In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity model.However,due to static pressure,temperature difference and other factors,random disturbances may often f...In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity model.However,due to static pressure,temperature difference and other factors,random disturbances may often frequently in seawater bodies.The impact of such disturbances on data processing results is a topic of theoretical research.Since seawater sound velocity is a difficult physical quantity to measure,there is a need for a method that can generate models conforming to seawater characteristics.This article will combine the Munk model and Perlin noise to propose a two-dimensional dynamic seawater sound velocity model generation method,a method that can generate a dynamic,continuous,random seawater sound velocity model with some regularity at large scales.Moreover,the paper discusses the influence of the inhomogeneity characteristics of seawater on wave field propagation and imaging.The results show that the seawater sound velocity model with random disturbance will have a significant influence on the wave field simulation and imaging results.展开更多
Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and rando...Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and randomly accessible.Traditional lattice noise functions such as Perlin noise,however,exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients.In this paper,we introduce a parameterized lattice noise called prime gradient noise(PGN)that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency.PGN utilizes prime gradients,a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system.To map axial indices of lattice vertices to prime gradients,PGN employs Szudzik pairing,a bijection F:N2→N.Compositions of Szudzik pairing functions are used in higher dimensions.At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals.A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence,periodicity in the noise texture.We demonstrate the utility of the proposed noise function with several examples in procedural modeling,parameterized pattern synthesis,and solid texturing.展开更多
基金The General Program of National Natural Science Foundation of China under contract No.42074150。
文摘In the processing of conventional marine seismic data,seawater is often assumed to have a constant velocity model.However,due to static pressure,temperature difference and other factors,random disturbances may often frequently in seawater bodies.The impact of such disturbances on data processing results is a topic of theoretical research.Since seawater sound velocity is a difficult physical quantity to measure,there is a need for a method that can generate models conforming to seawater characteristics.This article will combine the Munk model and Perlin noise to propose a two-dimensional dynamic seawater sound velocity model generation method,a method that can generate a dynamic,continuous,random seawater sound velocity model with some regularity at large scales.Moreover,the paper discusses the influence of the inhomogeneity characteristics of seawater on wave field propagation and imaging.The results show that the seawater sound velocity model with random disturbance will have a significant influence on the wave field simulation and imaging results.
基金supported by the National Science and Engineering Research Council of Canada(NSERC)Discovery Grant No.2019-05092。
文摘Procedural noise functions are fundamental tools in computer graphics used for synthesizing virtual geometry and texture patterns.Ideally,a procedural noise function should be compact,aperiodic,parameterized,and randomly accessible.Traditional lattice noise functions such as Perlin noise,however,exhibit periodicity due to the axial correlation induced while hashing the lattice vertices to the gradients.In this paper,we introduce a parameterized lattice noise called prime gradient noise(PGN)that minimizes discernible periodicity in the noise while enhancing the algorithmic efficiency.PGN utilizes prime gradients,a set of random unit vectors constructed from subsets of prime numbers plotted in polar coordinate system.To map axial indices of lattice vertices to prime gradients,PGN employs Szudzik pairing,a bijection F:N2→N.Compositions of Szudzik pairing functions are used in higher dimensions.At the core of PGN is the ability to parameterize noise generation though prime sequence offsetting which facilitates the creation of fractal noise with varying levels of heterogeneity ranging from homogeneous to hybrid multifractals.A comparative spectral analysis of the proposed noise with other noises including lattice noises show that PGN significantly reduces axial correlation and hence,periodicity in the noise texture.We demonstrate the utility of the proposed noise function with several examples in procedural modeling,parameterized pattern synthesis,and solid texturing.
