## 3 Results and Comparative Experiments
### Experimental Results and Comparative Experiments  
Since the CASLOM model already incorporates the algorithms and functions of the PMOP model, the experimental section focuses solely on evaluating and conducting comparative experiments on the CASLOM model, which is suited for complex marine environments. Figure 10 illustrates the CASLOM model's survey line results in the second, fourth, and sixth sampling areas of the marine region, demonstrating how the survey lines curve in response to the complex variations in the seabed terrain. The lines are arranged at certain intervals to ensure comprehensive area coverage while avoiding unnecessary resource waste. There is some overlap between the survey lines, with the overlap rate kept below 20%, ensuring continuity and redundancy of the survey data, preventing omissions, and improving measurement accuracy. Table 1 displays the effects of survey line design by the CASLOM model across eight sampling areas in the marine region. Data from four key performance indicators show how our model performs across all datasets. To comprehensively evaluate the application potential of our method in bathymetric surveying technology, we conducted a comparative analysis with three current cutting-edge academic research models. [PERSON] and others proposed the Multibeam Echosounder Measurement Uncertainty Prediction Model (AMUST) [26], which uses statistical methods to predict measurement uncertainties, thereby improving the reliability of measurement data. [PERSON], [PERSON], and [PERSON] discussed the Dual Robust Strategy (DRS) for removing outliers in multibeam echosounding to enhance seabed terrain quality estimation [27], using advanced processing techniques to enhance the robustness and accuracy of data processing. [PERSON] proposed the Local Polynomial Interpolation Optimization Method (LPIOM) [28], which improves the precision of terrain model generation by applying local polynomial interpolation techniques. In addition, this experiment used a baseline model as the most basic control. This method uses a very basic multibeam echosounder system configuration, with evenly spaced survey lines and an interval between each line fixed at 700 m. In the baseline model, the survey vessel measures along the longer side of the sampling area as the survey line direction to reduce the number of turns. We conducted experiments with these four models alongside our own using all data from the dataset, designing survey lines to determine the effective coverage area and total length of survey lines for each model. We then compared the performance metrics across the models. Table 2 displays the average results of comparative experiments conducted across eight datasets. Our model achieves an effective coverage area of \(1.1897\times 10^{8}\) m\({}^{2}\), with a coverage rate as high as 94.98%, the highest among all models. Additionally, our model is highly efficient regarding total survey line length, requiring only 474,662 m, which is shorter than any other model. In terms of the weighted average overlap rate, our model is only \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CASLOM** & **Effective Coverage** & **Coverage** & **Total Survey** & **Weighted Average** \\
**Model** & **Area (m\({}^{2}\))** & **Rate (\%)** & **Line Length (m)** & **Overlap Rate (\%)** \\ \hline Dataset 1 & \(1.1228\times 10^{8}\) & 94.95 & 470,527 & 3.12 \\ Dataset 2 & \(1.2169\times 10^{8}\) & 95.02 & 476,893 & 3.19 \\ Dataset 3 & \(1.1887\times 10^{8}\) & 94.89 & 474,671 & 3.21 \\ Dataset 4 & \(1.2331\times 10^{8}\) & 95.01 & 479,438 & 3.35 \\ Dataset 5 & \(1.1972\times 10^{8}\) & 94.96 & 474,792 & 3.25 \\ Dataset 6 & \(1.1904\times 10^{8}\) & 95.03 & 474,556 & 3.22 \\ Dataset 7 & \(1.2095\times 10^{8}\) & 94.92 & 475,847 & 3.16 \\ Dataset 8 & \(1.1580\times 10^{8}\) & 95.06 & 471,572 & 2.94 \\ \hline \hline \end{tabular}
\end{table}