The Application of a Complex Composite Fractal Interpolation Algorithm in the Seabed Terrain Simulation

Lv, Chongyang; Yu, Fashan

doi:10.1155/2018/8641471

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In early research endeavors, traditional interpolation methods such as optimal interpolation (OI), Kriging interpolation, and triangular mesh linear interpolation were extensively applied for the processing of ocean temperature and salinity data [5][6][7]. While these methods ameliorated the spatio-temporal distribution of the data, to some extent, they exhibited limitations when dealing with complex oceanic phenomena [8,9], such as the spurious information and discontinuities [10].…”

Section: Introductionmentioning

confidence: 99%

DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic

Liu,

Jia,

Zhang

2024

JMSE

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The precise interpolation of oceanic temperature and salinity is crucial for comprehending the dynamics of marine systems and the implications of global climate change. Prior neural network-based interpolation methods face constraints related to their capacity to delineate the intricate spatio-temporal patterns that are intrinsic to ocean data. This research presents an innovative approach, known as the Discretized Spatial Encoding Neural Network (DSE-NN), comprising an encoder–decoder model designed on the basis of deep supervision, network visualization, and hyperparameter optimization. Through the discretization of input latitude and longitude data into specialized vectors, the DSE-NN adeptly captures temporal trends and augments the precision of reconstruction, concurrently addressing the complexity and fragmentation characteristic of oceanic data sets. Employing the North Atlantic as a case study, this investigation shows that the DSE-NN presents enhanced interpolation accuracy in comparison with a traditional neural network. The outcomes demonstrate its quicker convergence and lower loss function values, as well as the ability of the model to reflect the spatial and temporal distribution characteristics and physical laws of temperature and salinity. This research emphasizes the potential of the DSE-NN in providing a robust tool for three-dimensional ocean temperature and salinity reconstruction.

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Section: Introductionmentioning

confidence: 99%

DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic

Liu,

Jia,

Zhang

2024

JMSE

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“…On this basis, a multi-dimensional fractal porous medium is developed by Türk et al [21] via associating the stochastic function following the statistical characteristics of FBM with the properties of porous media. As a typical stochastic fractal model, FBM has proved its outstanding value in various practical applications, including terrain reconstruction, [22] pollutant diffusion, [23] and picture processing. [24] Kikkinides et al [25] developed a binary medium generation method for generation of three-dimensional porous media based on the FBM model and validated its reliability by comparing the numerical predictions with the actual experiment for the permeability of the sandstone.…”

Section: Introductionmentioning

confidence: 99%

Numerical study on permeability characteristics of fractal porous media*

Huang¹,

Yao²,

Zhou³

et al. 2020

Chinese Phys. B

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The fractal Brownian motion is utilized to describe pore structures in porous media. A numerical model of laminar flow in porous media is developed, and the flow characteristics are comprehensively analyzed and compared with those of homogeneous porous media. Moreover, the roles of the fractal dimension and porosity in permeability are quantitatively described. The results indicate that the pore structures of porous media significantly affect their seepage behaviors. The distributions of pressure and velocity in fractal porous media are both non-uniform; the streamline is no longer straight but tortuous. When Reynolds number Re < 1, the dimensionless permeability is independent of Reynolds number, but its further increase will lead to a smaller permeability. Moreover, due to the higher connectivity and enlarged equivalent aperture of internal channel network, the augment in porosity leads to the permeability enhancement, while it is small and insensitive to porosity variation when ε < 0.6. Fractal dimension also plays a significant role in the permeability of porous media. The increase in fractal dimension leads to the enhancement in pore connectivity and a decrease in channel tortuosity, which reduces the flow resistance and improves the transport capacity of porous media.

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