## 5 Discussion
### Real-Earthquake Prediction  
LSTM, which is the model with the optimal effect among these several prediction models, was chosen to predict the earthquake with a magnitude larger than 3.5 in the Sichuan-Yunnan region (\(22.00^{\circ}\) N \(\sim 34.00^{\circ}\) N, \(98.00^{\circ}\) E \(\sim 107.00^{\circ}\) E) from April 2021 to July 2021 for 16 consecutive weeks. Each prediction was made every Sunday and includes Y/N prediction for next 7 days in the target region, if it was a Y prediction, then the location and magnitude would be given as well.  
The whole prediction process is shown in Figure 11. When making a prediction for the next week's earthquake, the features are first input into the model of all stations in each region to get the risk_value for each region. If the risk_value is less than the threshold_value for the region, it means there is no expected earthquake and the prediction is finished. On the contrary, if it is judged to be an earthquake, then the prediction of magnitude and epicenter latitude and longitude will start.  
Figure 10: Overall results of the eight prediction models.  
LSTM is used for real-earthquake prediction for 16 consecutive weeks based on the multi-station decision mechanism method. Most of the large prediction deviation occurred in the first month, as it was difficult to set a reasonable regional risk threshold at the beginning. In order to reduce prediction deviation, this paper made several historical projections of the model, adjusted the regional risk threshold and optimized the model, which finally improved the model prediction results. AETA is also continuously collecting new data, and the model is incrementally learned in batches every two weeks. Then the new collected data is used for sample set construction. Every month, the model is learned in a full batch, and the training and testing sets are regenerated with all the data and the whole model is retrained. Finally, we save the best model with the best effect on the validation set.  
This paper focuses on the earthquakes with the largest magnitude within a week. Table 3 shows the results of the real-earthquake prediction for 16 consecutive weeks; the letter \"N\" means no earthquakes.  
The number of correct predictions is 10, the number of false predictions is 2, and the number of missed predictions is 4. The real-earthquake prediction achieves great results with an accuracy of 0.625, which is the number of correct prediction results divided by the total number of weeks. Some prediction results have a small deviation, such as the predictions in the eighth, eleventh, twelfth, thirteenth and sixteenth weeks. In addition, the model has won the top three in the second AETA earthquake prediction competition, which can also prove the effectiveness of our model. The website of the competition is [[https://competition.aeta.io/](https://competition.aeta.io/)]([https://competition.aeta.io/](https://competition.aeta.io/)), accessed on 1 September 2021.  
However, there are still some actual earthquakes that are not predicted. The predicted results may be inaccurate sometimes when predicting earthquakes near Yunnan. After research and discussion, the reasons can be attributed to the fact that the stations in Sichuan province are installed with high density and concentrated distribution, so that the stations can capture the anomaly signals in time, whereas the stations in Yunnan province are mostly scattered, resulting in the lack of abundant monitoring data and anomaly signals. Therefore, the number of stations installed near Yunnan or the weight of Yunnan in the model needs to be increased in the future.  
Figure 11: Decision process of earthquake-prediction model.