1. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang, Jiangxi 330013, China; 2. School of Geophysics and Measurement-control Technology, East China University of Technology, Nanchang Jiangxi 330013, China; 3. Geological Exploration Technology Institute of Anhui Province, Hefei, Anhui 230041, China
Abstract：Joint inversion based on a non-linear optimization algorithm has the advantages of global optimization, no need to calculate partial derivatives, and convenience for the integration of prior information. As a novel non-linear optimization algorithm, the artificial bee colony (ABC) algorithm has a unique role transformation mechanism and strong performance in solving optimization problems. However, it also has some shortcomings such as low search efficiency and weak local search ability. Considering that a dual-population framework is capable of improving the global optimization performance of an optimization algorithm, we propose an ABC algorithm with a dual-population framework, in which crossover and mutation operations and neighborhood search for the optimal solution are integrated into different populations. Typical test functions are selected to verify the effectiveness of the improved ABC algorithm. Besides, the algorithm is applied into the joint inversion of magnetotelluric (MT) and gravity data. Model tests and real data feedback show that the dual-population ABC algorithm has high global optimization capability and certain practicability.
于鹏, 王家林, 吴健生, 等.地球物理联合反演的研究现状和分析[J]. 勘探地球物理进展, 2006, 29(2):87-92.YU Peng, WANG Jialin, WU Jiansheng, et al. Review and discussions on geophysical joint inversion[J]. Progress in Exploration Geophysics, 2006, 29(2):87-92.
[3]
相鹏, 王金铎, 谭绍泉, 等.一种变密度-速度关系的重力与地震同步联合反演方法[J]. 石油地球物理勘探, 2020, 55(3):686-693.XIANG Peng, WANG Jinduo, TAN Shaoquan, et al. A combined inversion method of gravity and seismic synchronization with variable density-velocity relationship[J]. Oil Geophysical Prospecting, 2020, 55(3):686-693.
[4]
彭国民, 刘展.电磁和地震联合反演研究现状及发展趋势[J]. 石油地球物理勘探, 2020, 55(2):465-474.PENG Guomin, LIU Zhan. The current status and development trend of joint inversion of electromagne-tic and seismic[J]. Oil Geophysical Prospecting, 2020, 55(2):465-474.
[5]
陈晓, 于鹏, 邓居智, 等.基于宽范围岩石物性约束的大地电磁和地震联合反演[J]. 地球物理学报, 2016, 59(12):4690-4700.CHEN Xiao, YU Peng, DENG Juzhi, et al. Joint inversion of MT and seismic data based on wide-range petrophysical constraints[J]. Chinese Journal of Geophysics, 2016, 59(12):4690-4700.
[6]
陈晓, 于鹏, 张罗磊, 等. 地震与大地电磁测深数据的自适应正则化同步联合反演[J]. 地球物理学报, 2011, 54(10):2673-2681.CHEN Xiao, YU Peng, ZHANG Luolei, et al. Adaptive regularized synchronous joint inversion of MT and seismic data[J]. Chinese Journal of Geophysics, 2011, 54(10):2673-2681.
[7]
于鹏, 王家林, 吴健生, 等. 重力与地震资料的模拟退火约束联合反演[J]. 地球物理学报, 2007, 50(2):529-538.YU Peng, WANG Jialin, WU Jiansheng, et al. Constrained joint inversion of gravity and seismic data using the simulated annealing algorithm[J]. Chinese Journal of Geophysics, 2007, 50(2):529-538.
[8]
过仲阳, 王家林, 吴健生. 应用遗传算法联合反演地震-大地电磁测深数据[J]. 石油物探, 1999, 38(1):3-5.GUO Zhongyang, WANG Jialin, WU Jiansheng. Joint inversion of seismic and magnetotelluric sounding data using genetic algorithm[J]. Geophysical Prospecting for Petroleum, 1999, 38(1):3-5.
[9]
万玲, 林婷婷, 林君, 等. 基于自适应遗传算法的MRS-TEM联合反演方法研究[J]. 地球物理学报, 2013, 56(11):3728-3740.WAN Ling, LIN Tingting, LIN Jun, et al. Joint inversion of MRS and TEM data based on adaptive genetic algorithm[J]. Chinese Journal of Geophysics, 2013, 56(11):3728-3740.
