# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 5 Conclusions  
In summary, deep learning models demonstrate great potential in the field of earthquake prediction. Continuous optimization of model design, exploration of features and their physical significance, and improvements in data utilization and analytical comprehensiveness will likely lead to more substantial progress in earthquake prediction. Model advantages: Models combining convolutional neural networks (CNNs) and bidirectional long- and short-term memory networks (Bi-LSTMs) show significant advantages in magnitude prediction. Their strong feature learning capabilities and efficient temporal information capture enable them to more accurately reflect seismic wave characteristics and energy release patterns. Role of S-wave information: the addition of S-wave information further boosts the overall predictive power of the models, allowing for a more comprehensive grasp of various features throughout the earthquake process. Research contributions: This study shows that even when only waveforms are fed into the DCRNNAmp model without additional feature information, the model can effectively learn important information related to magnitude. Such end-to-end deep learning models exhibit significant accuracy and simplicity in magnitude prediction, significantly improving the judgment of ground motion damage in early earthquake warning (EEW). Real-time data streams: Since earthquake early warning systems usually need to deal with real-time data streams, RNNs and their variants are well suited for such application scenarios as they can continuously receive new data and update their internal state, thus enabling an immediate response. While many scholars in the field have already endorsed the existing MagNet method, we would like to start from this aspect by fine-tuning and modifying the technique to produce more objective fine-tuning suggestions for this type of model without wishing to undermine the general applicability or generalization of the existing earthquake magnitude prediction models by pushing back and reconstructing them. The authors' work discussing these three model architectures in earthquake magnitude prediction can be a reference for future generations to carry out related work. Despite the challenges in achieving precise magnitude prediction, our research provides valuable insights and technical support for future earthquake prediction efforts, pointing out directions for further optimization and potential research paths. Conceptualization, X.S. ; Methodology, X.S. and B.H. ; Software, B.H. ; Validation, X.S. and B.H. ; Formal analysis, X.S. ; Writing--original draft, X.S. ; Writing--review & editing, B.H., J.L. and S.L. ; Supervision, J.L. and S.L. ; Project administration, J.L. and S.L. ; Funding acquisition, J.L. and S.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was financially supported by the Key Science and Technology Project of Ministry of Emergency Management of the People's Republic of China (2024 EMST040409). **Data Availability Statement:** Seismic waveforms were provided by the Japanese National Research Institute for Earth Science and Disaster Resilience (NIED; [[http://www.kyoshin.bosai.go.jp/](http://www.kyoshin.bosai.go.jp/)]([http://www.kyoshin.bosai.go.jp/](http://www.kyoshin.bosai.go.jp/))), Tsukuba, Japan. The figures were made using Genetic Mapping Tools. The magnitude estimation model's codes were written with Python packages TensorFlow (www.tensorflow.org) and Sklearn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))). **Acknowledgments:** I would like to extend my heartfelt gratitude to [PERSON] for generously sharing the code and inspiring research ideas. My sincere appreciation also goes to [PERSON] for his unwavering support on the project and insightful suggestions for paper revisions. Additionally, I am thankful to [PERSON] for his thoughtful reviews and constructive feedback.