Abstract
The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.
Highlights
Based on the above research background, this paper proposes a data-adaptive inversedistance weighting interpolation method based on the first-order neighborhood selection strategy, which takes the spatial variability in geological attributes into account
Based on the above background, data-adaptive inverse-distance weighting interpolation method on above the first-order neighborhood selection strategy of spatial differBasedbased on the background, a data-adaptive inverse-distance weightin entiation of geological attributes was designed
The above analysis indicates that the data-adaptive inverse-distance weighting interpolation method proposed in this paper based on the first-order neighborhood selection strategy takes into account the spatial difference of geological attributes and can effectively improve the interpolation efficiency of large-scale geological modeling
Summary
Three-dimensional (3D) geological modeling is an important part of geological research and geological data visualization, as it can vividly reveal information such as the shape and structure of deep underground geological bodies, providing an important role for geological exploration, risk assessment, and so on in smart geology. The accuracy and reliability of 3D geological modeling have a direct impact on geology activities in practice. With the vigorous development of smart geology, the demand for large-scale geological modeling is growing rapidly [2]. Since the inverse-distance weighting interpolation method has been proven to perform well in terms of describing complex geological bodies in large-scale geological modeling [5], the improvements to the inverse-distance weighting interpolation method is an important research direction of spatial interpolation methods in 3D geological modeling
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