternative approach to the prediction of earthquakes and to determine their magnitudes based on Artificial Neural Networks (ANN) and ionospheric disturbances. For this, the Vertical Total Electron Content (VTEC) data from the National Oceanic and Atmosphere Administration (NOAA) were used to train the ANN. Results show that the ANN process achieved an accuracy of 85.71% in validation assessment to predict Tres Picos Mw=8.2 earthquake from 1:30 UTC to 04:00 UTC, approximately 3 hours before the seismic event. For magnitude classification, the ANN achieved an accuracy of 94.60%. The Matthews Correlation Coefficient (MCC) which takes into account all true/false positives and negatives was also evaluated and showed promising results.