Prediction Of Subsidence Basin By The Weibull Distribution Function

Zeng, R. H.
Organization: Society for Mining, Metallurgy & Exploration
Pages: 13
Publication Date: Jan 1, 1986
Many subsidence researchers in the U. S. have developed new empirical function methods to predict subsidence, or attempted to validate some empirical functions developed by foreign researchers for use in the U. S. An attempt is made in this paper to develop a new empirical function to predict a surface subsidence basin due to longwall mining. In the U. S., the emphasis of empirical methods for the subsidence prediction over longwall panels has been in large part on determining the profile functions for the major cross-sections of a subsidence basin. In this approach, the surface monuments are usually laid out along the major cross-sections and thus, the subsidence profiles developed are restricted for subsidence prediction along the major cross-sections. However in practice many monuments or surface structures for which subsidence prediction for damage evaluation is required, are not laid out along the major cross-section of the basin. In such cases, it is difficult to get either the empirical profile function for the cross-sections where these structures are located or to predict their subsidence based on the profile function developed for the major cross-sections. Therefore, there exists a strong need to develop a subsidence basin function with a reasonable degree of accuracy, which can be determined or confirmed by the data of scattered monuments on a surface basin. The same subsidence basin function can also be used for prediction of subsidence for any arbitrary points over a longwall panel. In 1982, He (1) proposed the clastic theory, for which he developed the basic differential equations describing the movement and deformation of overburden due to underground mining, and performed the selection of best solution for the equations. He found that the Weibull dietribution is the optimal solution. The Weibull distribution has three parameters that vary to fit the subsidence profile so well that the profile function derived from it provides a high degree of accuracy in subeidence prediction. However the results
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