A Layer-Integrated Method for 3D Density Imaging from Gravity Gradients: Application to the South China Sea

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2026
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Abstract
Summary Accurate three-dimensional (3D) density models aid dynamics and oil exploration, yet inversion in complex regions faces non-uniqueness and noise. Due to the non-uniqueness and noise sensitivity of the solution process, accurately resolving spatial density variations from gravity data remains a major challenge. Wavelet decomposition is applied to the separation of gravity signals, and inverting the processed signals can effectively reduce non-uniqueness. However, wavelet multiscale decomposition can suffer from inter-layer crosstalk and boundary blurring due to incomplete signal separation. To address this, we proposed the Layered-Integrated Density Inversion Method (LID). The process begins with a layered inversion using wavelet decomposition. This integrated model serves as the initial model in the subsequent simulated annealing inversion. Simulation experiments showed that the Root Mean Square Error (RMSE) of the LID results is 0.056 g/cm3, representing a 63.47% improvement over the traditional method, which is based on wavelet layered inversion. In noise resistance experiments, the RMSE of the LID results was 0.077 g/cm3, a 54.67% improvement over the traditional method. Applying the method to the South China Sea (SCS) using Surface Water and Ocean Topography (SWOT) satellite vertical gravity gradient data, we identified three prominent geological features: (1) traces of seafloor spreading along the mid-ocean ridge of the SCS; (2) fractures generated during subduction of SCS plate; (3) anomalous signals detected southeast of Palawan, which possibly correspond to remnants of the Paleo-Pacific plate. These findings align with existing theoretical models, providing independent evidence for rift dynamics and post-spreading crustal evolution of this passive continental margin.
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Authors Juntao Liang, Minli Li, Chuang Xu, Hangtao Yu, Chunhong Wu
Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag284
URL
https://doi.org/10.1093/gji/ggag284
Keywords Keywords not found

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