High-precision seafloor topographic mapping based on data-knowledge-driven: An example from the South China Sea

doi:10.3969/j.issn.1001-909X.2024.03.012

Abstract

Abstract:

Seafloor topography is of considerable value in commercial, engineering, military and scientific research. Currently, gravity field data, such as free air gravity anomalies and vertical gravity gradients, are commonly used to inverse seafloor topography. However, due to the strong multi-resolution of the existing methods to inverse seafloor topography, it is still impossible to obtain accurate high-precision seafloor topography. A new data-knowledge-driven method was proposed to reconstruct accurate seafloor topography, which combines the gravity-density method with random forests. This method was applied to the South China Sea and compared with the gravity-density, random forest, and existing SIO models. The inversion results show that the data-knowledge-driven method provides better inversion performance, followed by the random forest and gravity-density methods, and the SIO model is the worst. The mean absolute error, mean relative error and root mean square error of the data-knowledge-driven are reduced by 21%, 25% and 7%, respectively, compared to those of the gravity-density method, while they are also reduced by 20%, 20% and 20%, respectively, compared to those of the random forest. In addition, the data-knowledge-driven model has a high degree of agreement with the shipboard bathymetry data, with approximately 72% of its differences distributed within ±10 m, which is higher than that of the other three models. The results demonstrate the feasibility and effectiveness of the data-knowledge-driven approach in seafloor topography inversion, which helps to accelerate the mapping of high-precision seafloor topography.

Key words: seafloor topography, machine learning, data-driven, knowledge-driven, gravity-density method, random forest, SIO model, shipboard bathymetry

CLC Number:

P714.7

LIU Yang, LI Sanzhong, ZOU Zhuoyan, SUO Yanhui, SUN Yi. High-precision seafloor topographic mapping based on data-knowledge-driven: An example from the South China Sea[J]. Journal of Marine Sciences, 2024, 42(3): 142-152.

Figures/Tables 10

Fig.1 Distribution of shipboard bathymetric data (The blue and orange points represent control and check points, respec-tively, with the bathymetric model of SIOv25.1 in the background.)

Fig.2 Distribution of geophysical data in the study area

Tab.1 Statistical analysis of geophysical data in the study area

特征参数	最大值	最小值	平均值	标准差
自由空气重力异常/mGal	207.90	-60.64	4.87	19.66
垂直重力梯度/E	281.23	-114.34	0.34	19.97
均衡重力异常/mGal	80.38	-97.52	14.13	18.56
磁异常/nT	217.55	-172.32	5.95	43.39

Fig.3 Schematic diagram of the principle of gravity-density method

Fig.4 Correlation coefficients and standard deviations between gravity-density method and shipboard bathymetry for different density difference constants

Tab.2 Optimal parameters for random forests based on two driving methods

参数	最优值
参数	数据驱动	数据-知识驱动
n_eatimators	1 000	1 000
max_depth	60	50
max_features	4	3
min_samples_leaf	1	2
min_samples_split	2	2

Fig.5 Seafloor topography of the study area by four models

Tab.3 Evaluation indexes for the four models

评估指标	SIO	重力-密度法	随机森林	数据-知识驱动
平均绝对误差/m	83.18±138.66	24.86±51.85	24.51±62.02	19.65±49.59
平均相对误差/%	3.47±7.17	1.09±4.07	1.03±4.09	0.82±3.81
均方根误差/m	161.69	57.50	66.68	53.34
相关系数	0.990 5	0.998 8	0.998 4	0.999 0

Fig.6 Comparison of shipboard bathymetry and inversion depths by four models

Fig.7 Histogram of the difference statistics among the inversion results of the four models and shipboard bathymetry

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