A Sparse Spherical Radial Basis Function Modeling Method for Local Gravity Field

Abstract

The invention discloses a sparse spherical radial basis function local gravity field modeling method, the steps are as follows: S1: collect multi-source gravity observation data; S2: remove the long and / or short-wave part in the multi-source gravity observation data; S3: Construct the spherical radial basis function model of the residual potential; S4: Determine the observation equations of each observation quantity according to the functional relationship between the gravity anomaly and the gravitational disturbance potential and the spherical radial basis function model of the residual potential; S5: Solve the parameters to be estimated in the observation equation; S6: Optimize the regularization hyperparameters, and use the parameters to be estimated corresponding to the optimized regularization hyperparameters as the parameters to be estimated after solving; S7: Calculate the parameters to be estimated after solving The parameters are substituted into the spherical radial basis function model of the residual potential, the long and / or short wave part is recovered, the disturbed gravity potential model is obtained, and other gravity field functional models are derived. The sparse model obtained by the invention is not only simpler, but also has higher precision, and can also realize automatic kernel function selection.

Description

technical field [0001] The invention relates to the technical field of local gravity field modeling, in particular to a local gravity field modeling method of a sparse spherical radial basis function. Background technique [0002] Compared with the traditional gravity field modeling method using the spherical harmonic function model, the spherical radial basis kernel function method is a flexible method with local support characteristics, especially suitable for local gravity field modeling problems using local gravity observation data , so this method has received widespread attention internationally after entering the new millennium. [0003] The flexibility of the spherical radial basis kernel function method comes from the existence of many hyperparameters, and the rationality of hyperparameter adjustment has an important impact on the final modeling effect. The most important of these hyperparameters is the location and quantity of the kernel function, that is, the pla...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: Aiming at the problem of the number and position selection of the radial basis function in the modeling of the local gravity field of the spherical radial basis function, the present invention proposes a method for modeling the local gravity field of the sparse spherical radial basis function

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