## 6 Conclusions  
In this study, we explored the possibility of using machine learning for earthquake prediction by applying four traditional machine learning methods and the LSTM neural network to predict the occurrences and maximum magnitudes of earthquakes in the Sichuan-Yunnan region. We calculated and extracted seismicity parameters related to earthquake occurrence from the earthquake catalog as input features. The results showed that the random forest method was the most effective at classifying large earthquake occurrences, and the LSTM method provided a reasonable estimation of earthquake magnitude.  
The findings support the idea that small earthquakes contain information relevant to predicting future large earthquakes and offer a promising way to predict the occurrence of large earthquakes. Additionally, the findings provide useful information on which features that are consistent with physical interpretation are important for earthquake prediction.  
While the limitations of this study should be noted, they also represent the next steps for future work. First, under this framework, earthquake swarms, which are statistically very rare [45], are difficult to predict due to the small differences between their magnitudes. Second, longer-term seismic monitoring is needed for the further application of more complex models (e.g., transformer) to improve the performance of predictions. Third, the spatial locations of earthquakes are not considered in this study but are important for earthquake prediction. New models which can address spatial information (e.g., graph neural networks) may be useful to tackle this problem in the future. Although limitations and difficulties exist, we are trying to explore the nonlinear relations of earthquake prediction, which is one of the most difficult problems in seismology, by applying machine learning  
Figure 16: Feature importance for the retrospective experiment with [37].  
methods. This study presents a potential avenue for improving the accuracy of earthquake prediction in the future.  
Methodology, X.W., Z.Z. and K.J.; Software, X.W., Z.Z., Y.Y. and K.J.; Validation, X.W., Z.Z. and Z.L.; Formal analysis, S.Z., C.J. and K.J.; Resources, K.J.; Data curation, C.J.; Writing--original draft, X.W. and Z.Z.; Writing--review & editing, S.Z. and K.J.; Visualization, X.W. and Z.Z.; Supervision, K.J.; Project administration, K.J.; Funding acquisition, S.Z. and K.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Special Fund of the Institute of Geophysics, China Earthquake Administration (Grant No. DQJB 22Z01-09), the National Natural Science Foundation of China (Grant Nos. 42274068, U2039204), the Key Research and Development Project in Shaanxi Province (Grant No. 2023-YBSF-237), and the Shanghai Sheshan National Geophysical Observatory (Grant No. SSOP202207).  
Not applicable.  
The dataset of this research can be found from the China Earthquake Data Center (CEDC, [[http://data.earthquake.cn/index.html](http://data.earthquake.cn/index.html)]([http://data.earthquake.cn/index.html](http://data.earthquake.cn/index.html)), last accessed on 23 May 2022).  
We benefit from helpful discussions with [PERSON], [PERSON] and [PERSON]. Comments and suggestions from Editor and two anonymous reviewers are appreciated.  
The authors declare no conflict of interest.