## 4 Discussion  
Through the analysis of comparative experimental data, our model demonstrates superior performance across multiple key indicators, showcasing its unique design advantages and optimization methods. Firstly, our model employs intelligent algorithms to optimize survey line layout, allowing the lines to adapt to the seabed terrain's complex variations dynamically, ensuring higher coverage efficiency and measurement accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Average Effective** & **Average Coverage** & **Average Survey** & **Weighted Average** \\  & **Coverage Area (m\({}^{2}\))** & **Rate (\%)** & **Line Length (m)** & **Overlap Rate (\%)** \\ \hline AMUST & \(1.0602\times 10^{8}\) & 88.56 & 528,235 & 12.24 \\ DRS & \(1.0410\times 10^{8}\) & 86.95 & 545,410 & 10.84 \\ LPMOM & \(1.0448\times 10^{8}\) & 87.27 & 559,507 & 11.58 \\ Baseline & \(8.743\times 10^{7}\) & 73.03 & 101,860 & \(-\)5.42 \\ Our Model & \(1.1897\times 10^{8}\) & 94.98 & 474,662 & 3.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: This table details the performance of five different multibeam echosounder models on key performance metrics, including average effective coverage area, average coverage rate, average survey line length, and average weighted overlap rate. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elevation Change (m)** & \(\mathbf{-300}\) & \(\mathbf{-150}\) & \(\mathbf{0}\) & \(\mathbf{+150}\) & \(\mathbf{+300}\) \\ \hline
**Effective Coverage Area (m\({}^{2}\))** & \(1.2443\times 10^{8}\) & \(1.2659\times 10^{8}\) & \(1.3039\times 10^{8}\) & \(1.2944\times 10^{8}\) & \(1.2935\times 10^{8}\) \\
**Coverage Rate (\%)** & 92.43\% & 92.80\% & 95.25\% & 94.89\% & 94.82\% \\
**Total Survey Line Length (m)** & 492,572 & 493,723 & 494,387 & 495,621 & 495,876 \\
**Weighted Average Overlap Rate (\%)** & 9.92 & 8.67 & 7.51 & 10.53 & 11.16 \\ \hline \hline \end{tabular}  
* The data for this robustness analysis experiment are derived from the 2 nd group of the dataset and four additional groups adjusted accordingly. The total area of the sampling interval is 1.3461\(\times\)10\({}^{8}\) m\({}^{2}\).  
\end{table} 
Table 3: The table details the robustness analysis experiment results, revealing the impact of changes in water depth on the model’s effective coverage area, coverage rate, total survey line length, and weighted average overlap rate. Our model achieves an effective coverage area of \(1.1897\times 10^{8}\) m\({}^{2}\), significantly surpassing other models. Although the AMUST model enhances the reliability of measurement data, its effective coverage area is relatively smaller. Secondly, our model significantly reduces the total survey line length to only 474,662 m through refined algorithm optimization, demonstrating higher economic efficiency. In contrast, while the LPIOM model improves the precision of terrain model generation, it may lose some key terrain features when dealing with complex topography. Our optimization method expands the coverage area and accuracy and reduces overlap rates, enhancing data integrity and measurement efficiency. These characteristics enable our model to complete measurements more efficiently and economically in practical applications. Our model demonstrated significant advantages in the robustness analysis, particularly regarding adaptability and reliability under different water depth conditions. By altering the seabed depth of the original dataset, we validated the model's sensitivity and adaptability to various environmental variable changes. The analysis results indicate that our model performs well under various elevation change conditions. When the elevation ranges from \(-550\) to \(-250\) m, our model exhibits optimal coverage efficiency and the lowest overlap rate, with an average overlap rate of only 7.51%. This indicates that our model can perform measurements most effectively under moderate water depth conditions while maintaining high measurement accuracy and data integrity.