Abstract
Daily total electron content (TEC) images created by splitting TEC maps for three time periods from September 1 to 24, 1999; from February 1 to 24, 2003; and from May 1 to 24, 2003 (Taiwan Standard Time [TST]) as training images (inputs) were used to create two convolutional neural network (CNN) models. However, splitting the TEC maps of the three time periods into daily TEC images caused wedge effects. The wedge effects were reduced using a low-pass filter called the Butterworth filter. This resulted in clearer TEC precursors for earthquakes, facilitating the identification of earthquakes of magnitude M w ≥ 5.0 that exhibited associated TEC precursors during three periods, particularly for the Chi-Chi earthquake of September21, 1999. The results of this study were compared with those of Lin et al. and Lin associated with the Chi-Chi earthquake. Simultaneously, two CNN models that were developed were verified to be rational due to the high accuracy of their predictions. These two models were used to verify each other's accuracies and to demonstrate the reliability of the method in this study. Therefore, statistical analysis was not the aim. The final outputs of the two CNN model were defined as similarities. Similarities, which are larger than 0.5, were defined as TEC precursors of earthquakes. TEC precursors described as temporal TEC multi-precursors (TTMPs) by Zoran et al. were detectable on the 1st, 3rd, and 4th days (that is, on September 17, 18, and 20, 1999, respectively) prior to the Chi-Chi earthquake of September 21, 1999. These results are consistent with those of Liu et al. and Lin. A TEC precursor on May 13, 2003, (TST) was detectable 2 days prior to the earthquake on May 15, 2003, (TST) with the magnitude (M w ) of 5.52. The low standard deviation (STD) and mean square error (MSE) confirm the reliability of both CNN models. Regarding mechanical principles, the TTMPs related to the Chi-Chi earthquake were caused by an electric field. The cause of the TEC precursor on May 13, 2003, prior to the earthquake on May 15, 2003, was an argument without any corresponding study for comparison.
Highlights
Many studies have researched the total electron content (TEC) anomalies associated with large earthquakes [64], [44], [61], [25], [39], [57]
This study demonstrates that the creation of a convolutional neural network (CNN) model for the identification of TEC precursors is uncomplicated without numerous TEC maps as training images
Table 1 provides the results of the inside tests for the time period in Fig. 1 using the first CNN model to verify the accuracy and reliability of this model’s prediction of TEC precursors for earthquakes with the magnitude (Mw) ≥ 5.0
Summary
Many studies have researched the total electron content (TEC) anomalies associated with large earthquakes [64], [44], [61], [25], [39], [57]. Wang et al [64] predicted seismoionospheric TEC disturbances before an earthquake. Liu et al [44] detected TEC precursors from 1 day before the Sumatra Indonesia Mw 7.2 earthquake that occurred on July 5, 2005. Tao et al [61] identified a TEC anomaly from 2 days before the Mw 7.7 earthquake south of Java on July 17, 2006. Ho et al [25] described seismoionospheric TEC anomalies preceding 49 earthquakes with Mw ≥ 6.5 in 2010 in Chile
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