Geologic body modeling method and device based on sliding Kriging interpolation

Abstract

The invention discloses a geologic body modeling method and device based on sliding Kriging interpolation, and the method comprises the steps: obtaining geological data which is four-dimensional data combining attribute data and spatial data; taking each group of four-dimensional data as an initial sampling point, and calculating a depth value according to the three-dimensional coordinates of the initial sampling point; sorting and grouping all numerical point coordinates to obtain a variable range dist; dividing grids according to the variable range dist and determining the grid to which each sampling point belongs; obtaining an estimated value of a to-be-interpolated point according to a Kriging equation, and removing interpolation points with the depth smaller than 0 to obtain an interpolation point and sampling point set; converting depth values of the interpolation points and the sampling point set into elevation values; and constructing a triangulation network based on the interpolation points and the sampling points to obtain a geological curved surface model with attribute data. According to the method, the data interpolation speed and precision are improved, and the geologic body modeling effect is improved.

Description

technical field [0001] The invention belongs to the technical field of computer graphics processing, and in particular relates to a geological body modeling method and device. Background technique [0002] Geological body modeling is a method that relies on geological exploration data and displays it on the computer through computer graphics and computer theory to provide solutions and strategies for geological exploration engineering. [0003] In the process of geological exploration data collection, sampling collection and collection at a certain distance are usually used, but it is not possible to collect data for each spatial location point, and the spatial data obtained by sampling presents a discrete distribution. The amount of data required for geological modeling cannot be met. Therefore, it is necessary to use the spatial interpolation method to perform interpolation estimation for each spatial position, so as to achieve the effect of encrypting spatial data and no...

AI Technical Summary

Problems solved by technology

However, practice has proved that when the 3D spatial data is added to the attribute data to perform interpolation of the 4D spatial data volume, the method has the following problems: First, because the attribute data volume is usually very large, the range value of the initial Kriging interpolation There are difficulties in the calculation, the interpolation operation cannot be completed, and the estimated value of each spatial position cannot be obtained during modeling

Second, sampling points tend to be discretely distributed and unevenly distributed

When the spatial position and attribute values ​​interpolated by the original Kriging interpolation method are unevenly distributed, the interpolation results obtained by this method are inaccurate, and the modeling effect and the actual modeling requirements have a large error

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