# Time Series and Non-Time Series Models of Earthquake Prediction Based on AETA Data: 16-Week Real Case Study

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. AETA System

#### 2.1. AETA Devices and Data Acquisition

#### 2.2. Data Set Construction

## 3. Model Construction

#### 3.1. Non-Time Series Prediction Model

#### 3.1.1. LightGBM

#### 3.1.2. NN

#### 3.1.3. Other Models

#### 3.2. Time-Series Prediction Models

#### 3.2.1. LSTM

_{S}is the weight parameter, b is bias parameter, $\sigma \left(z\right)$ is an activation function, ${C}_{t}$ refers to the previously stored information, ${h}_{t-1}$ is the output at the previous moment, and ${h}_{t}$ is the output.

#### 3.2.2. GRU

_{S}is the weight parameter, $\sigma \left(z\right)$ is an activation function, ${h}_{t-1}$ is the output at the previous moment, and ${h}_{t}$ is the output.

#### 3.2.3. CNN+GRU

#### 3.3. Model Parameters

#### 3.4. Softmax-AUC Index Weighting Method

#### 3.5. Model Evaluation Indicators

## 4. Results

#### 4.1. Prediction Results of Non-Time Series Models

#### 4.2. Prediction Results of the Time Series Models

## 5. Discussion

#### 5.1. Comparison of Non-Time Series Models and Time Series Models

#### 5.2. Real-Earthquake Prediction

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Type | Feature | Meaning | Number of EM Feature | Number of GA Feature |
---|---|---|---|---|

Time domain features | abs_mean | Mean of absolute value | 2 | 2 |

var | Variance | 2 | 1 | |

power | Power | 2 | 1 | |

skew | Skewness | 2 | 1 | |

kurt | Kurtosis | 2 | 1 | |

abs_max | Maximum absolute value | 2 | 1 | |

abs_top_x | Absolute maximum x% of position | 4 | 2 | |

energy_sstd | standard deviation of short-time energy | 2 | 1 | |

energy_smax | Short-time maximum energy | 2 | 1 | |

s_zero_rate | Short-time average over-zero rate | 0 | 1 | |

s_zero_rate_max | Short-time maximum over-zero rate | 0 | 1 | |

Frequency domain features | power_rate_atob | Power from a~bHz in the frequency spectrum | 11 | 11 |

frequency_center | Center of gravity frequency | 1 | 1 | |

mean_square_frequency | Mean square frequency | 1 | 1 | |

variance_frequency | Frequency variance | 1 | 1 | |

frequency_entropy | Entropy of the spectrum | 1 | 1 | |

Wavelet transforms | levelx_absmean | Mean value after the reconstruction of layer x | 4 | 4 |

levelx_energy | Energy after the reconstruction of layer x | 4 | 4 | |

levelx_energy_svar | Variance of the energy value after the reconstruction of layer x | 4 | 4 | |

levelx_energy_smax | Maximum value of energy after the reconstruction of layer x | 4 | 4 | |

