Research on interpolation error analysis of geological modeling of intelligent working face

Authors

AN Lin, Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, ChinaFollow
HAN Baoshan, Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China
LI Peng, Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China; College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
DAI Zhenhua, Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China; China Coal Research Institute, Beijing 100013, China
WANG Xinmiao, Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an 710077, China

Abstract

Working face modeling generally obtains the actual distribution of the working face through detection methods such as three-dimensional seismic, tunnel and borehole surveys, and then it establishes the corresponding model using interpolation algorithms. During the modeling process, sampling data are the foundation and interpolation is the necessary way to realize the working face model. The interpolation algorithm and the amount of sampled data affect the accuracy of the face model to varying degrees. Quantitative research on the factors affecting the accuracy of the face model will provide important reference value for the optimization of the interpolation algorithm and the amount of sampled data. On the basis of the detection data of the working face, firstly, the cross-validation method is used to calculate the interpolation errors of the contrast function interpolation, DSI interpolation and Kriging interpolation. Then, in order to solve the problem of large sampling amount of transparent working surface modeling, the relative spacing error is proposed, and the relative spacing error of the models with 13 groups of different sampling scales is calculated. The results show that: (1) The average absolute errors of DSI interpolation, Kriging interpolation and function interpolation are respectively in the process of constructing a transparent face model. They are 0.015 5, 0.022 5, and 0.231 2, so the model constructed according to the DSI interpolation algorithm has the highest accuracy, followed by the Kriging interpolation algorithm, and the function interpolation algorithm is the worst. (2) As the amount of sampled data increases, the error of the model gradually decreases. When the amount of sampled data is less than 10%, the interpolation error decreases greatly; but when the amount of sampled data is greater than 10%, the decrease tends to be gentle. It is recommended that the sample data volume could be greater than 10% for constructing the working face model. (3) In the actual construction process of the transparent working face, it is recommended to use the DSI interpolation algorithm; at the same time, sampling is carried out according to the optimal update distance and the optimal sampling interval obtained by the analysis of the minimum sampling data volume, so as to increase the local data volume of the working face.

Keywords

working face modeling, intelligent working face, interpolation algorithm, error analysis, discrete smooth interpolation

DOI

10.12363/issn.1001-1986.21.07.0368

Recommended Citation

AN Lin, HAN Baoshan, LI Peng, et al. (2022) "Research on interpolation error analysis of geological modeling of intelligent working face," Coal Geology & Exploration: Vol. 50: Iss. 6, Article 21.
DOI: 10.12363/issn.1001-1986.21.07.0368
Available at: https://cge.researchcommons.org/journal/vol50/iss6/21

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