## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task
Additionally, experiments show that increasing the P-wave duration used as input enhances prediction accuracy, reinforcing the importance of capturing more waveform information. In [49], the EEWPEnsembleStack model was proposed to estimate magnitude using the first 3 s of the P-wave, similar to previous models that also rely on this initial time window for rapid magnitude estimation. This ensemble model combines ML techniques such as AdaBoost, XGBoost, LightGBM, decision tree (DT), and Lasso regression. Predictions from earlier layers serve as inputs to subsequent layers, culminating in the final magnitude estimates. The model was trained and validated using a 10-fold cross-validation approach to prevent overfitting, and hyperparameters are optimized via grid search. In specific tests, the model correctly predicted a magnitude of 6.3 as 6.0 \(\pm\) 0.5, outperforming traditional methods such as \(\tau_{c}\) and \(P_{d}\), which underestimated magnitude by approximately two units. Additionally, the study highlights the impact of feature ablation analysis, demonstrating that the combination of all extracted features enhances model performance. Notably, \(P_{d}\), \(ID2\), and \(IV2\) were identified as the most significant features for magnitude estimation. Furthermore, the model was tested in various hypocentral scenarios, showing that its accuracy was unaffected by epicentral distance. However, while the ensemble model achieves lower prediction errors compared to conventional methods, its computational complexity and inference speed could be potential challenges for real-time applications in EEW systems. Future work could explore reducing the number of base models while maintaining accuracy. Ref. [46] presented TEAM-LM, a Transformer-based model designed for real-time magnitude and hypocentral location estimation. Unlike previous deep learning models that relied on fixed station sets or time windows, TEAM-LM processes seismic data from a dynamically varying set of stations, leveraging self-attention mechanisms to capture inter-station dependencies. The model integrates a mixture density network to provide probabilistic estimates of magnitude and location. Experiments were conducted with three diverse datasets from Chile, Italy, and Japan, showcasing the model's robustness across different tectonic environments. TEAM-LM demonstrated superior performance compared to classical magnitude estimation approaches and previous deep learning models, achieving root mean square error (RMSE) values of 0.08 in Chile, 0.20 in Italy, and 0.22 in Japan for magnitude estimation. For epicentral location, the model improved over time, reaching an error of 2 km in Chile and Italy after 25 s. However, challenges remain, particularly in the estimation of large magnitudes, where limited training data on major seismic events can lead to underestimation. To address this, transfer learning techniques were explored, incorporating events from different regions into the training process, which improved large-magnitude predictions. Additionally, while location estimates are precise in well-sampled regions, errors increase significantly outside the training distribution, suggesting potential limitations in generalization. Future research could focus on refining the model's ability to generalize beyond the training distribution and incorporating physics-based constraints to enhance reliability in real-world applications. In [51], the EQGraphNet model was presented for magnitude estimation using a single station. The architecture combines deep CNN layers with a graph-based approach, incorporating 11 CNN layers and 10 residual connection graph layers (RCGLs), designed to mitigate over-smoothing issues typical in deep graph neural network (GNN) models and improve temporal feature extraction. EQGraphNet processes seismic signals as temporal graphs, where each time step is represented as a node connected to adjacent time steps through a symmetric graph structure.