## 7 Conclusions  
Earthquakes can cause severe damage to people's lives and property. They have strong nonlinear characteristics, and their patterns are difficult to discern, as their occurrence has always been considered a random event. This paper combines deep learning with VMD to predict the monthly maximum magnitudes of earthquakes in the North-South Seismic Belt region based on earthquake catalog data. The results show that using multiple relatively stable mode data obtained through VMD, along with a new dataset composed of seismological parameters, can enhance the prediction performance of deep learning models. It can effectively capture short-term magnitude changes and is sensitive to trends in earthquake magnitudes. With a time window size of 12, the ATT-BiLSTM achieved the best prediction results, showing good performance for earthquakes of magnitude _M_s 5.0-6.0. Although the research results for earthquakes above magnitude _M_s 6 are less satisfactory, with the development of satellite technology, more earthquake-related data are being acquired. Deep learning is advancing rapidly and has shown good performance in data mining, and various signal decomposition methods are becoming more refined. It is believed that integrating these approaches may provide some learning capability for understanding certain patterns of earthquake occurrences.  
The study area of this paper is the entire North-South Seismic Belt, which is too broad. Future research could consider the impact on specific local areas. For example, when predicting future earthquakes in a particular province, it would be beneficial to not only consider the earthquakes occurring in that specific region but also incorporate data from the North-South Seismic Belt and even the entirety of China to study the seismic patterns of that area. Currently, the focus is on predicting the magnitude of earthquakes. However, predicting earthquake occurrence time, epicenter location, and even focal depth is also crucial for a more comprehensive earthquake prediction task. Future work should involve incorporating multiple parameters, considering more spatial relationships, and using more refined models to better understand the patterns of earthquake occurrences.  
Conceptualization, N.M. and K.S.; methodology, N.M.; software, N.M. and J.Z.; validation, N.M. and K.S.; formal analysis, N.M.; investigation, J.Z.; resources, N.M.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, N.M., J.Z. and K.S.; visualization, J.Z.; supervision, K.S.; project administration, K.S.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. U2039202 and the National Key Research and Development Program of China under Grant No. 2023 YFC3007303.  
Not applicable.  
The data used in this paper are earthquake catalog data, which can be downloaded from the China Earthquake Networks Center: [[https://news.ceic.ac.cn/index.html](https://news.ceic.ac.cn/index.html)]([https://news.ceic.ac.cn/index.html](https://news.ceic.ac.cn/index.html)).  
We thank the China Earthquake Networks Center for providing data download access.  
The authors declare no conflicts of interest.