# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 6 Conclusions  
This study provides a 3D-CNN-RNN-based methodology for classifying earthquake magnitudes. We have presented a novel method that can automatically categorize earthquake magnitudes using artificial intelligence-based algorithms. We investigate earthquake magnitude categorization as a multi-label classification problem by assessing the characteristics derived from log-Mel spectrograms and studying the interactions between distinct earthquake categories. Moreover, to address the earthquake categorization problem, we provide a 3D-CNN-RNN-based framework. It contains a 3D-CNN for extracting features from an input spectrogram and recurrent layers to examine patterns between seismic signals. Furthermore, we also create a new multi-label earthquake dataset using an existing earthquake dataset. Based on the results, we summarize that the multi-label classification model demonstrates how effectively deep learning-based classifiers can accurately detect earthquakes of varying intensities. As part of our future research, we want to investigate more closely the relationships between earthquake recognition and its application in real-world environments using semi-supervised-based classification approaches. Furthermore, it will be interesting to categorize earthquakes by creating synthetic data and leveraging encoder-decoder-based models to achieve better performance. The findings from this research have demonstrated that deep learning-based algorithms can automatically classify earthquake magnitudes based on data from single stations. Moreover, we report the inference speed for each prediction using a dedicated GPU; however, in the future, more work is needed by using model pruning techniques to deploy the learned model on a personal seismometer having no GPU. This also provides an opportunity for future study into the efficient integration of ground-motion sensors in urban areas for the early detection of minor-to-major earthquakes. This project is part of a larger effort to build a comprehensive deep-learning-based infrastructure for seismic signal recognition and prediction. The future research will explore the real-time deployments of the Artificial intelligence system on personal seismometers and its effectiveness in densely populated locations with a high level of seismic activity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Earthquake Categories** & **Precision** & **Recall** & **F1-Score** \\ \hline Magnitudes (0–1) & 0.97 & 0.50 & 0.66 \\ Magnitudes (1–2) & 0.98 & 0.69 & 0.81 \\ Magnitudes (2–3) & 0.83 & 0.51 & 0.63 \\ Magnitudes (3–4) & 0.93 & 0.90 & 0.91 \\ Magnitudes (4–8) & 0.84 & 0.81 & 0.82 \\ Non-earthquake & 0.99 & 0.87 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-label: Accuracy results for the proposed method in this study.  
**Author Contributions:** Conceptualization, M.S. and K.N. ([PERSON]); methodology, M.S.; software, M.S.; validation, M.S.; formal analysis, M.S.; investigation, M.S.; resources, M.S.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, M.S., K.N. ([PERSON]), K.I. and K.N. ([PERSON]); visualization, M.S.; supervision, K.N. ([PERSON]); project administration, M.S.; funding acquisition, K.N. ([PERSON]), K.I. and K.N. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant No. JP19K12017, JP19 KK0260 and JP20H00475.  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available in a publicly accessible repository: The data presented in this study is publicly available online through [[https://doi.org/10.1109/ACCESS.2019.2947848](https://doi.org/10.1109/ACCESS.2019.2947848)]([https://doi.org/10.1109/ACCESS.2019.2947848](https://doi.org/10.1109/ACCESS.2019.2947848)), reference number [29], at [[https://github.com/smousavi05/STEAD](https://github.com/smousavi05/STEAD)]([https://github.com/smousavi05/STEAD](https://github.com/smousavi05/STEAD)).  
The authors declare no conflict of interest.