## 4 Trends and Future Work  
The use of advanced preprocessing techniques, such as noise reduction and time-frequency domain transformations [80], enables more robust analysis, especially for large-magnitude events that are underrepresented in databases due to their low-frequency content. Methods like the S-transform and generalized S-transform [81] have been used to enhance seismic data representation. To address data scarcity, GAN-based data augmentation has been proposed [61]. However, further research is required to ensure that synthetic waveforms accurately capture real seismic patterns and do not introduce biases into ML models [34, 56]. Hybrid approaches combining real and synthetic seismic data could further improve dataset balance, reducing bias toward specific magnitude ranges. One of the main challenges in magnitude estimation is ensuring model applicability to different geological regions. While traditional models can be calibrated with minimal local data, ML/DL models require large datasets to prevent overfitting. Transfer learning allows pre-trained models on large seismic databases to be adapted to regions with limited data. For example, CNNs trained on global seismic records have shown significant improvements in magnitude estimation when fine-tuned for specific seismic events. Similarly, Transformer-based and recurrent models have leveraged transfer learning to refine their performance in predicting large magnitudes, where training data are scarce. One promising strategy involves combining DL models with physics-based constraints. Incorporating wave propagation equations and geological characteristics into neural network architectures enhances estimation accuracy, particularly in under-instrumented re gions. Recent studies have shown that integrating physical constraints into ML models reduces dependency on large labeled datasets and improves prediction stability, mitigating errors associated with training data biases [4]. Some ML/DL models exhibit overconfidence in their predictions, which can be problematic for early warning systems. To improve probabilistic prediction calibration, uncertainty quantification techniques, such as Bayesian deep learning, have been proposed [82]. These methods provide confidence intervals for magnitude estimates, increasing their reliability for EEW applications. ML models are highly sensitive to noisy or distorted data, which can impact magnitude estimation accuracy. To address this, strategies such as including noise in training data and using adaptive neural network architectures have been explored. These architectures dynamically adjust the time window size and the number of seismic stations used for prediction. Recent studies have demonstrated that multi-station integration significantly improves magnitude estimation, suggesting that future models should prioritize hybrid approaches that leverage both single- and multi-station inputs dynamically [43; 58]. Additionally, extending these models to practical applications, such as urban personal seismometers, could expand their usability in seismic monitoring [83]. The next crucial step in ML/DL model evolution involves validating them in operational EEW systems. To facilitate widespread adoption, these techniques must be tested in diverse regions and over extended operational periods. Real-time optimization and adaptation to different sensor configurations and data types are critical for achieving a precise and robust seismic monitoring system. Furthermore, implementing these technologies in mobile devices for real-time earthquake alerts presents a significant opportunity to enhance EEW accessibility [84]. Extending real-time EEW applications to large-scale environments, including mobile sensor networks, could benefit both high-risk urban areas and remote locations with limited seismic infrastructure. The integration of ML/DL models into EEW systems presents significant opportunities, but addressing computational, data-related, and interpretability challenges remains crucial for widespread adoption.