Total | 51 | 44 |

## Appendix B

No. | Station | AUC | PA | PP | RP | PN | RN |
---|---|---|---|---|---|---|---|

1 | DJY | 0.68 | 0.68 | 0.64 | 0.81 | 0.74 | 0.55 |

2 | SMSD | 0.68 | 0.68 | 0.60 | 1.00 | 1.00 | 0.37 |

3 | QC | 0.69 | 0.69 | 0.69 | 0.71 | 0.69 | 0.67 |

4 | WC | 0.67 | 0.67 | 0.73 | 0.55 | 0.62 | 0.79 |

5 | BX | 0.78 | 0.78 | 0.83 | 0.71 | 0.74 | 0.85 |

6 | GZYJ | 0.80 | 0.80 | 0.87 | 0.71 | 0.75 | 0.89 |

7 | EB | 0.66 | 0.66 | 0.60 | 1.00 | 1.00 | 0.33 |

8 | GYCT | 0.68 | 0.69 | 0.66 | 0.84 | 0.75 | 0.53 |

9 | JC | 0.82 | 0.82 | 0.76 | 0.96 | 0.94 | 0.69 |

10 | DF | 0.76 | 0.76 | 0.69 | 1.00 | 1.00 | 0.52 |

11 | QCYD | 0.76 | 0.77 | 0.96 | 0.54 | 0.70 | 0.98 |

12 | QCPS | 0.78 | 0.78 | 0.87 | 0.67 | 0.72 | 0.90 |

13 | CZ | 0.81 | 0.81 | 0.73 | 1.00 | 1.00 | 0.62 |

14 | PWHY | 0.78 | 0.77 | 0.94 | 0.60 | 0.68 | 0.95 |

15 | SPMJ | 0.68 | 0.68 | 0.72 | 0.59 | 0.66 | 0.77 |

16 | PWBM | 0.74 | 0.75 | 0.68 | 1.00 | 1.00 | 0.49 |

17 | JCAN | 0.66 | 0.66 | 0.96 | 0.33 | 0.60 | 0.99 |

18 | YAYJ | 0.69 | 0.69 | 0.70 | 0.67 | 0.68 | 0.71 |

19 | HS | 0.77 | 0.77 | 0.69 | 1.00 | 1.00 | 0.55 |

20 | MXDX | 0.69 | 0.70 | 0.89 | 0.44 | 0.63 | 0.95 |

21 | JZG4 | 0.71 | 0.71 | 0.72 | 0.67 | 0.70 | 0.75 |

22 | JZG5 | 0.73 | 0.73 | 0.71 | 0.80 | 0.75 | 0.65 |

23 | JZG2 | 0.70 | 0.70 | 0.65 | 0.82 | 0.77 | 0.57 |

24 | PWNB | 0.69 | 0.68 | 0.65 | 0.75 | 0.72 | 0.62 |

25 | JZG1 | 0.68 | 0.68 | 0.68 | 0.71 | 0.68 | 0.66 |

26 | WXZZ | 0.80 | 0.80 | 0.72 | 1.00 | 1.00 | 0.60 |

27 | HYA | 0.67 | 0.65 | 0.58 | 1.00 | 1.00 | 0.34 |

28 | DL | 0.80 | 0.80 | 0.79 | 0.76 | 0.81 | 0.83 |

29 | BK | 0.68 | 0.68 | 0.70 | 0.64 | 0.66 | 0.72 |

30 | HBY | 0.72 | 0.72 | 0.75 | 0.65 | 0.70 | 0.79 |

31 | REG | 0.88 | 0.88 | 0.83 | 0.96 | 0.95 | 0.79 |

32 | EMHW | 0.95 | 0.95 | 0.91 | 1.00 | 1.00 | 0.90 |

33 | JYZJJ | 0.93 | 0.93 | 0.87 | 1.00 | 1.00 | 0.86 |

34 | LSSW | 0.69 | 0.70 | 0.82 | 0.47 | 0.65 | 0.91 |

35 | RXCS | 0.96 | 0.96 | 0.93 | 1.00 | 1.00 | 0.93 |

36 | ZGDA | 0.77 | 0.77 | 0.68 | 1.00 | 1.00 | 0.55 |

37 | MYBC | 0.86 | 0.86 | 0.77 | 1.00 | 1.00 | 0.72 |

No. | Station | AUC | PA | PP | RP | PN | RN |
---|---|---|---|---|---|---|---|

1 | MB | 0.65 | 0.65 | 0.59 | 0.73 | 0.72 | 0.58 |

2 | LB | 0.76 | 0.76 | 0.77 | 0.79 | 0.75 | 0.72 |

3 | ML | 0.73 | 0.75 | 0.81 | 0.57 | 0.72 | 0.89 |

4 | EMS | 0.70 | 0.68 | 0.59 | 0.89 | 0.85 | 0.50 |

5 | XJX | 0.66 | 0.66 | 0.60 | 0.68 | 0.72 | 0.64 |

6 | DF | 0.65 | 0.65 | 0.68 | 0.66 | 0.62 | 0.65 |

7 | XCXM | 0.65 | 0.65 | 0.70 | 0.59 | 0.61 | 0.71 |

8 | LDDZ | 0.65 | 0.66 | 0.69 | 0.54 | 0.64 | 0.77 |

9 | YAYJ | 0.66 | 0.68 | 0.62 | 1.00 | 1.00 | 0.32 |

10 | LSBS | 0.69 | 0.69 | 0.78 | 0.52 | 0.64 | 0.85 |

11 | HYA | 0.85 | 0.84 | 0.75 | 1.00 | 1.00 | 0.70 |

