A program for bathymetry prediction from vertical gravity gradient anomalies and ship soundings

Hu, Minzhang; Li, Jiancheng; Li, Hui; Shen, Chongyang; Xing, Lelin

doi:10.1007/s12517-014-1570-0

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…In the Atlantic study region, a maximum error STD of 122.92 m was obtained after omitting 𝐴𝐴 𝐴𝐴𝑦𝑦𝑦𝑦 ; this makes it the most influential gravity gradient in the Atlantic study region. This was followed by It is worth noting that in each study region, 𝐴𝐴 𝐴𝐴𝑧𝑧𝑧𝑧 was not the most influential gravity gradient, although this gradient component has been widely used in the previous investigations (Hu et al, 2014(Hu et al, , 2015(Hu et al, , 2021. Instead, 𝐴𝐴 𝐴𝐴𝑥𝑥𝑥𝑥 contributed the largest in the inversions of the Indian and Arctic study regions; whereas in the Atlantic and Pacific study regions, 𝐴𝐴 𝐴𝐴𝑦𝑦𝑦𝑦 contributed the most.…”

Section: The Influence Of Each Gravity Gradient Component In the Bpnn...mentioning

confidence: 91%

“…(2021). Indeed, altimetry satellite observations can also be used to derive gravity gradient products, which are very beneficial to geoscientists (ESA, 1999), such as for tectonic structure research (Sandwell et al., 2014), bathymetry inversion (Hu et al., 2015; Wan et al., 2019; Y. M. Wang, 2000) and underwater navigation (Wan & Yu, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

2023

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Altimetry satellites have played significant roles in Earth science, and one of their main applications is marine gravimetry. Apart from the marine geoid, the main geodetic end-product from altimetry satellites has been marine gravity anomaly. Several institutes and universities have published very highly accurate marine gravity anomaly products, such as: Andersen and Knudsen (2019), Sandwell et al. (2019), andAndersen et al. (2021). Indeed, altimetry satellite observations can also be used to derive gravity gradient products, which are very beneficial to

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Section: The Influence Of Each Gravity Gradient Component In the Bpnn...mentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

2023

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“…However, it has not been experimented using actual gravity gradient data. Hu et al (2015) used vertical gravity gradients to predict the bathymetry over a part of the North Pacific and the accuracy can be improved by combing gravity gradients and gravity anomalies, compared to the bathymetry inversion only based on gravity anomalies. Fan et al (2021) proposed a nonlinear iterative least square method for seafloor topography by combining gravity anomalies and gravity gradients.…”

Section: Introductionmentioning

confidence: 99%

Bathymetry Inversion Using Vertical Deflections: a Case Study in South China Sea

Wan¹,

Liu²,

Sui³

et al. 2021

Preprint

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As an alternative method, an algorithm for bathymetry inversion using vertical deflections is proposed. Firstly, the formulas for the bathymetry inversion from north and east components of vertical deflections are derived and the data processing is introduced. Then a local area in the South China Sea is selected as an example to experiment the method. The bathymetry inversion based on gravity anomaly was also conducted for a comparison. The results show that the bathymetry derived from the north component of the vertical deflections have almost the same accuracy as that derived from gravity anomalies and the results derived from the east component have the poorest accuracy. The experiment’s results also show that accuracy of the derived bathymetry can be improved if the fitting parameters are adjusted according to the water depths. In summary, among the gravity field products used in this study, although the gravity anomaly yielded the best performance in the bathymetry inversion, the vertical defections can still be used as supplements, especially in areas where accurate vertical deflections exist. This is because deriving gravity anomaly from altimetry observations needs additional data and calculation efforts.

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“…Currently, ST inversion methods using satellite altimetry gravity data can be generally divided into space domain and spectral-domain approaches. ST inversion in the frequency domain is mainly based on the relationship between the mass body and gravity anomaly [7], such as regression analysis [8][9][10][11][12][13], admittance function method [14][15][16], simulated annealing method [17,18]. The admittance function method and regression analysis ignore the influence of nonlinear ST on the inversion results.…”

Section: Introductionmentioning

confidence: 99%

Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method

Fan

et al. 2020

Remote Sensing

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Currently, seafloor topography inversion based on satellite altimetry gravity data provides the principal means to predict the global seafloor topography. Researchers often use sea surface geoid height or gravity anomaly to predict sea depth in the space domain. In this paper, a comprehensive discussion on seafloor topography inversion formulas in the space domain is presented using sea surface geoid height, gravity anomaly and introduces an approach that uses vertical gravity gradient. This would be the first study to estimate seafloor topography by vertical gravity gradient in the space domain. Further, a nonlinear iterative least-square inversion process is discussed. Using the search area for the Malaysia Airlines Flight MH370 as study site, we used the DTU17 gravity anomaly model and SIO V29.1 vertical gravity gradient to generate the seafloor topography. The results of the proposed bathymetric models were analyzed and compared with the DTU18 and SIO V20.1 bathymetric models. The experimental results show that the gravity anomaly and vertical gravity gradient in the study area are strongly correlated with the seafloor topography in the 20–200 km wavelength range. The optimal initial iteration values for seafloor topography variance and correlation length are 0.6365 km2 and 10.5′, respectively. Shipborne measurements from SONAR data were used as external checkpoints to evaluate the bathymetric models. The results show that the RMS for BAT_VGG_ILS (inversion model constructed by vertical gravity gradient) is smaller than for BAT_GA_ILS (inversion model constructed by gravity anomaly) and BAT_GA_VGG_ILS (inversion model constructed by gravity anomaly and vertical gravity gradient). The relative accuracy of the DTU18 bathymetry model was 9.27%, while the relative accuracy of the proposed seafloor models was higher than 4%. Within the 200 m difference range, the proportion of checkpoints for BAT_VGG_ILS was close to 95%, about 80% for BAT_GA_ILS and BAT_GA_VGG_ILS, and less than 50% for the DTU18. The results show that the nonlinear iterative least square method in the space domain is feasible.

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A program for bathymetry prediction from vertical gravity gradient anomalies and ship soundings

Cited by 14 publications

References 8 publications

Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

Altimetry‐Derived Gravity Gradients Using Spectral Method and Their Performance in Bathymetry Inversion Using Back‐Propagation Neural Network

Bathymetry Inversion Using Vertical Deflections: a Case Study in South China Sea

Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method

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