| Issue |
E3S Web Conf.
Volume 192, 2020
VIII International Scientific Conference “Problems of Complex Development of Georesources” (PCDG 2020)
|
|
|---|---|---|
| Article Number | 04007 | |
| Number of page(s) | 6 | |
| Section | Innovative Technologies and Methods for Monitoring Natural and Mining Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202019204007 | |
| Published online | 30 September 2020 | |
Prediction of the spatial variability of coal-bearing rocks at the Elginsky coal mine
1
Technical Institute (branch) of the M.K. Ammosov North-Eastern Federal University, 16 Kravchenko str., Neryungri, Republic of Sakha (Yakutia), 678960, Russia
2
State Key Laboratory of Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
3
School of Civil Engineering & Institute of Cold Regions Engineering, Science and Technology, Northeast Forestry University, Harbin 150040, China.
4
Northeast-China Observatory and Research-Station of Permafrost Geo-Environment, Ministry of Education, Northeast Forestry University, Harbin 150040, China
5
Academy of Sciences of the Republic of Sakha (Yakutia), 677007, Russia
* Corresponding author: alkor.05@mail.ru
The article presents a brief analysis of the key methods used for spatial modelling of mining and geological indicators describing the composition, structure and state of rock deposits. The main limitations of the analysed methods when applied under real conditions are outlined. It is proposed to overcome these limitations using Markov nonlinear algorithms. By applying the principles of multi-dimensional Markov modelling to a geological object, interval types were determined for modelling mining and geological parameters of the Elginsky coal mine. As an example, the article presents the results of predicting the ash content for the U5 section of the Elginsky coal mine on the basis of one of the cross-sections of the developed three-dimensional model.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.