# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 4 Discussion  
This study delves into the application of deep learning models for magnitude prediction based on the correlation between the first few seconds of P-wave time series and frequency information with magnitude. We conducted a comparative analysis of four models: MagNet, Exams with multi-layer CNN, DCRNN augmented with CNN layers from MagNet, and DCRNNAmp, which further incorporates global amplitude scaling information. The results show that the modified DCRNN and DCRNNAmp models perform best in real-time magnitude prediction, primarily due to their significant advantage in extracting complex waveform features. Their deeper CNN structures can capture more temporal information and global features.  
Timeliness and accuracy: In terms of timeliness, the DCRNNAmp model is similar to the DCRNN model, but it exhibits higher accuracy. Through comprehensive analysis of the dynamic changes in magnitude prediction and overall error metrics across the four models, we found that the DCRNNAmp model improves prediction errors faster when handling high-magnitude events and gradually enhances its ability to capture earthquake-related features over time, thanks to the inclusion of global scale factors.  
Impact factor analysis: Further analysis of how model parameters change with epicentral distance, depth, and signal-to-noise ratio (SNR) revealed a positive correlation between epicentral distance and magnitude prediction. Stations farther from the epicenter tend to underestimate magnitude more severely, but this bias improves after about 5 s. Overall errors and large-event errors converge as SNR increases. In terms of depth, large-event errors are mainly concentrated within 70 km, particularly around 25 km, possibly due to low-velocity layers or fault planes.  
This study also examined the impact of S-wave phase information on the accuracy of magnitude prediction. After inputting continuous waveforms without labeled S-waves, the model's accuracy in magnitude prediction significantly improved after the arrival of S-waves. This improvement is attributed to the rich spatial and temporal features provided by S-waves during earthquake propagation, allowing the model to better capture  
Figure 10: Variation in error with epicenter distance after the appearance of S-wave seismic phase at 1–20 s (gray is all events; black is the appearance of S-wave seismic phase in the station waveform of the current event, Red symbols are error bars for black seismic events, and blue symbols are error bars for gray seismic events).  
seismic wave characteristics and their relationship with magnitude, thereby enhancing prediction capability.  
Notably, while the models exhibit significant accuracy and simplicity in magnitude prediction, there are certain limitations. First, the design of machine learning models (including architecture and parameters) is experience-based, potentially limiting performance in magnitude estimation. Future research should optimize model design to improve prediction accuracy. Second, as deep learning operates as a black box method, the lack of transparency in internal mechanisms hinders clear understanding of which features are the most important in EEW applications and their physical meanings. Therefore, future studies should focus on revealing these features and their physical explanations. Additionally, the differences in model predictions between large and small earthquakes, as well as the impact of dataset imbalance on training outcomes, need further consideration. To enhance data utilization and prediction accuracy, future research could explore the integrated use of various monitoring devices and more comprehensive analytical methods.