12 | MSQS | 0.67 | 0.70 | 0.68 | 0.52 | 0.71 | 0.83 |

13 | EMGQ | 0.68 | 0.73 | 0.95 | 0.37 | 0.69 | 0.99 |

14 | MBMZ | 0.68 | 0.63 | 0.92 | 0.41 | 0.53 | 0.95 |

15 | MBRD | 0.75 | 0.76 | 0.77 | 0.80 | 0.75 | 0.70 |

16 | MBYJ | 0.69 | 0.69 | 0.65 | 0.63 | 0.73 | 0.74 |

17 | WTQ | 0.77 | 0.77 | 0.69 | 0.79 | 0.83 | 0.74 |

18 | NJWYYLZ | 0.66 | 0.66 | 0.61 | 0.71 | 0.71 | 0.60 |

19 | YBYX | 0.98 | 0.98 | 0.95 | 1.00 | 1.00 | 0.95 |

20 | LSFZJZ | 0.75 | 0.76 | 0.76 | 0.65 | 0.75 | 0.84 |

21 | ZGDA | 0.75 | 0.75 | 0.69 | 0.76 | 0.80 | 0.74 |

No. | Station | AUC | PA | PP | RP | PN | RN |
---|---|---|---|---|---|---|---|

1 | TH | 0.69 | 0.69 | 0.62 | 1.00 | 1.00 | 0.38 |

2 | CX | 0.80 | 0.80 | 0.71 | 1.00 | 1.00 | 0.61 |

3 | QJ | 0.68 | 0.69 | 0.68 | 0.73 | 0.69 | 0.64 |

4 | LJSD | 0.78 | 0.78 | 0.78 | 0.79 | 0.78 | 0.77 |

5 | SPI | 0.85 | 0.86 | 0.78 | 1.00 | 1.00 | 0.71 |

6 | DHZ | 0.68 | 0.67 | 0.62 | 0.87 | 0.79 | 0.49 |

7 | DR | 0.73 | 0.73 | 0.87 | 0.53 | 0.66 | 0.92 |

8 | DLSL | 0.77 | 0.74 | 0.63 | 0.96 | 0.95 | 0.58 |

9 | JN | 0.85 | 0.85 | 0.86 | 0.85 | 0.85 | 0.86 |

10 | YX | 0.68 | 0.68 | 0.90 | 0.40 | 0.62 | 0.95 |

11 | KM | 0.70 | 0.71 | 0.83 | 0.50 | 0.66 | 0.90 |

12 | LJYS | 0.67 | 0.67 | 0.61 | 1.00 | 1.00 | 0.33 |

13 | LJDZ | 0.76 | 0.76 | 0.68 | 1.00 | 1.00 | 0.52 |

14 | JZS | 0.91 | 0.91 | 0.97 | 0.86 | 0.87 | 0.97 |

15 | LJLD | 0.69 | 0.69 | 0.86 | 0.47 | 0.62 | 0.92 |

16 | TC | 0.79 | 0.79 | 0.73 | 0.93 | 0.90 | 0.65 |

17 | DQZ | 0.68 | 0.68 | 0.67 | 0.73 | 0.70 | 0.63 |

18 | JP | 0.87 | 0.87 | 0.79 | 1.00 | 1.00 | 0.73 |

19 | HH | 0.83 | 0.83 | 0.75 | 1.00 | 1.00 | 0.66 |

20 | TCMZ | 0.80 | 0.80 | 0.90 | 0.67 | 0.75 | 0.93 |

21 | LJNL | 0.68 | 0.68 | 0.77 | 0.53 | 0.63 | 0.83 |

22 | YYLG | 0.65 | 0.64 | 0.57 | 1.00 | 1.00 | 0.31 |

23 | XCH | 0.87 | 0.87 | 0.79 | 1.00 | 1.00 | 0.74 |

24 | DLHZ | 0.92 | 0.91 | 0.84 | 1.00 | 1.00 | 0.83 |

25 | XGLL | 0.90 | 0.90 | 0.83 | 1.00 | 1.00 | 0.79 |

## Appendix C

No. | Station | Mag_Mae | Distance_Average (km) |
---|---|---|---|

1 | DJY | 0.26 | 98.83 |

2 | SMWJ | 0.16 | 57.97 |

3 | LXSM | 0.44 | 119.46 |

4 | WC | 0.12 | 96.04 |

5 | GYCT | 0.08 | 102.62 |

6 | SP | 0.32 | 50.16 |

7 | QCYD | 0.27 | 100.69 |

8 | QCPS | 0.08 | 50.48 |

9 | YAYJ | 0.14 | 50.47 |

10 | QCCB | 0.18 | 44.30 |

11 | HS | 0.25 | 47.65 |

12 | JZG2 | 0.15 | 19.53 |

13 | LSBS | 0.15 | 50.04 |

14 | MSQS | 0.40 | 48.30 |

15 | HBY | 0.17 | 82.37 |

16 | REG | 0.38 | 72.72 |

17 | WTQ | 0.26 | 26.32 |

18 | NJWYYLZ | 0.13 | 26.88 |

19 | JYZJJ | 0.09 | 14.30 |

20 | LSSW | 0.13 | 18.91 |

21 | RXCS | 0.27 | 63.67 |

22 | LSFZJZ | 0.10 | 16.96 |

23 | LSJJRMZF | 0.08 | 13.22 |

24 | ZGDA | 0.08 | 21.58 |

25 | MYBC | 0.10 | 95.15 |

26 | SMAS | 0.13 | 95.60 |

