Research and application of inversion parameters based on geological statistics inversion in transparent mines rock major modeling

Chen Baiping; Cui Fan; Liu Bo; Du Yunfei; Wang Zichang

doi:10.19606/j.cnki.jmst.2022.04.004

Volume 7 Issue 4

Aug. 2022

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Chen Baiping, Cui Fan, Liu Bo, Du Yunfei, Wang Zichang. Research and application of inversion parameters based on geological statistics inversion in transparent mines rock major modeling[J]. Journal of Mining Science and Technology, 2022, 7(4): 427-436. doi: 10.19606/j.cnki.jmst.2022.04.004

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Research and application of inversion parameters based on geological statistics inversion in transparent mines rock major modeling

doi: 10.19606/j.cnki.jmst.2022.04.004

1.
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
2.
School of Earth Science and Engineering, Hebei University of Engineering, Handan Hebei 056038, China

Received Date: 2021-11-04
Rev Recd Date: 2021-12-25
Publish Date: 2022-08-30

Abstract

Abstract

In order to provide the basic data of lithology modeling with high resolution in the early stage of coal precise mining geological guarantee, this paper established the p-wave impedance model of wedge-shaped coal seam according to the thickness and storage conditions of coal measure strata based on geostatistical inversion theory. The influence of different probability density functions and transverse range parameters on thickness inversion of coal seam are analyzed through multiple inversion experiments. The effect of inversion under different amount and location constraints is further discussed. Experimental results show that geostatistical inversion parameters have a great influence on the inversion results. Reasonable probability density function, lateral range and constrained well selection can improve the accuracy of inversion. Results shows that geostatistical inversion can predict about 1 m thin coal seam, and the error range between the inversion coal seam thickness and the logging data is 1.82 % ~12.24 %. It is therefore feasible to predict the thickness of thin coal seam, and can provide valuable modeling data for the early lithologic geological modeling of "transparent mine".
- geostatistical inversion,
- transparent mine,
- coal seam thickness prediction,
- thin coal seam

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References

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Research and application of inversion parameters based on geological statistics inversion in transparent mines rock major modeling

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