顾及众源水深数据特征的海峡通道DDM构建方法

doi:10.11947/j.AGCS.2026.20250325

摘要/Abstract

摘要：

针对当前方法未充分考虑众源水深数据分布不均匀、精度差异大等特点，导致所构数字水深模型（DDM）质量偏低的问题，本文提出了一种顾及众源水深数据分布与精度差异的海峡通道DDM构建方法。首先，分析原始数据分布不均匀、精度差异大对格网节点水深内插的影响机理；然后，考虑数据分布不均匀可能引起不同方向上的参考点数量存在较大差异，设计顾及原始数据分布各向异性的参考点八方向数量动态调优机制，避免传统方法因“方向性倾斜”导致内插方法稳健性差的问题；最后，以反距离加权的内插函数为基础，在函数中进一步考虑数据分布不均匀、精度差异大等因素的影响，通过引入数据精度因子、分布因子及方位因子，调和不同众源水深数据点对格网节点水深插值的贡献差异，提高格网节点的内插精度。综合试验结果表明，本文方法在DDM整体构建精度、不同海底地形适应性及方法稳健性方面均优于常规IDW和普通克里金插值等对比方法，能够更好地顾及众源水深数据特征及地形变化特征。权重因子的有效性试验进一步表明，本文方法能够更全面地刻画众源水深数据的空间特征与质量差异，从而提升DDM构建的精度与稳定性。

关键词: 数字水深模型, 众源水深数据, 海峡通道, 空间异质性, 特征因子融合, 综合优化IDW法

Abstract:

Aiming at the problem that the current methods fail to fully consider the characteristics of the uneven distribution and large precision differences in crowdsourced bathymetric data, resulting in the low quality of the constructed digital depth model (DDM), a method for constructing a DDM of a strait channel is proposed considering the distribution and precision differences of crowdsourced bathymetric data. Firstly, the influence mechanism of the uneven distribution and large precision differences of the original data on the interpolation of the grid node is analyzed. Then, considering that the uneven data distribution may lead to significant differences in the number of reference points in different directions, a dynamic adjustment mechanism for the number of reference points in eight directions is designed, which takes into account the anisotropy of the original data distribution and aims to avoid the problem of poor robustness of the interpolation method caused by the “directional tilt” in the traditional method. Finally, based on the inverse distance weighted interpolation function, the influences of factors such as the uneven data distribution and large precision differences are further considered in the function. By introducing the data precision factor, distribution factor, and direction factor, the contribution differences of different crowdsourced bathymetric data points to the interpolation of the grid node bathymetry are reconciled to improve the interpolation accuracy of the grid nodes. The experimental results show that: the integrated optimization IDW method proposed in this paper demonstrates superior performance in overall accuracy of DDM construction, adaptability to different seabed topographies, and robustness compared to conventional IDW methods and ordinary Kriging interpolation, which can effectively take into account the characteristics of multi-source depth data and changes in topography. Furthermore, through the effectiveness analysis of different weighting factors, the proposed method is validated to more comprehensively characterize the spatial features and quality differences of multisource depth data, thereby enhancing the accuracy and stability of DDM construction.

Key words: digital depth model, crowdsourced bathymetric data, strait passage, spatial heterogeneity, feature factor fusion, integrated optimization IDW method

中图分类号:

P229

孙启钤, 贾帅东, 梁志诚, 刘现鹏, 宋浩石. 顾及众源水深数据特征的海峡通道DDM构建方法[J]. 测绘学报, 2026, 55(1): 90-100.

Qiqian SUN, Shuaidong JIA, Zhicheng LIANG, Xianpeng LIU, Haoshi SONG. A method for constructing digital depth model of strait passage considering crowdsourced bathymetric data characteristics[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(1): 90-100.

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编号	测量时间	定位手段	测深手段	作业条件	精度
1	2021	GNSS	单波束测深	海况好，航速平稳，设备状态良好	高
2	2021	GNSS	单波束测深	海况较好，船舶航行状态较稳定	中
3	2020	GNSS	单波束测深	海况较差，船舶航行状态颠簸	低
4	2018	GNSS	单波束测深	海况较好，船舶航行状态较稳定	中

方法	RMSE	MAE
常规IDW法	3.366	2.154
优化选取IDW法	3.338	2.110
优化内插IDW法	2.903	1.851
综合优化IDW法	2.829	1.794
克里金法	3.109	1.946

点号	参考值	克里金		常规IDW		优化选取IDW		优化内插IDW		综合优化IDW
点号	参考值	内插值	绝对误差	内插值	绝对误差	内插值	绝对误差	内插值	绝对误差	内插值	绝对误差
1	511.17	503.55	7.62	502.73	8.44	518.04	6.87	516.41	5.24	507.23	3.94
2	502.57	495.36	7.21	493.42	9.15	509.66	7.09	508.26	5.69	499.22	3.35
3	473.53	466.87	6.66	465.64	7.89	479.76	6.23	468.64	4.89	477.03	3.50
4	465.83	458.05	7.78	474.51	8.68	458.02	7.81	461.53	4.30	469.33	3.50
5	376.01	368.81	7.20	369.11	6.90	370.48	5.53	379.96	3.95	373.24	2.77
6	544.56	535.96	8.60	534.40	10.16	553.64	9.08	551.00	6.44	540.78	3.78
7	515.99	508.05	7.94	506.22	9.77	507.66	8.33	510.15	5.84	520.19	4.20
8	465.46	455.49	9.97	456.67	8.79	459.34	6.12	470.41	4.95	468.52	3.06
9	508.48	500.74	7.74	518.07	9.59	516.59	8.11	502.15	6.33	512.41	3.93
10	439.20	431.78	7.42	446.43	7.23	445.23	6.03	434.61	4.59	442.37	3.17

权重因子组合	RMSE	MAE
单一距离因子	2.968	1.875
距离因子+分布因子	2.830	1.792
距离因子+分布因子+精度因子	2.903	1.851
距离因子+方位因子+分布因子	2.873	1.817
综合优化IDW法	2.829	1.794