# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 5 Evaluation
### Training  
We designed the 3D-CNN-RNN architecture, which includes 3D convolution filters and three convolutional layers. The model was trained using a drop-out rate of 0.3. We employed a sigmoid layer to calculate the loss for solving multi-label classification task. [PERSON]'s optimization method is used with a momentum of 0.9 and a starting learning rate of \(10^{-3}\) to prevent vanishing gradient problems and optimize the learning rate. We trained our models using a batch size of 64, i.e., 64 training examples. We train our baseline model using 97% of the total training data and use a 3% validation split to compute validation loss and monitor the training process. To minimize overfitting and promote generalization, we shuffled the data in the training set and halted the training after 50 epochs. The model is implemented using TensorFlow. Our networks are trained on a single NVIDIA V100 GPU. Our suggested architecture requires 14 h of learning time, including feature extraction, training, and testing.  
In the proposed multi-label structure, label distribution and feature extraction are essential factor for classification performance. The experimental results summarize our proposed method's accuracy and demonstrate improvement in the previous study's classification results utilizing a multi-class approach. Specifically, we transformed labels to multi-label prediction to contain labels with numeric values with a certain threshold. However, varying the threshold will impact the results and lead to different predictions. Additionally, we analyze the algorithm's classification performance regarding the proportion of successfully categorized earthquake signals into distinct earthquake classes with multiple labels. We select the best model in terms of accuracy, and the precision metrics remain the same every time we take the classification report. There is only a 0.005 variation which is usually ignorable when we obtain the accuracies up to two decimal points. Furthermore, we also monitor the inference speed for each prediction, i.e., 2.27 seconds using a dedicated V100 GPU. We use an input length of sixty seconds of the signal to calculate the inference speed. Since this is the first experiment of its type, the findings obtained using our suggested approach validate as the standard for multi-class and multi-label earthquake magnitude categorization. As seen in Tables 2 and 3, there is a significant improvement in the overall accuracy of the predicted labels. In Tables 2 and 3, the term \"Magnitudes (0-1)\" means we include all the earthquakes with magnitude greater than zero and less than equal to one in this category. The same procedure is valid for all the other categories.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Earthquake Categories** & **Precision** & **Recall** & **F1-Score** \\ \hline Magnitudes (0–1) & 0.72 & 0.60 & 0.65 \\ Magnitudes (1–2) & 0.52 & 0.52 & 0.52 \\ Magnitudes (2–3) & 0.50 & 0.34 & 0.40 \\ Magnitudes (3–4) & 0.46 & 0.58 & 0.52 \\ Magnitudes (4–8) & 0.61 & 0.75 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-class: Accuracy results for the proposed method in the reference paper [33].  
We trained our model using a real-world, relatively small dataset (see Section 3.1) in addition to improving its generalizability in real-world circumstances. Our model's effectiveness depends on the feature extraction approach to extract spectral and temporal information. Given the presence of significant seismic traffic, a signal de-noising approach may be used to decrease the rate of misidentification in various earthquake classes. Although it is difficult to assume that excessive seismic noise is only responsible for misclassifications, raw signal amplification by instruments might be one of the explanations for the findings mentioned above.