No. | Station | Mag_Mae | Distance_Average (km) |
---|---|---|---|

1 | CX | 0.25 | 77.79 |

2 | SMWJ | 0.25 | 80.03 |

3 | QW | 0.40 | 82.78 |

4 | GAX | 0.20 | 93.71 |

5 | YYYT | 0.13 | 97.71 |

6 | EB | 0.57 | 79.23 |

7 | MS | 0.22 | 85.35 |

8 | DF | 0.33 | 75.27 |

9 | YM | 0.13 | 96.95 |

10 | LDDZ | 0.50 | 79.81 |

11 | KM | 0.21 | 60.83 |

12 | CZ | 0.40 | 60.74 |

13 | MNLZ | 0.19 | 94.53 |

14 | HYA | 0.46 | 88.26 |

15 | YSHX | 0.13 | 96.26 |

16 | MBQB | 0.17 | 97.19 |

17 | MBMZ | 0.39 | 57.86 |

18 | MBSK | 0.41 | 37.05 |

19 | GYCT | 0.18 | 80.16 |

20 | SMAS | 0.22 | 91.43 |

21 | MBYJ | 0.15 | 85.54 |

22 | JYZJJ | 0.24 | 73.20 |

23 | RXCS | 0.30 | 58.51 |

24 | LSFZJZ | 0.22 | 83.11 |

25 | LSJJRMZF | 0.22 | 78.03 |

26 | ZGDA | 0.20 | 78.62 |

27 | YBCNQXJ | 0.64 | 59.24 |

28 | YBXWSHC | 0.27 | 96.84 |

No. | Station | Mag_Mae | Distance_Average (km) |
---|---|---|---|

1 | TH | 0.06 | 42.34 |

2 | XC | 0.26 | 101.01 |

3 | DC | 0.15 | 99.96 |

4 | DHZ | 0.23 | 42.08 |

5 | XCXM | 0.68 | 117.21 |

6 | DLSL | 0.27 | 113.03 |

7 | YL | 0.30 | 55.52 |

8 | YM | 0.31 | 44.14 |

9 | HA | 0.06 | 109.35 |

10 | YX | 0.16 | 79.27 |

11 | LJYS | 0.05 | 78.47 |

12 | LJGC | 0.04 | 54.87 |

13 | DQZ | 0.03 | 59.05 |

14 | HH | 0.03 | 14.53 |

15 | LJDD | 0.06 | 42.34 |

16 | TCMZ | 0.26 | 101.01 |

17 | LJHP | 0.15 | 99.96 |

18 | TCRH | 0.23 | 42.08 |

19 | LJNL | 0.68 | 117.21 |

20 | XCH | 0.27 | 113.03 |

## References

- Wang, X.; Yong, S.; Xu, B.; Liang, Y.; Bai, Z.; An, H.; Zhang, X.; Huang, J.; Xie, Z.; Lin, K.; et al. Research and Implementation of Multi-component Seismic Monitoring System AETA. Acta Sci. Nat. Univ. Pekin.
**2018**, 54, 487–494. [Google Scholar] - Varotsos, P.; Alexopoulos, K. Physical properties of the variations of the electric field of the earth preceding earthquakes, I. Tectonophysics
**1984**, 110, 73–98. [Google Scholar] [CrossRef] - Frasersmith, A.C.; Bernardi, A.; McGill, P.R.; Ladd, M.E.; Helliwell, R.A.; Villard, O.G. Low-frequency magnetic-field measurements near the epicenter of the ms-7.1 Loma-Prieta earthquake. Geophys. Res. Lett.
**1990**, 17, 1465–1468. [Google Scholar] [CrossRef] - Huang, Q.; Ikeya, M. Seismic electromagnetic signals (SEMS) explained by a simulation experiment using electromagnetic waves. Phys. Earth Planet. Inter.
**1998**, 109, 107–114. [Google Scholar] [CrossRef] - Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Magnetic field variations associated with SES. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci.
**2001**, 77, 87–92. [Google Scholar] [CrossRef] - Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Electric Fields that “Arrive’’ before the Time Derivative of the Magnetic Field prior to Major Earthquakes. Phys. Rev. Lett.
