Method and device for constructing geographically weighted regression model for mineral exploration

Abstract

The invention provides a method and device for constructing a geographically weighted regression model for mineral exploration, and the method comprises the steps of selecting a target sample point in a target work area to construct a plurality of ellipse screening regions, carrying out the fitting of the attribute features of the target sample point and the corresponding mineralization through employing a weighted least square method, and obtaining a plurality of to-be-determined geoweighted regression models corresponding to a target sample point; and selecting the to-be-determined geoweighted regression model corresponding to the minimum residual sum of squares as an optimal geoweighted regression model, so that by taking mineral exploration based on geochemical data of water system sediments as an example, the influence of anisotropy characteristics of various geological elements related to mineralization can be represented, the complex geological process and the spatial distribution of products of the complex geological process can be objectively reflected, and the analysis precision of mineral exploration can be improved.

Description

technical field [0001] The invention relates to events and processes with anisotropic characteristics in the technical field of mineral exploration, in particular to a method and device for constructing a geographically weighted regression model for mineral exploration. Background technique [0002] In the process of mineralization and mineral resource exploration, the Geographically Weighted Regression (GWR) analysis model (Geographically Weighted Regression, GWR) is often used to perform local weighted least squares regression on a small number of samples in a given area to estimate the local relationship between geographical variables. Reveal detailed changes in localized areas. [0003] Taking metallogenetic geological events as an example, due to the nonlinear characteristics of the metallogenic process, such as complexity, anisotropy, irreversibility, and self-similarity, the traditional geographically weighted regression model believes that sampling points at equal di...

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Problems solved by technology

[0003] Taking metallogenetic geological events as an example, due to the nonlinear characteristics of the metallogenic process, such as complexity, anisotropy, irreversibility, and self-similarity, the traditional geographically weighted regression model believes that sampling points at equal distances from the regression point have an important influence on the regression point. With the same influence, it cannot objectively reflect the complex geological process and the spatial distribution of its products, and cannot consider the anisotropy characteristics of various geological elements related to ore-forming. The analysis accuracy is low, which affects the accuracy of mineral exploration

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