Kernel Density Estimation Method of Elastic Parameters Based on Variational Mode Decomposition
Probability density modelling is a crucial aspect of seismic stochastic simulation,and probability density estimation of the high frequency components of the elastic parameters determines the accuracy of high resolution seismic stochastic simulation results.To address inaccuracies in extracting high frequency components of elastic parameters,excessive constraints in a priori conditions for probability density modelling,and hierarchical design in conventional methods,an elastic parameter kernel density estimation method based on variational mode decomposition(VMD)were proposed.Firstly,the VMD method was employed to conduct modal decomposition on the logging elastic parameter data,followed by the selection of high frequency terms within the intrinsic mode function(IMF)in order to acquire the high frequency component of the logging elastic parameter.Then the probability density model of high frequency components was obtained by using kernel density estimation hierarchical computation,and the model was used to generate random high-frequency components by random sampling to be superimposed on the seismic data next to the wells in order to enrich the high-frequency content of the seismic elasticity parameter data.The experimental results in Well 34 of the Pearl River Mouth Basin show that VMD effectively separates the high frequency components of the logging elastic parameters with a central frequency above 70 Hz.The kernel density estimation method with layered design highlights the statistical law of the high frequency components.After superimposing the random high frequency components,the high frequency components of the seismic elastic parameters above 70 Hz are obviously supplemented.This method provides a new idea for high-resolution stochastic earthquake simulation.
high resolutionseismic dataelastic parametervariational mode decompositionkernel density estimation
万方数据
维普
