Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

Abstract

There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (> Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).

Anomalous Variations in Ionosphere TEC Before the Earthquakes of 2021 in the Different Parts of the Globe

“…In the scientific community, there is no consensus on a scientific mechanism to explain recorded ionospheric fluctuation related to earthquakes. Many researchers have seen changes in ionospheric TEC prior to an earthquake [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. According to Parrot [25], the piezoelectric and turbo-electric effects are more likely to be involved in the propagation of the direct wave caused by the compression of rocks near the epicenter.…”

HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics

“…Specifically, In the UCs for this controlled scenario, there could be two centroids mainly focused on the worker and the Cobot. Thus, ML classifier algorithms were selected taking into account the knowledge about the task and the robustness of these classifiers for real-time and noise environment requirements oriented to a binary case [ 41 , 42 , 43 , 44 , 45 , 46 ]. The scenario’s classes for modelling are the number of centroids (NC =2, C1 and C2).…”

PLSSVM—Parallel Least Squares Support Vector Machine

“…A Support Vector Machine (SVM) is a supervised machine learning method. They are currently used, e.g., for earthquake prediction [1], forecasting the mechanical properties of plastic concrete [2], or lupus nephritis diagnosis [3]. In their basic form, SVMs are used for binary classification tasks.…”