## 5 Discussion  
The proposed UMAP-CNN framework has significant practical implications for seismic exploration and reservoir characterization. By providing a more accurate and efficient method for seismic waveform features extraction, the framework can aid in identifying favorable zones for hydrocarbon accumulation, improving drilling success rates, and optimizing reservoir management strategies. Additionally, the framework's ability to handle large-scale datasets makes it particularly valuable in regions with limited well data, where traditional methods often struggle to provide reliable predictions. The results from the Ordos Basin case study highlight the framework's potential to enhance geological interpretations and support decision-making in exploration and production activities.  
When compared to other manifold learning techniques such as t-SNE, LLE, and Isomap in the experiment, the UMAP-CNN framework demonstrates a superior performance in both computational efficiency and classification accuracy. This performance gain can be attributed to the framework's ability to balance local and global data structures while effectively capturing the intrinsic geometry of high-dimensional seismic data. The integration of UMAP and CNN leverages the complementary strengths of manifold learning and deep learning, overcoming the limitations of shallow feature representation often encountered in traditional manifold learning techniques and mitigating the \"curse of dimensionality\" associated with high-dimensional feature spaces in deep learning. This synergy results in a more robust and interpretable feature representation, which is critical for accurate seismic facies classification and geological interpretation.  
Despite its advantages, the UMAP-CNN framework has certain limitations that warrant further investigation. First, the framework's performance is sensitive to hyperparameter selection. While this study employed default hyperparameters for consistency, future work could explore automated hyperparameter tuning or adaptive methods to optimize performance across different datasets. Second, the framework's computational  
Figure 10: Sedimentary facies diagram of the Benxi Formation based on stratigraphic interpretation.  
efficiency may still be a challenge for extremely large seismic datasets due to the memory requirements of UMAP. Addressing this limitation could involve developing more efficient implementations of UMAP or integrating parallel computing techniques to handle large-scale data.  
Another area for future improvement is the incorporation of additional geophysical attributes and prior geological knowledge into the framework. While the current study focuses on seismic waveform data, integrating other attributes such as amplitude, phase, and frequency could enhance the framework's ability to capture multidimensional relationships in seismic data. Furthermore, extending the framework to account for temporal or spatial dependencies in seismic data could improve its applicability to dynamic reservoir characterization and monitoring.  
In summary, the UMAP-CNN framework offers a promising approach to seismic waveform feature extraction and clustering, addressing key challenges in traditional methods while providing robust and interpretable results. Future research could further refine the framework to address its limitations and expand its applicability to a broader range of geophysical problems.