**2003**, 91, 148501. [Google Scholar] [CrossRef] - Huang, Q. Controlled analogue experiments on propagation of seismic electromagnetic signals. Chin. Sci. Bull.
**2005**, 50, 1956–1961. [Google Scholar] [CrossRef] - Uyeda, S.; Nagao, T.; Kamogawa, M. Short-term earthquake prediction: Current status of seismo-electromagnetics. Tectonophysics
**2009**, 470, 205–213. [Google Scholar] [CrossRef] - Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Identifying long-range correlated signals upon significant periodic data loss. Tectonophysics
**2011**, 503, 189–194. [Google Scholar] [CrossRef] - Potirakis, S.M.; Karadimitrakis, A.; Eftaxias, K. Natural time analysis of critical phenomena: The case of pre-fracture electromagnetic emissions. Chaos
**2013**, 23, 23117. [Google Scholar] [CrossRef] - Han, P.; Hattori, K.; Hirokawa, M.; Zhuang, J.; Chen, C.-H.; Febriani, F.; Yamaguchi, H.; Yoshino, C.; Liu, J.-Y.; Yoshida, S. Statistical analysis of ULF seismomagnetic phenomena at Kakioka, Japan, during 2001–2010. J. Geophys. Res. Space Phys.
**2014**, 119, 4998–5011. [Google Scholar] [CrossRef] - Hayakawa, M.; Schekotov, A.; Potirakis, S.; Eftaxias, K. Criticality features in ULF magnetic fields prior to the 2011 Tohoku earthquake. Jpn. Acad. Ser. B Phys. Biol. Sci.
**2015**, 91, 25–30. [Google Scholar] [CrossRef] [PubMed] - Han, P.; Hattori, K.; Huang, Q.; Hirooka, S.; Yoshino, C. Spatiotemporal characteristics of the geomagnetic diurnal variation anomalies prior to the 2011 Tohoku earthquake (Mw 9.0) and the possible coupling of multiple pre-earthquake phenomena. J. Asian Earth Sci.
**2016**, 129, 13–21. [Google Scholar] [CrossRef] - Sarlis, N.V. Statistical Significance of Earth’s Electric and Magnetic Field Variations Preceding Earthquakes in Greece and Japan Revisited. Entropy
**2018**, 20, 561. [Google Scholar] [CrossRef] - Sarlis, N.V.; Varotos, P.A.; Skordas, E.S.; Uyeda, S.; Zlotnicki, J.; Nagao, T.; Rybin, A.; Lazaridou-Varotsos, M.S.; Papadopoulou, K.A. Seismic electric signals in seismic prone areas. Earthq. Sci.
**2018**, 31, 44–51. [Google Scholar] [CrossRef] - Varotsos, P.A.; Sarlis, N.V.; Skordas, E.S. Order Parameter and Entropy of Seismicity in Natural Time before Major Earthquakes: Recent Results. Geosciences
**2022**, 12, 225. [Google Scholar] [CrossRef] - Zhang, Y.; Guo, H.; Yin, W.; Zhao, Z.; Ran, Q. Detection Method of Earthquake Disaster Image Anomaly Based on SIFT Feature and SVM Classification. J. Seismol. Res.
**2019**, 42, 265–272. [Google Scholar] - Jozinovic, D.; Lomax, A.; Stajduhar, I.; Michelini, A. Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network. Geophys. J. Int.
**2020**, 222, 1379–1389. [Google Scholar] [CrossRef] - Xiong, P.; Long, C.; Zhou, H.Y.; Battiston, R.; Zhang, X.M.; Shen, X.H. Identification of Electromagnetic Pre-Earthquake Perturbations from the DEMETER Data by Machine Learning. Remote Sens.
