A new study published recently in Remote Sensing proposes the implementation of 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.
- While there are significant controversies surrounding the prediction of earthquakes, many scientists and amateurs alike are trying to successfuly predict them. What’s more, our history shows that some of them were quite successfull and that some of them paid great price for their discoveries.
- Among the precursory phenomena associated with earthquake predictions, the ionospheric TEC enhancement preceding earthquake events has attracted significant scientific attention.
A group of scientists reporting in Opan Access science journal Remote Vieving is proposing the implementation of 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.1
Their main goal was to gain new insight into the possibility of predicting earthquake occurrences using TEC value anomalies. To eliminate all non-related geodynamic effects (e.g., solar flares, coronal mass ejections, geomagnetic storms, and X-ray flux events), they excluded all earthquake events that took place during enhanced solar activity (X or M class solar flares and large scale coronal mass ejections).
“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%,” researchers said. “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).”
Observational and modeling results have confirmed the existence and detectability of earthquake and tsunami signatures in the ionosphere caused by both acoustic and gravity waves, disturbing the electron density in the F-region.
Regarding the connection between the F-region and earthquakes—the source of the earthquake generates acoustic and gravity-acoustic waves that propagate laterally and upward, away from the source and through the ionospheric layers.
This means that such hazards can create atmospheric and ionospheric perturbations via direct coupling.
Such signatures have also been confirmed by anomalous increases in the intensity of the electromagnetic signal, received in the VLF/ULF bands, during the period immediately preceding an earthquake, and are caused by ionospheric D-layer electron density disturbances.
However, there are still a fair amount of ambiguities regarding the scientific validations of these discoveries as reliable tools that may be used for detecting precursor natural-hazard-generated ionospheric perturbations.
For example, after the devastating Tōhoku-oki earthquake (Mw 9.1) and tsunami that took place in Japan on March 11, 2011, using Japan’s dense GPS satellite network, Heki reported that a clear precursor was detected in the form of a positive anomaly of ionospheric total electron content (TEC).
This began about 40 min before the earthquake and reached nearly 10% of the background TEC, and lasted until atmospheric waves arrived at the ionosphere.
Kuo et al. suggested that these ionospheric density variations could be caused by Earth surface charges (or currents) produced from electric currents associated with the stressed rock.
However, when Komjathy et al. investigated the global ionospheric TEC perturbations just before and after the event, they concluded that the geomagnetic activity indicated storm conditions that were already apparent from processed GEONET data one day prior to and after the event.
The same ambiguity can be found with ionospheric D-layer electron density disturbances extracted from VLF measurements.
Hayakawa et al. reported on a possible VLF sub-ionospheric precursor for the same earthquake in Japan. They found a remarkable anomaly for the NLK-Chofu great circle path on March 5 and 6 in the form of nighttime average amplitude signal decrees that exceeded −4σ.
The anomaly was found on the same day along other propagation paths (from NLK to both Kasugai and Kochi), although it was less enhanced.
The experimental results of this new study show that using GPS ionospheric TEC enhancement as an earthquake precursor predictor can be potentially useful for large earthquakes, with an accuracy of 83%, and 0.66 TSS and HSS skill scores.
1 Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events – Saed Asaly et al. – Remote Sens. 2022, 14(12), 2822; https://doi.org/10.3390/rs14122822
Featured image credit: Remote Sensing / Authors
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