## 3 Results and Discussion
### Comparison of Deep Learning Models  
Deep learning methods play a crucial role in microseismic data analysis. By automatically learning and extracting features from waveform data, these methods provide powerful tools for seismologists and engineers to understand subsurface structures and seismic activities. However, having a clear understanding of the advantages and disadvantages of different deep learning methods is essential when choosing the appropriate approach. Table 3 provides descriptions, advantages, and disadvantages analysis for several common deep learning methods. From CNNs that capture spatial features to LSTM networks that handle temporal dependencies, each method has its own characteristics and application scope. Additionally, transfer learning, attention mechanisms, and ensemble methods have been introduced to further enhance the classification performance and information extraction capabilities of microseismic data. A comprehensive evaluation of these deep learning methods can assist researchers in selecting the most suitable approach for their research objectives and drive the development and innovation in the field of microseismic data analysis.  
Taking CNNs as an example, we have summarized the advantages and disadvantages of commonly used classical image classification models (AlexNet, GoogLeNet, ResNet) and the standard CNN model. Specific details can be found in Table 4. This analysis  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Deep Learning Method** & **Description** & **Advantage** & **Disadvantage** \\ \hline Convolutional neural networks (CNNs) & Utilizes convolutional layers to automatically learn hierarchical features from raw waveform images. Suited for sequential data, processes waveforms in a time series manner, capturing temporal patterns. & Captures spatial features, strong in image data & Requires large labeled datasets, complex model architectures \\  & A type of RNN designed to avoid long-term dependency issues that effectively captures long-range temporal information. & Captures temporal dependencies, adaptable & Vulnerable to vanishing/exploding gradient problem, less suitable for complex spatial patterns \\  & A type of RNN designed to avoid long-term dependency issues that effectively captures long-range temporal information. & Handles long-range dependencies, suitable for time series & More complex to implement, and requires careful tuning and training \\  & Another type of RNN that balances complexity and performance and is similar to LSTM but with fewer parameters. Utilizes pretrained models on large datasets to extract general features and then fine-tunes for specific microseismic data. Enhances feature extraction by assigning different weights to different parts of the input waveform, focusing on important details. & Efficient memory usage, adaptable & May struggle with very long sequences, less expressive than LSTM \\  & Combines multiple deep learning models to improve classification performance and reduce individual model bias. & Requires less labeled data, faster convergence & Depending on the availability of relevant pretrained models, potential domain gap issues \\ \hline \hline \end{tabular}
\end{table}
Table 3: A summary and comparison of commonly used deep learning methods for microseismic event waveform recognition and classification.  
complements the findings in Table 3 and provides a more in-depth discussion. Overall, these classical image classification models have all solved the challenges in deep convolutional neural networks to varying degrees, and have achieved remarkable results in image classification tasks. Selecting a suitable model requires weighing the advantages and disadvantages according to the requirements of the task and the available resources.