Static and Dynamic Subsidence Prediction in the Northern Appalachian Based on the Use of a Variable Subsidence Coefficient

Adamek, Vladimir
Organization: Society for Mining, Metallurgy & Exploration
Pages: 12
Publication Date: Jan 1, 1986
Due to the variability of subsidence characteristics across the U.S. coalfields, it was concluded that it would be practically impossible to develop a universal predictive model for mining-induced subsidence based on theoretical assumptions. Therefore, an effort was made to find a procedure to develop an empirical subsidence predictive model based on a sufficient Mount of field data from one mining area (in this case the Northern Appalachian Coal Region). It was also thought that this procedure, if successful, could be used as the template for developing predictive capabilities for other coalfields with different subsidence characteristics given a reasonable amount of field data. It has been found, in the Northern Appalachian Coal Region, that the variability of subsidence characteristics can be expressed by a polynomial equation developed through regression analysis of the variable subsidence coefficient and derived directly from the field data. In this study, field data were obtained from 11 Bureau longwall panel studies (16 half profiles) for static subsidence and 14 panels for dynamic subsidence. The effects of lithology, expressed in the form of a variable subsidence coefficient, have been separated for each test site by introducing a correlation between hythetically homogeneous overburden and existing ithological conditions. For each longwall panel, the characteristic of the variable subsidence coefficient was defined along individual static profiles. The definition of mean values with acceptable deviations is the substance of the predictive method. The same procedure was used to develop a predictive model for dynamic subsidence, since it war discovered that the rate of longwall face advance is not a necessary functional parameter. This paper presents the theory, development, and application of the static and dynamic subsidence prediction models.
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