混合智能优化地震反演
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摘要
传统非线性反演方法存在着收敛效率低,有时会陷入局部极值的问题。为此,研究开发了融合粒子群优化算法和郭涛算法的混合智能优化地震非线性反演技术。粒子群优化算法对解的更新更具有目的性,收敛速度快;郭涛算法构造了多父体交叉重组算子并采用群体爬山策略,求解精度高;混合智能优化算法以粒子群优化算法为主体框架,融入郭涛算法的寻优机制。函数优化测试、理论模型试算和实际资料反演处理结果表明,混合智能优化算法具有求解效率高、全局寻优能力强的优点,适合解决复杂的地震反演问题。
The conventional non-linear seismic inversion methods are low in convergence and sometimes will result in the problem of local extremum.To solve these problems,we developed a non-linear seismic inversion of hybrid intelligent optimization with the particle warm optimization algorithm and the Guo's algorithm integrated.The particle warm algorithm is characterized by higher objective in solution upgrading and rapid convergence,while the Guo's algorithm constructs a multi-parent combination crossover and adopts a colony mountain climbing search strategy,thus is high in accuracy of solution.The hybrid intelligent algorithm uses the particle warm optimal algorithm as the framework and takes advantage of the optimization mechanism of the Guo's algorithm.We performed function optimization test,trial calculation with the theoretical model and real data inversion processing.The results show that this method has the advantages of high efficiency and strong capacity of global optimization,thus is suitable for complex seismic inversion.
The conventional non-linear seismic inversion methods are low in convergence and sometimes will result in the problem of local extremum.To solve these problems,we developed a non-linear seismic inversion of hybrid intelligent optimization with the particle warm optimization algorithm and the Guo's algorithm integrated.The particle warm algorithm is characterized by higher objective in solution upgrading and rapid convergence,while the Guo's algorithm constructs a multi-parent combination crossover and adopts a colony mountain climbing search strategy,thus is high in accuracy of solution.The hybrid intelligent algorithm uses the particle warm optimal algorithm as the framework and takes advantage of the optimization mechanism of the Guo's algorithm.We performed function optimization test,trial calculation with the theoretical model and real data inversion processing.The results show that this method has the advantages of high efficiency and strong capacity of global optimization,thus is suitable for complex seismic inversion.
引文
[1]杨文采.神经网络算法在地球物理反演中的应用[J].石油物探,1995,34(2):116-121.
[2]吴大奎,李亚林,伍志明,等.地震、测井资料联合反演的神经网络算法研究[J].天然气工业,2004,24(3):55-57.
[3]王山山,李灿平,李青仁,等.快速模拟退火地震反演[J].地球物理学报,1995,38(S1):123-134.
[4]SAMBRIDGE M,DRIJKONGEN G.Genetic algorithm in seismic waveform inversion[J].Geophysical Journal International,1992,109(2):323-342.
[5]KENNEDY J,EBERHART R C.Proceedings of IEEE international conference on neural networks:particle swarm optimization[C].IEEE Service Center,Piscat-away,NewJersey:IEEEISSN:1098-7576,1995.
[6]易远元,袁三一,黄凯,等.地震波阻抗反演的粒子群算法实现[J].石油天然气学报,2007,29(3):79-81.
[7]SHI YH,EBERHART R C.Proceedings of IEEEinter-national conference on evolutionary computation:parame-ter selectionin particle swarm optimization[C].Anchor-age,Alaska:1998.
[8]CLERC M,KENNEDY J.The particle swarm-explo-sion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[9]TRELEA I C.The particle swarm optimization algo-rithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325.
[10]吴秋波,王允诚,赵秋亮,等.混沌惯性权值调整策略的粒子群优化算法[J].计算机工程与应用,2009,45(7):49-51.
[11]ROBINSON J,SINTON S,RAHMAT S Y.Particle swarm,genetic algorithm,and their hybrids:optimiza-tion of a profiled corrugated horn antenna[J].Antennas and Propagation Society International Symposium,2002(1):314-317.
[12]GUO T,KANG L S.A new evolutionary algorithmfor function optimization[J].Wuhan University Journal of Natural Science,1999,4(4):409-414.
[13]李艳,康卓,刘溥.郭涛算法及其应用[J].武汉汽车工业大学学报,2000,22(3):101-104.
[14]贺毅朝,张翠军,王培崇,等.微粒群算法与郭涛算法在数值优化中的比较[J].计算机工程与应用,2007,43(11):100-103.
[15]贺毅朝,寇应展,陈致明.一种基于全局劣汰策略的混合粒子群优化算法[J].计算机应用研究,2007,24(8):75-78.
[2]吴大奎,李亚林,伍志明,等.地震、测井资料联合反演的神经网络算法研究[J].天然气工业,2004,24(3):55-57.
[3]王山山,李灿平,李青仁,等.快速模拟退火地震反演[J].地球物理学报,1995,38(S1):123-134.
[4]SAMBRIDGE M,DRIJKONGEN G.Genetic algorithm in seismic waveform inversion[J].Geophysical Journal International,1992,109(2):323-342.
[5]KENNEDY J,EBERHART R C.Proceedings of IEEE international conference on neural networks:particle swarm optimization[C].IEEE Service Center,Piscat-away,NewJersey:IEEEISSN:1098-7576,1995.
[6]易远元,袁三一,黄凯,等.地震波阻抗反演的粒子群算法实现[J].石油天然气学报,2007,29(3):79-81.
[7]SHI YH,EBERHART R C.Proceedings of IEEEinter-national conference on evolutionary computation:parame-ter selectionin particle swarm optimization[C].Anchor-age,Alaska:1998.
[8]CLERC M,KENNEDY J.The particle swarm-explo-sion,stability,and convergence in a multidimensional complex space[J].IEEE Transactions on Evolutionary Computation,2002,6(1):58-73.
[9]TRELEA I C.The particle swarm optimization algo-rithm:convergence analysis and parameter selection[J].Information Processing Letters,2003,85(6):317-325.
[10]吴秋波,王允诚,赵秋亮,等.混沌惯性权值调整策略的粒子群优化算法[J].计算机工程与应用,2009,45(7):49-51.
[11]ROBINSON J,SINTON S,RAHMAT S Y.Particle swarm,genetic algorithm,and their hybrids:optimiza-tion of a profiled corrugated horn antenna[J].Antennas and Propagation Society International Symposium,2002(1):314-317.
[12]GUO T,KANG L S.A new evolutionary algorithmfor function optimization[J].Wuhan University Journal of Natural Science,1999,4(4):409-414.
[13]李艳,康卓,刘溥.郭涛算法及其应用[J].武汉汽车工业大学学报,2000,22(3):101-104.
[14]贺毅朝,张翠军,王培崇,等.微粒群算法与郭涛算法在数值优化中的比较[J].计算机工程与应用,2007,43(11):100-103.
[15]贺毅朝,寇应展,陈致明.一种基于全局劣汰策略的混合粒子群优化算法[J].计算机应用研究,2007,24(8):75-78.
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