## 3 Results
### Early Earthquake Magnitude Estimation as a Classification Task
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Author, Year.** & **Dataset Size** & **Target Features** & **Al Model** & **Performance** & **Performance** \\
**Country, Ref.** & **Country** & **Targed Features** & **Al Model** & **Index Name** & **Value (Unit)** \\ \hline \multirow{8}{*}{2022, [PERSON] et al., 2022, China, [60] & CSMNC & & & Estimation error: & \multicolumn{1}{c}{Estimation error:} & \multicolumn{1}{c}{At2.2, \(M5.3\)} & \multicolumn{1}{c}{and \(M6.3\)} \\  & 7236 waveforms & Magnitude & SVM & \multicolumn{1}{c}{Estimation error:} & \multicolumn{1}{c}{Estimation error:} & \multicolumn{1}{c}{Estimation error:} \\  & 461 earthquakes & China & & & \(M6.6\) and \(M7\) & \multicolumn{1}{c}{\(M6.6\) and \(M7\)} \\  & & & & & Average error & \(\pm 0.3\) units 13 s \\  & & & & & \(\sigma\) & 0.31 \\  & & & & & 0.41 \\ \hline \multirow{8}{*}{2025, [PERSON] et al., 2025, India, [61]} & \multirow{8}{*}{18,994 waveforms} & \multirow{8}{*}{Magnitude} & First 3 s: & First 3 s: \\  & & & MAE & 0.42 & \multicolumn{1}{c}{RMSE} & 0.56 \\  & & & MagPred & First 4 s: & First 4 s: \\  & & & XGBoost-LightGBM & MAE & 0.40 \\  & & & CatBoost-RF & RMSE & 0.54 \\  & & & & First 5 s: & \\  & & & & MAE & 0.39 \\  & & & & RMSE & 0.53 \\ \hline \multirow{8}{*}{2025, [PERSON] et al., 2025, India, [34]} & \multirow{8}{*}{26,279 waveforms} & \multirow{8}{*}{Magnitude} & Magnitude: & Magnitude: & Magnitude: \\  & & & MAE & 0.66 \\  & & & RMSE & 0.85 \\  & & & & \(M_{U}\) & 3.81 \\  & & & & \(M_{L}\) & 0.12 \\  & & & DFTQuake & Training time (s) & 62.22 \\  & & & AM-NN-LightGBM & Parameters & \(\sim\)2.4 M \\  & & & XGBoost-RF & PGA: & PGA: \\  & & & MAE & 0.25 \\  & & & RMSE & 0.32 \\  & & & \(M_{U}\) & 95.19 \\  & & & & \(M_{L}\) & 99.78 \\  & & & Training time (s) & 62.22 \\  & & & Parameters & \(\sim\)2.4 M \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._with a \(10^{-4}\) learning rate. The model achieved an accuracy of 97.90%, outperforming the traditional method based on \(P_{d}\) amplitude (94.54%). Tests were conducted with real earthquakes, such as the Changing (\(M6.0\)) and Tangshan (\(M5.1\)) swarms, with an accuracy of 98.63% in identifying small aftershocks. The model demonstrated strong generalization when tested with the INSTANCE dataset (accuracy of 95.52%) and in real-time at the China Earthquake Networks Center (CENC), monitoring 16 earthquakes. One key advantage of this approach is its ability to operate with minimal waveform information while maintaining high accuracy, making it suitable for early warning applications in regions with sparse seismic networks. However, the binary classification scheme may limit its applicability for detailed magnitude estimation, particularly for borderline cases around the \(M5.0\) threshold. In [57], the EEWMagNet model was introduced for rapid seismic magnitude classification in EEW systems. Its goal is to identify whether an earthquake's magnitude is above or below 4, which is critical for issuing warnings without calculating the exact magnitude. EEWMagNet employs CNN layers, a Dense Block with a bottleneck for feature extraction, and multi-head attention for temporal feature correlation. The epicentral distance is also incorporated to improve the accuracy of the magnitude classification. Models were trained with different P-wave window lengths from CENC data, and results showed that a 7 s window provided the best performance, even compared to longer ones. The dataset includes recordings from 1104 stations, with \(M\geq 4\) events augmented using sliding windows and noise overlay techniques to address the imbalance. Experiments demonstrated that EEWMagNet achieves an accuracy of 90.23%, with a precision of 89.35%, recall of 91.08%, and an F1 score of 90.21%. Incorporating epicentral distance improved classification by 18%, and avoiding normalization improved amplitude retention, increasing accuracy by 8%. The model demonstrates the potential for deployment in early warning systems, offering rapid and accurate magnitude classification with minimal data length requirements.