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

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## Abstract

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## 1. Introduction

#### 1.1. Natural Hazard Signatures Associated with Geodynamic Processes

#### 1.2. Machine Learning and Deep Learning

#### 1.3. Ionospheric Total Electron Content (TEC)

## 2. Motivation

## 3. Datasets and Methodology

#### 3.1. Datasets

#### 3.1.1. Earthquakes Dataset

#### 3.1.2. TEC Dataset

#### 3.1.3. Solar Flares, Geomagnetic Storms, and Coronal Mass Ejection (CME) Datasets

#### 3.2. Methodology

#### 3.2.1. TEC Data Rejection

#### 3.2.2. TEC Data Pre-Processing

- 1.
- Epicenter TEC value evaluation:

- 2.
- Epicenter time series generation:

- 3.
- TEC detrending time series:

- 4.
- Ionospheric Quiet days–mean and standard deviation TEC time series estimation:

#### 3.2.3. Bayesian Hyperparameter Optimization for SVM

## 4. Experimental Results

## 5. Analysis and Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

TEC | total electron content |

SVM | support vector machine |

GNSS | global navigation satellite system |

ML | machine learning |

RF | random forest |

DL | deep learning |

ANNs | artificial neural networks |

CNNs | convolutional neural networks |

RNNs | recurrent neural networks |

LSTM | long short-term memory |

EUV | extreme ultra-violet |

InSAR | interferometric synthetic aperture radar |

GOES | Geostationary Operational Environmental Satellites |

## Appendix A

**Figure A1.**An example of a randomly picked quiet day (18 November 2003) from the quiet days corresponding to the earthquake that occurred on 18 November 2015.

**Figure A2.**An example of a randomly picked quiet day (24 May 2015) from the quiet days corresponding to the earthquake that occurred in 24 May 2013.

**Figure A3.**An example of a randomly picked quiet day (11 April 2004) from the quiet days corresponding to the earthquake that occurred on 11 April 2012.

**Figure A4.**An example of a randomly picked quiet day (16 September 2011) from the quiet days corresponding to the earthquake that occurred on 16 September 2015.

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**Figure 1.**A plot of all the earthquake events with magnitudes larger than Mw 6.8 spread on a global map, taken from our earthquakes dataset. The radius and the color of each earthquake are consistent to the magnitude–yellow corresponds to earthquakes in the range of Mw 6.8–7, orange corresponds to earthquakes in the range of Mw 7–7.9, and red for earthquakes ≥Mw 7.9.

**Figure 2.**An example of TEC maps for an earthquake day (

**upper**) and for the same earthquake time in the following year (

**lower**), where the red area is the the earthquake’s area, and the red point is the earthquake’s epicenter.

**Figure 3.**An illustration of the epicenter evaluation process, where the black point is the point to be estimated, and the four red points are the nearest neighbours.

**Figure 4.**An example of the epicenter time series generation process applied with a 9.1 magnitude earthquake that occurred on 3 November 2011, at 05:46 UTC, where (1–4) are the time series’ of the four nearest points to the epicenter on the map, and (5) is the resulting weighted average time series; the red line is the time of the earthquake.

**Figure 5.**An example of the result for the daily detrending process applied to the Tōhoku earthquake (11 March 2011), where (1–4) are the time series’ of the four nearest points around the epicenter location, and (5) is the resulting weighted average time series.

**Figure 6.**An example of the earthquake epicenter time series extraction in different years. The area under the curve marked in red corresponds to a monthly mean SSN number higher than 50 SSN; the area under the curve marked in green corresponds to the monthly mean SSN number lower than 50 SSN. The 9.1 Tōhoku earthquake occurred on 11 March 2011, marked in a red vertical line.

**Figure 7.**An example of a randomly picked normalized quiet day (3 November 2013) from the quiet days corresponding to the 9.1 Tōhoku earthquake that occurred on 3 November 2011. As can be seen, within a two days time window (before the earthquake event), the normalized ionospheric TEC values exceeded four times the standard deviation of the randomly chosen normalized quiet day.

**Figure 8.**The confusion matrix for the SVM model results extracted from the training set (

**left**), and the test set (

**right**).

Precision | Recall | HSS | Accuracy | TSS | |
---|---|---|---|---|---|

Training Set (n = 84) | 0.76 | 0.609 | 0.419 | 0.7095 | 0.419 |

Test Set (n = 22) | 0.85 | 0.8 | 0.657 | 0.8285 | 0.657 |

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**MDPI and ACS Style**

Asaly, S.; Gottlieb, L.-A.; Inbar, N.; Reuveni, Y.
Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events. *Remote Sens.* **2022**, *14*, 2822.
https://doi.org/10.3390/rs14122822

**AMA Style**

Asaly S, Gottlieb L-A, Inbar N, Reuveni Y.
Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events. *Remote Sensing*. 2022; 14(12):2822.
https://doi.org/10.3390/rs14122822

**Chicago/Turabian Style**

Asaly, Saed, Lee-Ad Gottlieb, Nimrod Inbar, and Yuval Reuveni.
2022. "Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events" *Remote Sensing* 14, no. 12: 2822.
https://doi.org/10.3390/rs14122822