**2020**, 12, 3643. [Google Scholar] [CrossRef] - Wang, L.; Wu, J.; Zhang, W.; Wang, L.; Cui, W. Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms. Front. Earth Sci.
**2021**, 9, 807317. [Google Scholar] [CrossRef] - Saad, O.M.; Chen, Y.F.; Trugman, D.; Soliman, M.S.; Samy, L.; Savvaidis, A.; Khamis, M.A.; Hafez, A.G.; Fomel, S.; Chen, Y.K. Machine Learning for Fast and Reliable Source-Location Estimation in Earthquake Early Warning. IEEE Geosci. Remote Sens. Lett.
**2022**, 19, 8025705. [Google Scholar] [CrossRef] - Kanarachos, S.; Christopoulos, S.R.G.; Chroneos, A.; Fitzpatrick, M.E. Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform. Expert Syst. Appl.
**2017**, 85, 292–304. [Google Scholar] [CrossRef] - Zhou, Y.; Yue, H.; Kong, Q.; Zhou, S. Hybrid Event Detection and Phase-Picking Algorithm Using Convolutional and Recurrent Neural Networks. Seismol. Res. Lett.
**2019**, 90, 1079–1087. [Google Scholar] [CrossRef] - Titos, M.; Bueno, A.; Garcia, L.; Benitez, M.C.; Ibanez, J. Detection and Classification of Continuous Volcano-Seismic Signals with Recurrent Neural Networks. IEEE Trans. Geosci. Remote Sens.
**2019**, 57, 1936–1948. [Google Scholar] [CrossRef] - Jena, R.; Pradhan, B.; Alamri, A.M. Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India. Appl. Sci.
**2020**, 10, 5355. [Google Scholar] [CrossRef] - Xu, Y.; Lu, X.; Cetiner, B.; Taciroglu, E. Real-time regional seismic damage assessment framework based on long short-term memory neural network. Comput. Aided Civil Infrastruct. Eng.
**2021**, 36, 504–521. [Google Scholar] [CrossRef] - Yan, X.; Shi, Z.M.; Wang, G.; Zhang, H.; Bi, E. Detection of possible hydrological precursor anomalies using long short-term memory: A case study of the 1996 Lijiang earthquake. J. Hydrol.
**2021**, 599, 126369. [Google Scholar] [CrossRef] - Huang, Y.; Han, X.; Zhao, L. Recurrent neural networks for complicated seismic dynamic response prediction of a slope system. Eng. Geol.
**2021**, 289, 106198. [Google Scholar] [CrossRef] - Xue, J.; Huang, Q.; Wu, S.; Nagao, T. LSTM-Autoencoder Network for the Detection of Seismic Electric Signals. IEEE Trans. Geosci. Remote Sens.
**2022**, 60, 5917012. [Google Scholar] [CrossRef] - Yong, S.; Wang, X.; Zhang, X.; Guo, Q.; Wang, J.; Yang, C.; Jiang, B.H. Periodic electromagnetic signals as potential precursor for seismic activity. J. Cent. South Univ.
**2021**, 28, 2463–2471. [Google Scholar] [CrossRef] - Bao, Z.; Zhao, J.; Huang, P.; Yong, S.; Wang, X. Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction. Sensors
**2021**, 21, 4434. [Google Scholar] [CrossRef] [PubMed] - Yong, S.; Wang, X.; Pang, R.; Jin, X.; Zeng, J.; Han, C.; Xu, B.X. Development of Inductive Magnetic Sensor for Multi-component Seismic Monitoring System AETA. Acta Sci. Nat. Univ. Pekin.
