A method for constructing digital depth model of strait passage considering crowdsourced bathymetric data characteristics

doi:10.11947/j.AGCS.2026.20250325

Abstract

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

CLC Number:

P229

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.

Figures/Tables 11

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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