BP model of gas content prediction based on quantitative assessment of geological structure complexity

Three kinds of quantitative assessment indexes were analyzed and summarized,which were Kd(representing the fault structure complexity),Kz(representing the fold structure complexity),Kq(representing the inclined angle structure complexity).Then,the BP neural model of Pansan Mine'gas content prediction model was found based on analysis its gas-geological characteristic by selecting Kd,Kz,Kq,buried depth and bedrock depth as affecting factors.The BP neural model was convergent by learning and training of 5 470 repetitions and the model precision was greatly higher than muti-variable regression model,which shows that using quantitative assessment coefficient of the geological structure complexity to prediction gas content is feasible.