地震多参数BP神经网络预测煤层厚度
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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
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.
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.
引文
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[3] 窦易升.薄层厚度定量解释的振幅谱方法研究[J].石油物探信息,1994.
[4] 胡强,刘福贵,冯德永等.利用振幅、频率信息综合反演储层厚度[J].石油地球物理勘探,1995,30(增刊1):96—101.
[5] 中国煤田地质总局岩性勘探课题组.煤田地球物理岩性勘探技术文集[D].北京:煤炭工业出版社,1996.
[6] 邵治龙,尹成,黄德济等.用多参量法计算薄层和薄互层厚度[J].石油物探,1997,36(4):92—98.
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