## 3 Results
### Comparison Between Machine Learning Techniques and Traditional Methods  
The estimation of earthquake magnitude has traditionally relied on empirical methods such as \(\tau_{c}\)[9] and \(P_{d}\)[10], which analyze the first few seconds of the P-wave to infer the event's magnitude. These approaches are widely used in operational EEW systems due to their simplicity, real-time applicability, and minimal data requirements for calibration, making them practical for deployment in different tectonic settings [68]. However, they also have limitations, including limited effectiveness for large-magnitude events and sensitivity to noise, which can affect their reliability in complex seismic environments. On the other hand, recent advances in ML and DL have significantly improved the accuracy and reliability of magnitude estimation [4; 7; 33; 61]. ML-based models, including XGBoost, RF, and SVMs, leverage extracted seismic features to enhance predictive performance, while DL architectures such as CNNs, LSTMs, and Transformers process raw \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML/DL Model** & **Advantages** & **Disadvantages** & **Application in Magnitude Estimation** & **Real-Time Feasibility** \\ \hline Bi-LSTM [7] & Captures long-term dependencies in seismic signals, robust to temporal distortions, effective for time-series prediction. & Higher computational cost than simple RNNs, requires large training datasets, prone to vanishing gradients in long sequences. & Used for direct magnitude regression based on waveform sequences, enhances feature extraction in hybrid architectures. & Efficient if optimized with pruning/quantization. \\ \hline TEAM-LM [46] & Captures global dependencies, highly parallelizable, effective in handling large datasets. & High memory consumption, complex architecture, requires extensive pre-training. & Optimized for fast magnitude estimation, potential in regional/global earthquake monitoring. & Requires substantial GPU resources. LoRA fine-tuning improves feasibility. \\ \hline MEANet [32] & Fast feature extraction, robust to noise, minimal manual preprocessing. & Limited to spatial features, lacks the ability to capture temporal dependencies. & Designed for rapid magnitude estimation from the initial P-wave signals, with potential applications in EEW. & Demonstrates high-speed processing, but has potential for EEW applications. \\ \hline SVM-M [40] & Works well with small datasets, interpretable, effective for binary magnitude classification. & Scalability issues with large datasets, less effective in highly non-linear relationships. & Applied in magnitude classification rather than direct regression. & Provides a rapid initial estimate and can guide early response decisions. \\ \hline MagNet [44] & Reduces noise through deep convolutions, and generates probabilistic magnitude estimates. & Requires large labeled datasets, sensitive to data distribution changes. & Used in probabilistic magnitude estimation and uncertainty quantification. & Deployable in real-time but requires fine-tuning for regional variations. \\ \hline MCFrame [52] & Combines CNN feature extraction with RNN for temporal dependencies. & High computational cost, difficult to fine-tune hyperparameters. & Used in event-based magnitude classification and real-time seismic monitoring. & Requires optimizations for real-time use, but has potential for EEW applications. \\ \hline EEWPEnsembleStack [49] & Combines multiple models to reduce bias and variance; improves accuracy. & Computationally expensive; requires careful feature engineering/ hyperparameter tuning. & Reduces uncertainty in magnitude estimation, used in multi-source seismic data fusion. & Requires substantial processing power, limiting real-time deployment. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of machine learning models for magnitude estimation. waveform data without requiring feature engineering.