## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task
AIMag was tested on historical earthquakes, such as the 2004 Sumatra (\(M9.1\)) and 2011 Tohoku (\(M9.1\)) events, correctly identifying them as large but underestimating their exact magnitudes. Despite this limitation, AIMag provides rapid magnitude estimates that could improve early warning systems, particularly in tsunami-prone regions. Ref. [58] introduced a DL model for real-time magnitude estimation. The model integrates three heterogeneous modalities, namely, three-component acceleration seismograms, differential P-wave arrivals, and differential sensor locations, using a combination of CNN-LSTM and Transformer architectures to process these data. Waveform inputs are processed using CNN layers to downsample dimensions and LSTM layers to capture temporal dependencies. The Transformer encoder extracts features from differential P-wave arrivals, representing travel time relationships, while the Transformer decoder combines these with differential seismometer locations to infer implicit distance information. A global average pooling (GAP) layer fuses features from multiple seismometers for final magnitude estimation through a fully connected layer. By incorporating location information, the model aims to enhance magnitude estimation accuracy, particularly in early warning scenarios where limited data are available. Data augmentation strategies were implemented to enhance model robustness. Key evaluation metrics included RMSE, MAE, and standard deviation, with values of 0.38, 0.29, and 0.38 at 3 s and 0.20, 0.15, and 0.20 after 14 s of the P-wave arrival. The model demonstrated greater accuracy for earthquakes with magnitudes between 3.0 and 5.0. However, performance deteriorated for larger magnitudes (\(\geq\)7.0) due to data limitations and saturation effects. The model also proved robust to errors of up to 0.2 s in P-wave detection. Ref. [59] proposed an SVM-based method to estimate earthquake magnitude and location using only the vertical component of a single seismic station. The SVM model employs a Gaussian kernel for regression, transforming the input features into a higher-dimensional space to separate non-linear relationships. Bayesian optimization was used to determine optimal hyperparameters, and tenfold cross-validation was applied to avoid overfitting. The model was trained on 400 synthetic seismograms generated with SPECFEM3D, simulating earthquakes with magnitudes between 4 and 7 at a fixed depth of 40 km. The study highlights the advantage of using single-component data, making it applicable in regions with sparse seismic networks. Six features were extracted from the seismic signal: mean amplitude, maximum amplitude, RMS, P- and S-wave arrival times, and the P-S interval. The model's performance was evaluated using various metrics. For magnitude estimation, the RMSE was 0.0412, with a perfect \(R^{2}\) of 1.0, indicating exact predictions. Despite these strong results, the model's reliance on synthetic data raises concerns about its generalizability to real seismic events, especially in high-noise environments. For hypocentral distance, the model achieved an RMSE of 485.53 m and an \(R^{2}\) of 1.0, with high accuracy. The elevation angle estimation was equally accurate, with an RMSE of 0.0056 radians and \(R^{2}\) of 1.0. However, azimuth angle estimation was less accurate, with an RMSE of 68.85 and \(R^{2}\) of 0.58. The model was tested under noise conditions, adding 1%, 3%, and 5% noise levels to synthetic seismograms. While the model maintained robustness for \(M>5\) events, its accuracy significantly declined for smaller magnitudes (\(M_{w}\) 4), suggesting a strong dependency on SNR. Ref. [60] developed a model for rapid seismic magnitude estimation in the Sichuan-Yunnan region, China, using SVM and transfer learning. The TLSVM-M model adjusts a pre-trained model to improve accuracy in magnitude estimation within the first 3 s of the P-wave.