Improved Bathymetric Prediction Using Geological Information: SYNBATH

Sandwell, David T.; Goff, John A.; Gevorgian, Julie; Harper, H.; Kim, Seung‐Sep; Yu, Yao; Tozer, B.; Wessel, Paul; Smith, Walter H. F.

doi:10.1029/2021ea002069

Cited by 30 publications

(11 citation statements)

References 47 publications

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“…The maximum absolute slope of the best-fit model is in good agreement with a previous study based on the analysis of 88 seamounts where the seamount height was one fifth of the basal radius (Smith, 1988). The final ratio between sigma and height, σ /h, with a value of about 2.4 is important in defining the final model, and ultimately, the gravity field of the Gaussian seamount that is used to construct a new global synthetic bathymetry (SYNBATH) where this factor is used to sharpen the shapes of predicted seamounts (Sandwell et al, 2022).…”

Section: Methods 1: Average Gaussian Fitsupporting

confidence: 86%

Global Distribution and Morphology of Small Seamounts

Gevorgian

Sandwell

et al. 2022

Preprint

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Seamounts are isolated elevations in the seafloor with circular or elliptical plans, comparatively steep slopes, and relatively small summit areas (Menard, 1964). The vertical gravity gradient (VGG), which is the curvature of the ocean surface topography derived from satellite altimeter measurements, has been used to map the global distribution of seamounts (Kim and Wessel, 2011). We used the latest grid of VGG to update and refine the global seamount catalog; we identified 10,796 new seamounts, expanding the catalog by 1/3. 739 well-surveyed seamounts, having heights ranging from 421 m to 2500 m, were then used to estimate the typical radiallysymmetric seamount morphology. First, an Empirical Orthogonal Function (EOF) analysis was used to demonstrate that these small seamounts have a basal radius that is linearly related to ix their height -their shapes are scale invariant. Two methods were then used to compute this characteristic base to height ratio: an average Gaussian fit to the stack of all profiles and an individual Gaussian fit for each seamount in the sample. The first method combined the radial normalized height data from all 739 seamounts to form median and median-absolute deviation. These data were fitted by a 3-parameter Gaussian model that explained 99.82 percent of the variance. The second method used the Gaussian function to individually model each seamount in the sample and further establish the Gaussian model. Using this characteristic Gaussian shape, we show that VGG can be used to estimate the height of small seamounts to an accuracy of about 270 m.x

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Section: Methods 1: Average Gaussian Fitsupporting

confidence: 86%

Global Distribution and Morphology of Small Seamounts

Gevorgian

Sandwell

et al. 2022

Preprint

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“…The seabed is, however, poorly mapped at the sub‐kilometer resolution needed to characterize abyssal hill statistics; acoustic bathymetry only covers ∼20% of the seafloor at 400 m resolution (Sandwell et al., 2022; https://seabed2030.org/mapping-progress). Outside of acoustic constraints, bathymetry is determined through downward continuation of satellite altimeter‐based gravity which, in its latest iteration, is resolved to 6 km half wavelength (Tozer et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Regional Variations in the Spreading‐Rate Dependence of Abyssal Hill Roughness as Indicators of Mantle Heterogeneity

Goff

2023

Geophysical Research Letters

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Abyssal hills are potential proxy records of mid‐ocean ridge faulting and volcanism, as well as the spreading rates and mantle properties that influence these processes. Satellite gravity‐based global prediction of abyssal hill root‐mean‐square (RMS) heights could provide such a proxy, but are broadly influenced off‐axis by pelagic sediment cover, which can lower their values by preferentially filling lows. Here I formulate a sediment‐corrected RMS height prediction by estimating an empirical relationship between mean RMS and sediment thickness as a function of spreading rate. I utilize these values to investigate regional variations in the spreading‐rate dependence of mean RMS across eight regions world‐wide, focusing on half spreading rates <40 mm/yr where spreading rate dependence is strongest. I find that regional variations in this relationship are significant, likely indicating heterogeneity in mantle properties between the different regions.

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“…The sensitivity matrix is simplified to the diagonal matrix of the Bouguer plate approximation (Silva et al, 2014;Uieda & Barbosa, 2017), this simplification cannot work for VGG. More and more studies have pointed out the superiority of VGG inversion for ST (Sandwell et al, 2022;Wan et al, 2019;Y. M. Wang, 2000;Yang et al, 2018).…”

Section: Differences From Existing Methodsmentioning

confidence: 99%

“…A remove-interpolate-restore approach was used multiple times throughout the workflow to add a new data layer onto existing models. This operation is called polish and details can be referred to Tozer et al (2019) and Sandwell et al (2022). In addition, it is important to note that the observed GA not only contains the topographic effect but also the nontopographic effect, for example, the variations of crustal thickness and sedimentary basins (Jordan et al, 2020;Yang et al, 2020).…”

Section: Algorithm Implementationmentioning

confidence: 99%

An Adaptive Nonlinear Iterative Method for Predicting Seafloor Topography From Altimetry‐Derived Gravity Data

Jian

et al. 2023

JGR Solid Earth

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The ocean covers 71% of the Earth's surface. At present, only about 20% of the seafloor topography (ST) has been directly measured by ships, and most areas are predicted from satellite altimetry‐derived gravity products. In this study, an adaptive nonlinear iterative (ANI) method is proposed to address two major problems in gravity ST inversion: linear approximation and empirical seafloor density contrast (SDC). In ANI, the SDC is adaptively estimated as an output, while higher‐order Parker expansion and modified Bott's iteration are combined to recover nonlinear topography. We apply our new method using the DTU21GRA altimetric gravity model and single‐beam bathymetry to predict the ST in a part of the South China Sea. Results reveal that the average SDC in the study area is 1.24 g/cm3, which compares well to CRUST1.0. The root‐mean‐square (RMS) error between the nonlinear model and single‐beam checkpoints is 102.1 m, which is improved by 34.5%, 29.2%, and 18.3% compared with the non‐gravity model, topo_24.1, and linear model, respectively. The RMS error between the nonlinear model and multibeam bathymetry is 91.0 m, which is better than the linear model. Analysis of two‐dimensional profiles shows that the nonlinear model reveals more terrain details than the linear model.

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Improved Bathymetric Prediction Using Geological Information: SYNBATH

Cited by 30 publications

References 47 publications

Global Distribution and Morphology of Small Seamounts

Global Distribution and Morphology of Small Seamounts

Regional Variations in the Spreading‐Rate Dependence of Abyssal Hill Roughness as Indicators of Mantle Heterogeneity

An Adaptive Nonlinear Iterative Method for Predicting Seafloor Topography From Altimetry‐Derived Gravity Data

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