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IDW interpolation method for multi-parameter collaborative optimization of geoscience data

A technology of collaborative optimization and interpolation method, applied in the direction of design optimization/simulation, etc., can solve the problems of overall optimization of difficult interpolation models, without considering the influence of anisotropy on spatial proximity, etc., and achieve the best interpolation effect

Pending Publication Date: 2020-11-24
JIANGXI NORMAL UNIVERSITY
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Problems solved by technology

However, most of the current IDW interpolation algorithms only consider the optimization of a single parameter, or independently tune each parameter, making it difficult to achieve the overall optimization of the interpolation model
In addition, traditional IDW interpolation algorithms do not take into account the effect of anisotropy on spatial proximity

Method used

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  • IDW interpolation method for multi-parameter collaborative optimization of geoscience data
  • IDW interpolation method for multi-parameter collaborative optimization of geoscience data
  • IDW interpolation method for multi-parameter collaborative optimization of geoscience data

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Embodiment Construction

[0054] refer to figure 1 , an IDW interpolation method for multi-parameter collaborative optimization of geoscience data, comprising the following steps,

[0055] A. Given the initialization conditions of the particle swarm optimization algorithm, the search dimension D=4(α, λ, θ, N), α is the distance attenuation coefficient, λ is the distance adjustment parameter, θ is the anisotropy direction, and N is the number of nearest neighbors , using the method of randomly initializing the population to generate a set of solutions containing (α, λ, θ, N);

[0056] B. Calculate the fitness value of the particle swarm;

[0057] C. Obtain the individual optimal value pbest of each particle i ;

[0058] D. Obtain the global optimal value of the group;

[0059] E. Update the velocity and position of the particles;

[0060] F. If the termination condition of the particle swarm optimization algorithm is not met, return to step B, and when the termination condition of the particle swar...

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Abstract

The invention discloses an IDW interpolation method for multi-parameter collaborative optimization of geoscience data. The method comprises the steps of A, giving initialization conditions of a particle swarm algorithm, enabling the searching dimension D to be 4(alpha, lambda, theta, N), wherein alpha is a distance attenuation coefficient, lambda is a distance adjustment parameter, theta is an anisotropy direction, N is a nearest point number, and generating a group of solution sets containing (alpha, lambda, theta, N) by adopting a random population initialization method; B, calculating the fitness value of the particle swarm; C, acquiring an individual optimal value pbesti of each particle; D, obtaining a global optimal value of the group; E, updating the speed and the position of the particle; F, if the termination condition of the particle swarm algorithm is not met, returning to the step B, and when the termination condition of the particle swarm algorithm is met, stopping updating; G, outputting the optimal position; and H, substituting the optimized parameter combination solution (alpha, lambda, theta, N) into the IDW interpolation model to obtain a to-be-interpolated pointattribute value. According to the method, the defects in the prior art can be overcome, and the satisfactory solution of the IDW interpolation effect in the global sense is obtained.

Description

technical field [0001] The invention belongs to the technical field of geoscience data processing, in particular to an IDW interpolation method for multi-parameter collaborative optimization of geoscience data. Background technique [0002] In geoscience research, surface data with certain rules are usually required. However, most of the available geoscientific data are collected at stations, presenting a discrete and irregular distribution. This often requires the use of spatial interpolation technology to convert irregular discrete data into regular surface spatial data. Spatial interpolation is to establish a certain interpolation function relationship through limited discrete sampling points, and substitute known sampling points within a certain range of the point to be interpolated into the function expression to obtain the attribute value of the interpolation point. Commonly used spatial interpolation methods include kriging interpolation method, natural neighbor met...

Claims

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Application Information

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IPC IPC(8): G06F30/25
CPCG06F30/25Y02A90/10
Inventor 吴波颜金彪林珲
Owner JIANGXI NORMAL UNIVERSITY
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