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Proceedings Paper

A new practical methodology of the coal bed stability evaluation: the trend and variation method
Author(s): Yingchun Wei; Daiyong Cao; Juemei Deng
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Paper Abstract

The coal bed stability classification is correct or not, which relates to directly the geological exploration type of coal resources and the correct assessment of the reasonable development and utilization value. Therefore, the study of the coal bed stability is very important. Based on the knowledge that the coal thickness change has the regional change and the partial change in the spatial distribution, and the disadvantages of the quantitative mathematical statistics methods, this article presented the new method of trend surface analysis and mathematical statistics combination, that is, the trend and variation method, to the quantitative evaluating coal bed stability, established the indicator system, including the minable index, the mean, the Coefficient of variation, the trend surface times, the trend surface fitting of the coal bed thickness, and the standard of the coal bed stability types. Took No.2 coal bed of Baie detailed exploration area in Shanxi as an application example, and it proved that the trend and variation method has obvious advantages on the coal bed stability evaluation, with making detailed contrasts and analysis of the results, which are evaluated respectively by the newly presented the trend and variation method and the traditional mathematical statistics method. The trend and variation method can reflect the discrete degree of the different coal bed thickness change and the spatial distribution and relativity of these coal thickness points value.

Paper Details

Date Published: 16 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74924J (16 October 2009); doi: 10.1117/12.837308
Show Author Affiliations
Yingchun Wei, China Univ. of Mining and Technology (China)
Daiyong Cao, China Univ. of Mining and Technology (China)
Juemei Deng, China Univ. of Mining and Technology (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining
Yaolin Liu; Xinming Tang, Editor(s)

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