## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task
By leveraging transfer learning, the model benefits from prior knowledge, reducing the need for extensive labeled training data and accelerating convergence during training. Unlike conventional SVM approaches, TLSVM-M incorporates regional data adaptation to enhance generalization. The model exhibited an average error of \(\pm\)0.3 units for minor to moderate earthquakes (\(M_{s}\) 4.2, 5.2, 6.3) within the first second. For larger magnitudes (\(M_{s}\) 6.6, 7.0), an initial underestimation improved within the first 13 s, achieving an error of \(\pm\)0.3 units. Compared to other methods, such as SVM without transfer learning (error of 0.44), \(\tau_{c}\) (1.35), and \(P_{d}\) (0.44), the model achieved an average error of 0.31 and a standard deviation of 0.41, making it the most precise. Despite these improvements, the reliance on single-station data might introduce uncertainties in complex seismic scenarios, suggesting the potential benefit of multi-station integration. In [4], CREIME, a multitask model for seismic event identification and magnitude estimation based on P-waves, was presented. The architecture integrates three convolutional layers, max-pooling for feature extraction and dimensionality reduction, and two Bi-LSTM layers to capture temporal dependencies in seismic signals. The output layer generates 512 values corresponding to the input samples. The model was trained with a custom loss function combining MSE, MAE, and a magnitude-specific penalty term to address the underestimation of high magnitudes. The primary objective of CREIME is to detect seismic signals, identify P-wave arrival, and estimate earthquake magnitude using only the first 2 s of P-wave data. Compared to conventional models, CREIME avoids explicit feature extraction by leveraging an end-to-end deep learning approach, reducing the need for prior knowledge about waveform characteristics. The model performed well for low and medium magnitudes but underestimated events with magnitudes above 5.5. To address this issue, oversampling and undersampling techniques were applied to balance the magnitude distribution. Another key advantage of CREIME is its robustness against seismic noise, achieving an event detection accuracy of 98% even in low-SNR environments. CREIME outperforms traditional methods like STA/LTA and can be implemented in early warning systems. In [50], an LSTM-based method was proposed to estimate magnitude using only data from a single seismic station, similar to previous approaches that have explored single-station magnitude estimation. The main objective is to enhance EEW systems. This model focuses on earthquakes with magnitudes above \(M6\) due to their more significant destructive potential and follows an end-to-end approach. Designed features are used instead of raw signals to overcome the limitation of large-event data. The dataset includes events with magnitudes ranging from \(M1.8\) to \(M8.3\), with an average distance of 196 km between the epicenter and the nearest station. The model showed a relative error of 4.01% for earthquakes above \(M4\) and 8.04% for smaller events. Results significantly improved when using nearby stations, with a mean absolute percentage error (MAPE) of 6.77% compared to 14.34% for distant stations. This suggests that distance plays a critical role in model accuracy, reinforcing the importance of single-station proximity in early warning systems. Tests included earthquakes greater and smaller than \(M4\) at varying station distances. The model achieved an overall MAE of 0.23, excelling in large events with an MAE of 0.21. The results are competitive with other methods in the literature. However, performance may degrade in regions with sparse seismic networks, where station distance increases uncertainty in magnitude estimation. In [61], MagPred, an ML ensemble model, was introduced for real-time earthquake magnitude estimation. The model addresses data imbalance by incorporating synthetic data generated with the conditional tabular generative adversarial network (CTGAN).