## 3 Results
### Early Earthquake Magnitude Estimation as a Regression Task  
Of the reviewed studies, 23 of 28 approached earthquake magnitude estimation as a regression task. The work presented in [7] estimates \(M_{L}\) and duration magnitude (\(M_{D}\)) using a single seismic station without requiring input data normalization. The network's input consists of three-component seismograms of 30 s duration (3000 samples), which includes both P and S waves, allowing the model to capture a more complete representation of seismic signals for improved magnitude estimation. The architecture consists of two convolutional layers for feature extraction, each followed by dropout and max-pooling layers for dimensionality reduction and regularization. The LSTM layer captures temporal dependencies in the waveforms, and the final fully connected layer predicts the earthquake magnitude. The model was trained using the Adam optimizer and minimizes mean squared error (MSE) as the loss function. The study demonstrates that neural networks can learn attenuation relationships and site effects necessary for magnitude estimation, achieving high prediction accuracy with a mean error close to zero and a standard deviation of \(\sim\)0.2. When using only a P-wave window without the S-wave, the model struggled to make accurate predictions, limiting its effectiveness for EEW applications. Similarly, in [45], magnitude estimation was performed, but using a single seismic station with only 3 s of P-wave data. The AMagDN model combines autocorrelation attention, LSTM, Bi-LSTM, and ML blocks to produce precise magnitude estimations. The ML block employs models like XGBoost, RF, and support vector regression (SVR) for final magnitude estimation. Unlike in [7], robust scaling was applied to normalize data features. The model performance indicates overestimation for low magnitudes (5.0-6.5 \(M_{\text{JMA}}\)) and underestimation for high magnitudes (6.5-8.0 \(M_{\text{JMA}}\)). However, the prediction's standard deviation (\(\sigma\)) remains within \(\pm\)0.55, indicating relatively low error. Moreover, the error decreases with greater epicentral distances, while inaccuracies occur for closer distances due to the rapid arrival of the P-wave. In [31], the first 3 s of the P-wave was also used with acceleration records. A machine learning-based approach using the XGBoost model was employed and compared to well-known methods such as \(\tau_{c}\) and \(P_{d}\). The model's improved accuracy, achieved through 29 extracted features, suggests that early P-wave characteristics contain enough information for rapid and reliable magnitude estimation. Similar to [45], tests were conducted on two earthquakes in Japan with magnitudes 4.5 and 6.1 \(M_{\text{JMA}}\). Based on the P-wave, the model predicted magnitudes of 4.58 \(\pm\) 0.33 and 6.32 \(\pm\) 0.29, respectively. This supports the feasibility of using short-window P-wave data for early warning applications. These results demonstrate that early ground motion (3 s of P-wave data) during an earthquake can effectively predict magnitude with reasonable accuracy. However, the model's reliance on a single seismic station and limited waveform duration could lead to underestimation or overestimation in complex ruptures or varying epicentral distances. Ref. [33] introduced a conformer-based architecture enhanced with LoRA for seismic event classification and magnitude estimation. Instead of using raw waveforms, the model employed spectrograms derived from Z-component seismograms via short-time Fourier transform (STFT), which may introduce a dependence on preprocessing techniques. The input is processed through convolutional subsampling, linear layers, and dropout. The conformer encoder is simplified to six layers with reduced dimensionality, drastically lowering parameters from 1.07 M to 12 K while preserving performance. The decoder differs for each task, for example, fully connected layers for classification and a Bi-LSTM for magnitude estimation.