**2018**, 54, 495–501. [Google Scholar] - Carmona-Cabezas, R.; Gomez-Gomez, J.; de Rave, E.G.; Jimenez-Hornero, F.J. A sliding window-based algorithm for faster transformation of time series into complex networks. Chaos
**2019**, 29, 103121. [Google Scholar] [CrossRef] [PubMed] - Bao, Z.; Yong, S.; Wang, X.; Yang, C.; Xie, J.; He, C. Seismic Reflection Analysis of AETA Electromagnetic Signals. Appl. Sci.
**2021**, 11, 5869. [Google Scholar] [CrossRef] - Hussein, A.S.; Li, T.R.; Yohannese, C.W.; Bashir, K. A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE. Int. J. Comput. Intell. Syst.
**2019**, 12, 1412–1422. [Google Scholar] [CrossRef] - Liang, W.; Luo, S.; Zhao, G.; Wu, H. Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms. Mathematics
**2020**, 8, 765. [Google Scholar] [CrossRef] - Zhang, D.; Gong, Y. The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure. IEEE Access
**2020**, 8, 220990–221003. [Google Scholar] [CrossRef] - Abdi, H. A neural network primer. J. Biol. Syst.
**1994**, 2, 247–281. [Google Scholar] [CrossRef] - Tsang, I.W.; Kwok, J.T.; Cheung, P.M. Core vector machines: Fast SVM training on very large data sets. J. Mach. Learn. Res.
**2005**, 6, 363–392. [Google Scholar] - Speiser, J.L.; Miller, M.E.; Tooze, J.; Ip, E. A comparison of random forest variable selection methods for classification prediction modeling. Expert Syst. Appl.
**2019**, 134, 93–101. [Google Scholar] [CrossRef] - Zhang, W.; Li, H.; Tang, L.; Gu, X.; Wang, L.; Wang, L. Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks. Acta Geotech.
**2022**, 17, 1367–1382. [Google Scholar] [CrossRef] - Liu, Y.; Yong, S.; He, C.; Wang, X.; Bao, Z.; Xie, J.; Zhang, X. An Earthquake Forecast Model Based on Multi-Station PCA Algorithm. Appl. Sci.
**2022**, 12, 3311. [Google Scholar] [CrossRef] - Christ, M.; Braun, N.; Neuffer, J.; Kempa-Liehr, A.W. Time Series Feature Extraction on basis of Scalable Hypothesis tests (tsfresh-A Python package). Neurocomputing
**2018**, 307, 72–77. [Google Scholar] [CrossRef] - Santos, M.S.; Soares, J.P.; Abreu, P.H.; Araujo, H.; Santos, J. Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches. IEEE Comput. Intell. Mag.
**2018**, 13, 59–76. [Google Scholar] [CrossRef] - Hosmer, D.W.; Lemeshow, S. Applied Logistic Regression; John Wiley & Sons, Ltd.: New York, NY, USA, 2000. [Google Scholar]
- Fawcett, T. An introduction to ROC analysis. Pattern Recogn. Lett.
**2006**, 27, 861–874. [Google Scholar] [CrossRef] - Sarlis, N.V.; Christopoulos, S.R.G. Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses. Comput. Phys. Commun.
**2014**, 185, 1172–1176. [Google Scholar] [CrossRef] [Green Version]

