Using an Iterative Algorithm to Predict Topography From Vertical Gravity Gradients and Ship Soundings

Xu, huan; Yu, Jinhai

doi:10.1029/2022ea002437

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…In this paper, the grid step length is 2 km, implying that the topography undulations within an area of 2 km×2 km are represented by the average depth, which means that the topography undulations within 2 km×2 km cannot be identified (Xu and Yu, 2022). Hopefully, the next generation of Surface Water and Ocean Topography (SWOT) satellites may revolutionize the improvement of marine gravity anomalies with a spatial resolution of 1 km (Bouman et al, 2011;Morrow et al, 2019;Yu J H et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Considering the research status quo of using gravity data to predict seafloor topography, gravity anomaly has been used as the main type of data (Calmant, 1994;Smith and Sandwell, 1997;Kim et al, 2011). Additionally, vertical gravity gradient data have also been used to a lesser extent (Hu et al, 2014;Kim and Wessel, 2016;Yang et al, 2018;Xu and Yu, 2022). The prediction methods are mainly divided into the spatial and frequency-domain methods.…”

Section: Introductionmentioning

confidence: 99%

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An iterative algorithm for predicting seafloor topography from gravity anomalies

et al. 2022

Preprint

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As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography and gravity anomaly. In actual application, it is also necessary to process the corresponding data according to some empirical methods, which can cause uncertainty in predicting topography. In this paper, we established analytical observation equations between the gravity anomaly and topography, and obtained the corresponding iterative solving method based on the least square method after linearizing the equations. Furthermore, the regularization method and piecewise bilinear interpolation function are introduced into the observation equations to effectively suppress the high-frequency effect of the boundary sea region and the low-frequency effect of the far sea region. Finally, the seafloor topography beneath a sea region (117.25°-118.25° E, 13.85°-14.85° N) in the South China Sea is predicted as an actual application, where gravity anomaly data of the study area with a resolution of 1′×1′ is from the DTU17 model. Comparing the prediction results with the data of ship soundings from the National Geophysical Data Center (NGDC), the root-mean-square (RMS) error and relative error can be up to 127.4 m and approximately 3.4%, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An iterative algorithm for predicting seafloor topography from gravity anomalies

et al. 2022

Preprint

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“…where G is the gravitational constant (6.672 × 10 −11 Nm 2 kg −2 ), ρ is the density of Ω, and (ξ, ψ, ζ) is the integral variable. Based on previous work [26,37,38], the VG and VGG generated by Ω at point P can be expressed as…”

Section: Establishment Of Observation Equationsmentioning

confidence: 99%

Comparative Study on Predicting Topography from Gravity Anomaly and Gravity Gradient Anomaly

Xu,

Tian,

et al. 2023

Remote Sensing

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Owing to the dependence of algorithms on the measurement of ship soundings and geophysical parameters, the accuracy and coverage of topography still need to be improved. Previous studies have mostly predicted topography using gravity or gravity gradient, However, there is a relative lack of integrated research combining or comparing gravity and gravity gradient. In this study, we develop observation equations to predict topography based on vertical gravity anomalies (VG; also called gravity anomalies) and vertical gravity gradient (VGG) anomalies generated by a rectangular prism. The sources of interference are divided into medium- to high-frequency errors and low-frequency errors, and these new methods reduce these errors through regularization and error equations. We also use numerical simulations to test the efficiency of the algorithm and error-reduction method. Statistics show that VGG anomalies are more sensitive to topographic fluctuations; however, the linear correlation between VG anomalies and topography is stronger. Additionally, we use the EIGEN-6C4 model of VG and VGG anomalies to predict topography in shallow and deep-sea areas, with maximum depths of 2 km and 5 km, respectively. In the shallow and deep-sea areas, the root mean square (RMS) errors of VGG anomalies prediction are 93.8 m and 233.8 m, and the corresponding accuracies improved by 7.3% and 2.3% compared with those of VG anomaly prediction, respectively. Furthermore, we use cubic spline interpolation to fuse ship soundings and improve the accuracy of the final topography results. We develop a novel analytical algorithm by constructing an observation equation system applicable to VG and VGG anomalies. This will provide new insights and directions to refine topography prediction based on VG and VGG anomalies.

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Seafloor topography inversion from multi-source marine gravity data using multi-channel convolutional neural network

Ge,

Guo,

Kong

et al. 2025

Engineering Applications of Artificial Intelligence

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Using an Iterative Algorithm to Predict Topography From Vertical Gravity Gradients and Ship Soundings

Cited by 7 publications

References 48 publications

An iterative algorithm for predicting seafloor topography from gravity anomalies

An iterative algorithm for predicting seafloor topography from gravity anomalies

Comparative Study on Predicting Topography from Gravity Anomaly and Gravity Gradient Anomaly

Seafloor topography inversion from multi-source marine gravity data using multi-channel convolutional neural network

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