地震资料预测薄层厚度方法研究与应用
|
摘要
薄层厚度预测一直是公认的难题之一,其难度就在于如何准确地识别和提取薄层的地震属性.常规方法是利用时间域或频率域地震属性与薄层厚度的线性关系计算.但是理论与实际资料表明,不同的薄层和地层组合对地震波的动力学的信息影响很大,各种参数与薄层厚度成非线性关系,使用单一的信息不可能准确预测薄层厚度.本文利用三种线性预测原理(模型),经数学变换为属性参数,采用非线性BP网络预测薄层厚度,取得了令人满意的效果.
The prediction of thin bed thickness is one of the difficult problems generally Acknowledged.Its difficulty lies in how to exactly extract the seismic attributes of thin bed.The traditional method is to use the linear relations between seismic attributes parameter of time or frequency and the thin bed thickness.But both theoretical and practical data indicate that different thin bed and strata combinations have a great influence on the dynamic information of seismic wave.All parameters depend nonlinearly on the thin bed thickness.The thin bed thickness can not be predicted exactly by using single seismic information.This essay converts the three linearity prediction theories into attributive parameters through mathematic ways.we adopt the several parameters,and use the neural network to predict the thin bed thickness.And with application of theoretical model and practical data,it is high-precision to predict thin bed thickness.we achieve a satisfactory result.
The prediction of thin bed thickness is one of the difficult problems generally Acknowledged.Its difficulty lies in how to exactly extract the seismic attributes of thin bed.The traditional method is to use the linear relations between seismic attributes parameter of time or frequency and the thin bed thickness.But both theoretical and practical data indicate that different thin bed and strata combinations have a great influence on the dynamic information of seismic wave.All parameters depend nonlinearly on the thin bed thickness.The thin bed thickness can not be predicted exactly by using single seismic information.This essay converts the three linearity prediction theories into attributive parameters through mathematic ways.we adopt the several parameters,and use the neural network to predict the thin bed thickness.And with application of theoretical model and practical data,it is high-precision to predict thin bed thickness.we achieve a satisfactory result.
引文
[1]黄真萍,王晓华,王云专.薄层地震属性参数分析和厚度预测[J].石油物探,1997,36(3):28~38.
[2]刘天放,等.地震反演煤层厚度的谱矩法及其应用[C].煤田地球物理岩性勘探文集,岩性勘探课题组编,北京:煤炭工业出版社,1996,135~146.
[3]Krollpfeifer D.Detection and resolution of thin layers:A mod-el seismic study.Geophysical Prospecting[J].1988,36,244~264.
[4]戚敬华.由地震剖面直接反演煤层厚度的方法[C],煤田地球物理岩性勘探文集,岩性勘探课题组编,北京:煤炭工业出版社,1996,126~133.
[5]崔若飞,许东.煤层厚度的非线性反演方法[J].勘察科学技术,1999,3:60~62.
[6]仲其涛.煤层厚度反演方法研究与应用[D].硕士学位论文,徐州:中国矿业大学,2001,5.
[7]董守华,马彦良,周明.煤层厚度与振幅、频率地震属性的正演模拟[J].中国矿业大学学报,2004,33(1):29~32.
[8]董敏煜.地震勘探信号分析[M].北京:石油大学出版社,1989.
[9]李大卫,尹成,熊晓军.高阶谱混合方法地震子波估计及处理[J].地球物理学进展,2005,20(1):29~33.
[10]李月,杨宝俊,赵雪平.检测地震勘探微弱同相轴的混沌振子算法[J].地球物理学报,2005,48(6):1428~1433.
[11]杨立强,宋海斌,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1):34~37.
[12]李春峰,Chlistopher Liner.基于小波多尺度分析的奇性指数:一种新地震属性[J].地球物理学报,2005,48(4):882~888.
[2]刘天放,等.地震反演煤层厚度的谱矩法及其应用[C].煤田地球物理岩性勘探文集,岩性勘探课题组编,北京:煤炭工业出版社,1996,135~146.
[3]Krollpfeifer D.Detection and resolution of thin layers:A mod-el seismic study.Geophysical Prospecting[J].1988,36,244~264.
[4]戚敬华.由地震剖面直接反演煤层厚度的方法[C],煤田地球物理岩性勘探文集,岩性勘探课题组编,北京:煤炭工业出版社,1996,126~133.
[5]崔若飞,许东.煤层厚度的非线性反演方法[J].勘察科学技术,1999,3:60~62.
[6]仲其涛.煤层厚度反演方法研究与应用[D].硕士学位论文,徐州:中国矿业大学,2001,5.
[7]董守华,马彦良,周明.煤层厚度与振幅、频率地震属性的正演模拟[J].中国矿业大学学报,2004,33(1):29~32.
[8]董敏煜.地震勘探信号分析[M].北京:石油大学出版社,1989.
[9]李大卫,尹成,熊晓军.高阶谱混合方法地震子波估计及处理[J].地球物理学进展,2005,20(1):29~33.
[10]李月,杨宝俊,赵雪平.检测地震勘探微弱同相轴的混沌振子算法[J].地球物理学报,2005,48(6):1428~1433.
[11]杨立强,宋海斌,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1):34~37.
[12]李春峰,Chlistopher Liner.基于小波多尺度分析的奇性指数:一种新地震属性[J].地球物理学报,2005,48(4):882~888.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心