## 2 Materials and Methods
### Literature Summary

Table 1: _Cont._for preprocessing, as mentioned in the study by [PERSON] et al. [31], highlight the challenges in ensuring data quality before applying recognition models. 4. Need for larger datasets: Many studies have highlighted the importance of larger and more diverse training datasets for the robustness of model performance. For instance, [PERSON] et al. 's [37] study emphasizes the need for extensive research to quantify the robustness of their method, indicating that dataset size could be a potential limiting factor. 5. Success of transfer learning: [PERSON] et al. 's [51] demonstration of the success of transfer learning, where pretrained models like AlexNet and GoogLeNet outperform traditional SVM classifiers, suggests that leveraging existing knowledge in models could be a powerful strategy in microseismic event recognition. 6. Computational resource requirements: While deep learning methods are powerful, they demand a substantial amount of labeled data and computational resources. This underscores a practical consideration for researchers, especially those with limitations in data availability or computing infrastructure. 7. Appeal for comprehensive evaluation: Several studies call for larger-scale research to quantify the robustness and generalizability of their proposed methods. This emphasizes the need for a comprehensive evaluation framework to assess the practical applicability of developed models. The verification of the stability and robustness of the model primarily includes (1) a stability assessment, which generally involves using techniques such as cross-validation, repeated experiments, or introducing noisy data to evaluate the consistency and stability of the model under different conditions and (2) robustness testing, which typically involves using adversarial samples to test the model's response to interference and perturbation. Additionally, introducing variations in input data, such as scaling, rotation, or translation, can be used to assess the model's robustness. Methods to enhance model robustness include using data augmentation techniques to expand the training dataset, applying regularization methods to reduce the risk of overfitting, and using ensemble learning methods to obtain more stable and robust predictions. Checking the results of different studies and validate their reproducibility usually involves the following methods: (1) dataset examination: examine the dataset used in each study. Evaluate the quality, scale, and applicability of the dataset. Understand information such as the source of the dataset, data collection methods, and preprocessing steps to ensure the reliability and reproducibility of the dataset. (2) Method description: carefully read the detailed description of the methods used in each study. Understand details such as model architecture, hyperparameter settings, training strategies, etc. Ensure that the method description is sufficiently clear, allowing other researchers to replicate the experiments following the same steps. (3) Code sharing: check if there is code related to the study available for sharing. If so, attempt to reproduce the author's experimental results. Running the same code with the same dataset and settings can validate the reproducibility of the results. (4) Result comparison: compare the results of different studies. Pay attention to the consistency and differences between them. Focus on changes in performance metrics (such as accuracy, recall, etc.) and how the model performs on different datasets or tasks. (5) External validation: attempt to validate the results of other studies using similar datasets and tasks. Through repeated experiments, the model's generalization ability and reliability can be assessed. (6) Collaborative communication: collaborate and communicate with other researchers to discuss their experimental results with the same methods and datasets. Through communication, the reproducibility of the results can be further verified, and potential issues can be addressed collaboratively.