## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task
LoRA is employed during fine-tuning, freezing pre-trained weights, and introducing low-rank decomposition matrices, which minimizes trainable parameters while adapting the model to regional datasets. The training utilizes SpecAugment for data augmentation, the Adam optimizer, and mean squared error or cross-entropy loss, depending on the task. The study used the STEAD and KPED datasets for classification and magnitude estimation. The resulting model outperformed state-of-the-art models such as EQTransformer [62], CRED [17], DetNet [63], Yews [64], and STA/LTA [65] in classification, as well as complex CNN [48], MagNet [44], and a deeper convolutional recurrent neural network (CRNN) [53] in magnitude estimation. Similarly, for magnitude estimation, it surpasses previous models, achieving a 7% reduction in mean absolute error (MAE) on the STEAD dataset and a 48% decrease on the KPED dataset. These improvements suggest that the conformer model, combined with LoRA fine-tuning, is well-suited for both global and regional seismicity studies. However, while LoRA reduces trainable parameters, its effectiveness relies on pre-trained weights from global datasets, which may not always capture local seismic characteristics. Future work could explore hybrid approaches integrating raw waveform processing alongside spectrogram-based feature extraction, as well as multi-station input to further enhance model robustness. Ref. [48] introduced a complex CNN-based model to estimate epicentral distance, depth, and magnitude from raw three-component seismograms without requiring P- and S-wave arrivals. By leveraging STFT representations, the model processes both magnitude and phase information using complex-valued convolutional layers (CReLUs), improving accuracy over real-valued CNNs. Unlike traditional methods that depend on explicit arrival-time picking, this model directly learns seismic attributes from waveform data, enhancing robustness against picking errors. Results show that complex CNN surpasses traditional models, achieving an MAE of 4.51 km for distance, 6.15 km for depth, and 0.26 for magnitude. The use of skip connections and residual complex layers enhances feature extraction and convergence, demonstrating the potential of complex-valued deep learning for single-station seismic analysis. Additionally, the model was tested on multiple datasets, confirming its adaptability to different seismic networks. However, eliminating P- and S-wave arrival constraints may introduce uncertainties in complex waveforms, and the higher computational cost could limit real-time applications. Future work should explore lightweight implementations of complex CNNs to reduce inference time while maintaining accuracy. Ref. [41] introduced a new approach for seismic magnitude estimation using a CNN called EEWNet. The novelty of this method lies in directly processing vertical accelerograms without preprocessing, adapting its architecture based on the P-wave length received as input. EEWNet's architecture includes sequential convolution and pooling layers, with ReLU activations, and a final fully connected layer. Unlike traditional approaches that rely on predefined P-wave parameters, EEWNet takes raw waveform data as input and adapts its architecture accordingly, eliminating the need for hyperparameter tuning. The model predicts the logarithm of the maximum displacement in horizontal components, which is used to calculate magnitude via established relationships. Training employed the Adam optimizer with a learning rate of 0.0001, batch size of 200, and 400 epochs. The dataset consists of 30,756 accelerograms recorded by 688 KiK-net borehole sensors, covering earthquakes with magnitudes from 4 to 9 and hypocentral distances between 25 km and 200 km. EEWNet's performance is compared to classic magnitude estimation approaches, such as \(\tau_{\text{log}}\), \(\tau_{c}\), and \(P_{d}\), demonstrating a lower standard deviation and a higher percentage of predictions with magnitude errors between \(-0.5\) and 0.5, indicating significantly improved accuracy.