【摘要】 依据煤层反射波运动学和动力学特征 ,提取出了波峰波谷振幅 A1、平均频率 Fa、主频带能量 Qf1、低频带宽能量 Qf 和峰值频率 Fmain等 5个地震特征参数。选取 8组学习样本 ,利用 4层 BP(Back Propagation)人工神经网络模型 ,采用动量法和自适应调整的改进算法 ,训练 BP网络 ,用训练好的 BP网络预测煤层厚度。经实例验证 ,地震多参数 BP网络预测煤层厚度精度高 ,是一种有效的煤厚预测方法更多还原

【Abstract】 Five seismic parameters such as amplitude of wave crest and hollow(A1),average frequency(Fa),energy in dominant frequency domain(Qf1),energy in low frequency domain(Qf),peak frequency(Fmain) are derived according to the seismic kinematics and dynamic characteristics of coal layer thickness.Eight groups of studying samples,made use of BP(Back Propagation)neural network of four layers improved by adopting momentum algorithm and self adaptive adjusting learning rate algorithm to train the BP neural network,and used the trained BP network to forecast coal layer thickness.It was proved that forecasting coal layer thickness by BP neural network from multiple seismic parameters had high accuracy by the practical data.and is an effective approach for forecasting coal layer thickness.更多还原