## 6 Conclusions and Outlook  
In this study, ResNet18 was employed for spatial feature extraction, with transfer learning accelerating convergence and enhancing model performance. The integration of the CBAM further enabled the model to focus on critical spatial and channel features, improving the identification accuracy by capturing spatial-channel interdependencies and thereby effectively addressing challenges in waveform feature extraction. Testing on 300 randomly selected waveforms yielded an average accuracy of 97.6% for the proposed model, providing valuable insights for microseismic magnitude prediction and contributing to seismological research. Notably, the model achieved a favorable balance between  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Calculated** & **Error \textless{} 0.3 Sample** & **Error \textless{} 0.2 Sample** & **Variance** \\  & **Sample Number** & **Number** & **Number** & \\ \hline CNN & 300 & 214 & 166 & 0.9553 \\ Alexnet & 300 & 269 & 210 & 0.5895 \\ VGGnet & 300 & 273 & 241 & 0.271 \\ ResNet18 + CBAM & 300 & 284 & 261 & 0.1362 \\ ResNet34 + CBAM & 300 & 279 & 249 & 0.2219 \\ ResNet50 + CBAM & 300 & 285 & 255 & 0.1893 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Table of magnitude prediction results.  
Figure 12: The magnitude prediction results for residual networks with different numbers of layers.  
performance and complexity: compared to ResNet50, its parameter size was reduced by 60%, and the training time per epoch was shortened by 40% (12 min vs. 20 min), making it more adaptable to microseismic monitoring devices with limited resources. It should be acknowledged that this study focused on verifying core performance; while the model selection logic already reflects efficiency considerations, metrics such as FLOPs and inference latency have not been quantified. These will be supplemented in future work to provide a more comprehensive evaluation of the model's practical applicability.  
This study primarily focused on waveform feature extraction. Future work will proceed in two directions: first, leveraging the model's speed and accuracy to enable applications in real-time prediction scenarios; second, enhancing model efficiency by reducing model parameters and computational overhead through adaptive weight adjustments of influencing factors, while simultaneously enhancing generalization ability.  
Practical prediction is influenced by multiple factors, including the source depth, regional attenuation, and station location. Additionally, magnitude measurement methods vary with the source depth. Given that the dataset in this study consisted of waveform images, future research should also consider the impact of abnormal waveforms and noise. Potential improvements include incorporating diverse waveform patterns, adding waveform preprocessing modules for optimization, using modified residual networks with transfer learning for magnitude classification, and ultimately developing new technical solutions for magnitude prediction.  
Methodology, [PERSON] ([PERSON]); Formal analysis, [PERSON] ([PERSON]); Investigation, [PERSON] ([PERSON]); Writing--original draft, [PERSON] ([PERSON]); Writing--review & editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
National Key Research and Development Program of China 2023 YFC3008904-02.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.