Abstract:Coal and gas outburst is one of main dynamic disasters of coal mine
in China. As latest studies, the development of tectonic coal is one
of essential preconditions for coal and gas outburst, and an
accurate thickness prediction of this coal will play a positive role
in safe mining. Unfortunately, few researches on quantitative
prediction of tectonic coal thickness, especially the methodology of
seismic attributes, have been carried out. The technology of seismic
attributes is one of the most important methodologies which can
reveal the difference between normal coal and tectonic coal from
earth surface, but there are too many factors which can affect
seismic attributes. Therefore, the relationship between tectonic
coal thickness and seismic attributes is non-linear. FNN (Fuzzy
Neural Network) has been used in many industries for its
characteristic of simple structure, strong approximation ability and
non-linear processing ability. We combined FNN and seismic
attributes together to quantitatively predict the thickness of
tectonic coal in coal bed. In practical forecasting, first of all,
we tested and obtained main parameters of FNN model and seismic
attributes using forward synthetic data. Then, we extracted and
regenerated a new training set of true seismic attributes such as 90
Hz spectral decomposition and sweetness from wells' nearby traces.
Through retraining of this new set, we built a practicable
prediction model of tectonic coal thickness, and used it to predict
tectonic coal thickness in research area. By compared to true values
uncovered by wells and geological disciplines, the prediction's
reliability is high and the prediction's errors meet actual
requirements of coal mining.
Xin Wang;Tongjun Chen. Quantitative Prediction of Tectonic Coal Thickness Based on FNN and Seismic Attributes[J]. , 2014, 11(11): 3653-3662.
Xin Wang;Tongjun Chen. Quantitative Prediction of Tectonic Coal Thickness Based on FNN and Seismic Attributes. , 2014, 11(11): 3653-3662.