**Figure 9.**(

**a**) Effect of window size for electromagnetic signals on prediction model. (

**b**) Effect of time window size for geoacoustic signals on prediction model.

Model | $\mathrm{AUC}\ge 0.65$ | $\mathrm{RP}\ge 0.70$ | $\mathrm{Distance}\_\mathrm{Average}\le 100\mathrm{km}$ | $\mathrm{Mag}\_\mathrm{Mae}\le 0.25$ |
---|---|---|---|---|

LightGBM | 68 | 43 | 52 | 36 |

NN | 57 | 49 | 47 | 39 |

SVM | 51 | 39 | 43 | 41 |

GBDT | 36 | 42 | 39 | 43 |

RF | 45 | 37 | 41 | 32 |

Model | $\mathbf{AUC}\ge 0.65$ | $\mathbf{RP}\ge 0.70$ | $\mathbf{Distance}\_\mathbf{Average}\le 100\mathbf{km}$ | $\mathbf{Mag}\_\mathbf{Mae}\le 0.25$ |
---|---|---|---|---|

LSTM | 84 | 55 | 64 | 47 |

GRU | 82 | 58 | 61 | 48 |

CNN+GRU | 79 | 49 | 56 | 40 |

Actual Magnitude | Predicted Magnitude | Actual Epicenter | Predicted Epicenter | |
---|---|---|---|---|

1th week (5 April 2021–11 April 2021) | N | N | N | N |

2th week (12 April 2021–18 April 2021) | N | Ms4.0 | N | $(28.38\xb0\mathrm{N},104.76\xb0\mathrm{E})$ |

3th week (19 April 2021–25 April 2021) | N | N | N | N |

4th week (26 April 2021–2 May 2021) | N | N | N | N |

5th week (3 May 2021–9 May 2021) | Ms3.6 | N | $(32.4\xb0\mathrm{N},104.02\xb0\mathrm{E})$ | N |

6th week (10 May 2021–16 May 2021) | Ms4.7 | N | $(24.43\xb0\mathrm{N},99.24\xb0\mathrm{E})$ | N |

7th week (17 May 2021–23 May 2021) | Ms6.4 | Ms3.9 | $(25.67\xb0\mathrm{N},99.87\xb0\mathrm{E})$ | $(28.41\xb0\mathrm{N}$$,104.65\xb0\mathrm{E}$) |

8th week (24 May 2021–30 May 2021) | Ms4.1 | Ms4.5 | $(25.74\xb0\mathrm{N},99.95\xb0\mathrm{E})$ | $(25.59\xb0\mathrm{N}$$,99.95\xb0\mathrm{E}$) |

9th week (31 May 2021–6 June 2021) | N | Ms4.1 | N | $(25.64\xb0\mathrm{N},99.98\xb0\mathrm{E})$ |

10th week (7 June 2021–13 June 2021) | Ms5.1 | N | $(24.34\xb0\mathrm{N},101.91\xb0\mathrm{E})$ | N |

11th week (14 June 2021–20 June 2021) | Ms4.2 | Ms4.2 | $(24.33\xb0\mathrm{N},101.91\xb0\mathrm{E})$ | $(24.53\xb0\mathrm{N}$$,99.41\xb0\mathrm{E}$) |

12th week (21 June 2021–27 June 2021) | Ms3.8 | Ms4.0 | $(32.2\xb0\mathrm{N},104.94\xb0\mathrm{E})$ | $(24.31\xb0\mathrm{N}$$,101.87\xb0\mathrm{E}$) |

13th week (28 June 2021–4 July 2021) | Ms4.6 | Ms3.9 | $(24.31\xb0\mathrm{N},101.89\xb0\mathrm{E})$ | $(32.08\xb0\mathrm{N}$$,104.57\xb0\mathrm{E}$) |

14th week (5 July 2021–11 July 2021) | Ms4.7 | N | $(24.43\xb0\mathrm{N},99.24\xb0\mathrm{E})$ | N |

15th week (12 July 2021–18 July 2021) | Ms4.8 | Ms3.9 | $(32.97\xb0\mathrm{N},103.84\xb0\mathrm{E})$ | $(28.12\xb0\mathrm{N}$$,104.64\xb0\mathrm{E}$) |

16th week (19 July 2021–25 July 2021) | Ms4.1 | Ms4.0 | $(29.28\xb0\mathrm{N},105.44\xb0\mathrm{E})$ | $(28.14\xb0\mathrm{N}$$,104.69\xb0\mathrm{E}$) |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, C.; Li, C.; Yong, S.; Wang, X.; Yang, C.
Time Series and Non-Time Series Models of Earthquake Prediction Based on AETA Data: 16-Week Real Case Study. *Appl. Sci.* **2022**, *12*, 8536.
https://doi.org/10.3390/app12178536

**AMA Style**

Wang C, Li C, Yong S, Wang X, Yang C.
Time Series and Non-Time Series Models of Earthquake Prediction Based on AETA Data: 16-Week Real Case Study. *Applied Sciences*. 2022; 12(17):8536.
https://doi.org/10.3390/app12178536

**Chicago/Turabian Style**

Wang, Chenyang, Chaorun Li, Shanshan Yong, Xin’an Wang, and Chao Yang.
2022. "Time Series and Non-Time Series Models of Earthquake Prediction Based on AETA Data: 16-Week Real Case Study" *Applied Sciences* 12, no. 17: 8536.
https://doi.org/10.3390/app12178536