Seafloor Topography Estimation From Gravity Gradients Using Simulated Annealing

Inferring seafloor topography from gravity anomaly currently is the dominant method to obtain a global view of the oceans. Standard techniques rely on an approximate, linear relationship between topography and gravity, which is valid only if the local topography is smooth compared with the regional...

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Bibliographic Details
Published in:Journal of geophysical research. Solid earth 2018-08, Vol.123 (8), p.6958-6975
Main Authors: Yang, Junjun, Jekeli, Christopher, Liu, Lintao
Format: Article
Language:English
Subjects:
Accuracy
Altimetry
Annealing
Approximation
Computer simulation
Estimation accuracy
Geophysics
Global optimization
Gradients
Gravitation
Gravity
Gravity anomalies
gravity gradient
Inverse problems
Magnetic measurement
Methods
Ocean floor
Oceans
Optimization techniques
seafloor topography
Simulated annealing
Topography
Topography (geology)
Wavelengths
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Summary:Inferring seafloor topography from gravity anomaly currently is the dominant method to obtain a global view of the oceans. Standard techniques rely on an approximate, linear relationship between topography and gravity, which is valid only if the local topography is smooth compared with the regional topography, so the estimation accuracy in the very rugged areas is low. Current methods can be improved by removing the linear approximation and estimating the topography through simulated annealing and by using gravity gradiometry that is more sensitive to topography at short wavelengths than the gravity anomaly. Simulated annealing is a global optimization technique that can process nonlinear inverse problems. It is developed to estimate the seafloor depths by minimizing the difference between the observed and forward‐computed vertical gravity gradients. The method is tested on altimetry‐derived gravity gradients in a 2∘×2∘ area of rugged seafloor topography in the West Pacific Ocean and results in estimates with a root‐mean‐square error of ±236 m. Compared to estimates from an existing model obtained by standard techniques this represents an accuracy improvement of 22%. Key Points The linear approximation in the modeled relationship between gravity and topography is removed Estimate using gravity gradients which are more sensitive to topography at short wavelengths Estimation accuracy in rugged areas is improved by modeling the nonlinearities
ISSN:2169-9313
2169-9356
DOI:10.1029/2018JB015883