## 4 Discussion
### Uncertainty Analysis
For example, in Figure 6, Methods A, B, and C show uniform confidence distribution but with large low-confidence areas, suggesting inadequate learning or poor adaptability to noisy data. In contrast, Methods D and E exhibit higher confidence in complex and noisy regions, adjusting dropout rates and activation strengths to enhance confidence and improve reconstruction accuracy. Notably, in Region 2 (e.g., a volcanic ridge with dramatic terrain changes), high-confidence regions further validate the model's ability to capture such features effectively. Uncertainty error maps for test regions (R1 and R2) visualize normalized prediction variance (0-1 scale) derived from MC-Dropout sampling, where pixel-wise variance is calculated across multiple predictions and scaled proportionally to local bathymetric depth. This dimensionless normalization addresses the nonlinear engineering significance of absolute errors (originally in m\({}^{2}\)) across varying seafloor elevations. By converting raw variance into relative error percentages, the method decouples error magnitude from topographic context while suppressing misleading high absolute errors in deep-sea regions caused by measurement noise. Low variance (deep blue) indicates stable, reliable predictions, while high variance (warm or orange-yellow) suggests fluctuations due to data noise, model limitations, or regional complexity. Figure 7 illustrates that Methods A, B, and C consistently show low uncertainty, potentially indicating overconfidence in predicting complex regions. Figure 5: Comparison of uncertainty metrics between five networks with different network structures: (a) ECE, (b) entropy, and (c) error variance. In contrast, Methods D and E demonstrate higher uncertainty, indicating sensitivity to risks and noise, thus accurately identifying predictive risks and model blind spots. Method E, in particular, displays fewer high-variance regions, indicating superior performance. The variance heatmap, using color gradients, highlights variance levels, with warm tones indicating high variance and cool tones indicating low variance. To enable cross-method comparison, the variance values in Figure 8 are normalized using Z-score standardization, transforming absolute variances (original unit: m\({}^{2}\)) into dimensionless quantities  
Figure 6: Comparison of confidence heatmaps for different network structures. Figure 7: Comparison of uncertainty heat maps for different network structures. relative to each method's distribution. This normalization maps all results to a unified (\(-3\sigma\), \(+3\sigma\)) color scale, addressing the critical issue of variance magnitude disparities spanning multiple orders of magnitude across regularization strategies. The normalized representation not only highlights statistically significant high-variance anomalies but also exposes relative stability differences among methods in complex topographic regions through variance intensity rankings. Figure 8 shows significant warm-colored areas for Methods A, B, and C, particularly Method C, indicating high uncertainty and difficulty in capturing local details with layered dropout approaches. In contrast, Methods D and E show more uniform blue or light-colored regions, with high-variance areas confined to specific locations. Combined with metrics like error variance and ECE, these results highlight lower prediction errors in high-variance regions, emphasizing the effective identification of high-risk areas while maintaining reconstruction accuracy. In summary, evaluating uncertainty quantification methods (Experiments A-E) in DBM reconstruction tasks reveals that simple dropout strategies, while helpful against overfitting, often fail to capture prediction uncertainty accurately, leading to instability in local predictions. Optimized experimental designs, especially with MC-Dropout and heteroscedastic noise modeling (Experiment E), significantly reduce error variance and ECE in complex regions, enhancing structural fidelity and high-frequency information recovery.