一种基于地质模型的电性与速度界面不一致的联合反演方法
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摘要
当电性与速度界面不一致进行联合反演时,存在参与联合反演的数据不同源的问题,如果做界面一致的假设,会降低反演的可靠性,建立电性与速度统一的反演模型是解决问题的关键。文章分析了地球物理勘探各类目标之间的相互关系,建立以地质模型作为速度和电性联合反演的统一模型,根据地质模型与物性模型、以及地球物理场分布特征的关系,模拟实际地质、地球物理状况,采用基于模式识别的联合反演算法,以直流电测深与地震数据为例进行了模拟实验,实验结果表明:以地质模型作为速度与电性不一致的联合反演统一模型是可行的。
If a joint inversion has been processed when electricity and velocity boundary are inconsistent,there should be a problem of different sources of the data which have been enlisted in joint inversion;supposing the boundary is consistent with the electricity,the reliability of the joint inversion should be reduced.Inhence,the key to solve the problem is to set up an inversion model with accord between electricity and velocity.It has analyzed the correlation among every objects of geophysical prospecting,and built up a geological model as an uniform model for joint inversion of velocity and electricity.According to the relationship among geological model,physical properties model,and the distributing characteristics of geophysical field,it has carried on simulating experiments through simulating practical geological and geophysical status,adopting the joint inversion calculation based on model discrimination,as well as taking D.C.sounding and earthquake data as examples,and the results have shown that it is a feasible method to take geological model as an uniform model for the joint inversion when electricity and velocity are inconsistent.
If a joint inversion has been processed when electricity and velocity boundary are inconsistent,there should be a problem of different sources of the data which have been enlisted in joint inversion;supposing the boundary is consistent with the electricity,the reliability of the joint inversion should be reduced.Inhence,the key to solve the problem is to set up an inversion model with accord between electricity and velocity.It has analyzed the correlation among every objects of geophysical prospecting,and built up a geological model as an uniform model for joint inversion of velocity and electricity.According to the relationship among geological model,physical properties model,and the distributing characteristics of geophysical field,it has carried on simulating experiments through simulating practical geological and geophysical status,adopting the joint inversion calculation based on model discrimination,as well as taking D.C.sounding and earthquake data as examples,and the results have shown that it is a feasible method to take geological model as an uniform model for the joint inversion when electricity and velocity are inconsistent.
引文
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[4]M.A.Meju,S.L.Fontes,M.F.B.Oliveira,J.P.R.Wma,et al.Regional aquifer mapping using combined VES-TEM-AMT/EMAP methods in the semiarid eastern margin of ParnaibaBasin,Brazil[J].Geophysics,1999,64(2):337-356.
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[6]Bimalendu B,Shalivahan and Mrinal K.Use of VFSA forresolution,sensitivity and uncertainty analysis in 1D DCresistivity and IP inversion[J].Geophysical prospecting,2003,51(5):393-408.
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[8]娄建武,龙源,等.基于小波包技术的爆破地震波特征提取及预报[J].爆炸与冲击,2004,24(3):261-267.
[9]Zhiyi zhang,Zhiqiang Zhou.Real-time quasi-2-D inversion ofarray resistitivty logging data neural network[J].Geophysics,2003,67(2):517-524.
[10]敬荣中,鲍光淑,林剑,等.一种基于数据融合的地球物理数据联合反演方法——以VES和MT为例[J].地球物理学报,2004,47(1):143~150.
[11]JoséManuel Cano Izquierdo,Yannis A.Dimitriadis,et.al.Learning from Noisy Information in FasART and FasBackNeuro-Fuzzy Systems[J].Neural Networks,2001,14(5):407-425.
[2]杨辉,王家林,吴健生,等.大地电磁与地震资料仿真退火约束联合反演[J].地球物理学报,2002,45(5):723-734.
[3]刘光鼎.论地球科学[J].地学前缘,1998,5(1-2):1-8.
[4]M.A.Meju,S.L.Fontes,M.F.B.Oliveira,J.P.R.Wma,et al.Regional aquifer mapping using combined VES-TEM-AMT/EMAP methods in the semiarid eastern margin of ParnaibaBasin,Brazil[J].Geophysics,1999,64(2):337-356.
[5]Gouviea W.P.Scale J.A.Bayesian seismic waveform in version:parameter estimation and uncertainty analysis[J].Journal ofGeophysics research,1998,103,2759-2779.
[6]Bimalendu B,Shalivahan and Mrinal K.Use of VFSA forresolution,sensitivity and uncertainty analysis in 1D DCresistivity and IP inversion[J].Geophysical prospecting,2003,51(5):393-408.
[7]Art Raiche.A pattern recognition approach geophysicalinversion using neural nets[J].Geophysics.J.Int.1991,105,629-648.
[8]娄建武,龙源,等.基于小波包技术的爆破地震波特征提取及预报[J].爆炸与冲击,2004,24(3):261-267.
[9]Zhiyi zhang,Zhiqiang Zhou.Real-time quasi-2-D inversion ofarray resistitivty logging data neural network[J].Geophysics,2003,67(2):517-524.
[10]敬荣中,鲍光淑,林剑,等.一种基于数据融合的地球物理数据联合反演方法——以VES和MT为例[J].地球物理学报,2004,47(1):143~150.
[11]JoséManuel Cano Izquierdo,Yannis A.Dimitriadis,et.al.Learning from Noisy Information in FasART and FasBackNeuro-Fuzzy Systems[J].Neural Networks,2001,14(5):407-425.
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