Using Machine Learning for identifying TEC signatures related to earthquakes and tsunamis: the 2015 Illapel event case study

It is known that natural hazards such as volcanic eruptions, earthquakes, and tsunamis can trigger acoustic and gravity waves (AGWs) that could reach the ionosphere and generate electron density disturbances known as Travelling Ionospheric Disturbances (TIDs). These disturbances can be investigated in terms of variations in the ionospheric total electron content (TEC) measurements, collected by continuously operating ground-based Global Navigation Satellite Systems (GNSS) receivers. The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm is a well-known real-time tool for estimating TEC variations. It is based on single-time differences of geometry-free combinations of GNSS carrier-phase measurements.

Artificial Intelligence (AI), particularly in machine learning, offers computational efficiency and data handling, leading to its exploration in ionospheric studies. In this context, the abundance of data allows the exploration of a VARION-based machine learning classification approach to detect TEC perturbation. For this purpose, we used the VARION-TEC variations from the 2015 Illapel earthquake and tsunami, leveraging the distinct ionospheric response triggered by the event.

We employed machine learning algorithms, specifically Random Forest (RF) and XGBoost (XGB), using the VARION-core observations (i.e., dsTEC/dt) as input features. We formulated a binary classification problem using supervised machine learning algorithms and manually labelled the time frames of TEC perturbations as the target variable. We considered two elevation cut-off time series, namely 15° and 25°, to which we applied the classifier. XGBoost with a 15° elevation cut-off dsTEC/dt time series reached the best performance, achieving an F1 score of 0.77, recall of 0.74, and precision of 0.80 on the test data. More in detail, regarding the testing samples, the model accurately classified 183 out of 247 (74.09%) samples of sTEC variations related to the earthquake and tsunami (True Positives, TP). Moreover, 2975 out of 3021 (98.49%) testing samples were correctly classified as containing no sTEC variations caused by an earthquake (True Negatives, TN). However, 64 out of 247 samples (25.91%) were erroneously classified as not containing sTEC variations related to the event (False Negatives, FN), while 46 out of 3021 (1.51%) were wrongly classified as containing sTEC variations related to the earthquake and tsunami (False Positives, FP).

This model showed a 75-second average deviation in predicting perturbation time frames for testing links, equivalent to 5 steps in the 15-second time series intervals. This highlights the algorithm's potential for early detection of ionospheric perturbations from earthquakes and tsunamis, aiding in early warning purposes.

Finally, the model efficiently detects TIDs within 2-3 minutes, showing an impressive computational efficiency, crucial for effective early warning systems. It relies only on the VARION-generated real-time TEC time series (dsTEC/dt), enabling its application in an operational real-time setting using real-time GNSS data.

In conclusion, this work demonstrates high-probability TEC signature detection by machine learning for earthquakes and tsunamis, which can be used to enhance tsunami early warning systems.