[10]
扈本娜. 浅层电震非线性反演理论的研究[D].河北石家庄:石家庄经济学院, 2013.
[11]
Zeng Z W, Chen X, Liu X, et al. Joint inversion of MT and gravity based on differential evolution algorithm[C]. China International Geoelectromagnetic Workshop, 2019.
[12]
胡祖志, 石艳玲, 刘云祥, 等. 电磁与重力数据非线性约束联合反演[J]. 石油地球物理勘探, 2020, 55(1):226-232.HU Zuzhi, SHI Yanling, LIU Yunxiang, et al. Nonlinear constrained joint inversion of MT and gravity data[J]. Oil Geophysical Prospecting, 2020, 55(1):226-232.
[13]
Karaboga D. An Idea Based on Honey Bee Swarm for Numerical Optimization[R]. Erciyes University, Kayseri, 2005.
[14]
王猛, 刘国辉, 王大勇, 等. 瞬变电磁测深资料的ABC算法反演研究[J]. 地球物理学进展, 2015, 30(1):133-139.WANG Meng, LIU Guohui, WANG Dayong, et al. Application of ABC algorithm inversion of transient electromagnetic sounding files[J]. Progress in Geophysics, 2015, 30(1):133-139.
[15]
侯征, 熊盛青, 杨进, 等.基于人工蜂群算法的瑞雷波多阶模式非线性联合反演研究[J]. 地球物理学进展, 2018, 33(1):362-371.HOU Zheng, XIONG Shengqing, YANG Jin, et al. Research on nonlinear joint inversion of multimode Rayleigh wave based on artificial bee colony algorithm[J]. Progress in Geophysics, 2018, 33(1):362-371.
[16]
Wen L F, Cheng J L, Li F, et al. Global optimization of controlled source audio-frequency magnetotelluric data with an improved artificial bee colony algorithm[J]. Journal of Applied Geophysics, 2019, 170:103845.
[17]
Zhu G, Kwong S. Best-guided artificial bee colony algorithm for numerical function optimization[J]. Applied Mathematics and Computation, 2010, 217(7):3166-3173.
[18]
Banharnsakun A, Achalakul T, Sirinaovakul B. The Best-So-Far selection in artificial bee colony algorithm[J]. Applied Soft Computing, 2011, 11(2):2888-2901.
吴亮红, 王耀南, 周少武, 等. 双群体伪并行差分进化算法研究及应用[J]. 控制理论与应用, 2007, 24(3):453-458.WU Lianghong, WANG Yaonan, ZHOU Shaowu, et al. Research and application of pseudo parallel diffe-rential evolution algorithm with dual subpopulation[J]. Control Theory and Application, 2007, 24(3):453-458.
[21]
暴励, 曾建潮. 一种双种群差分蜂群算法[J]. 控制理论与应用, 2011, 28(2):266-272.BAO Li, ZENG Jianchao. A dual-population differe-ntial bee colony algorithm[J]. Control Theory and Application, 2011, 28(2):266-272.
陈亚峰, 张晓明, 曹国清, 等. 双种群协同下带混沌闪烁机制的萤火虫算法研究[J]. 西安交通大学学报, 2018, 52(3):153-159.CHEN Yafeng, ZHANG Xiaoming, CAO Guoqing, et al. Research on firefly algorithm with chaotic blinking mechanism under dual-population cooperation[J]. Journal of Xi'an Jiaotong University, 2018, 52(3):153-159.
Srinivasan D, Seow T H. Particle Swarm Inspired Evo-lutionary Algorithm(PS-EA) for Multi-Objective Optimization Problems[M]. Springer, London, 2003.
[26]
Zhdanov M S. Geophysical Inverse Theory and Regularization Problems[M]. Elsevier Science, Netherlands, 2002.
[27]
向阳, 于鹏, 陈晓, 等. 大地电磁反演中改进的自适应正则化因子选取[J]. 同济大学学报(自然科学版), 2013, 41(9):1429-1434.XIANG Yang, YU Peng, CHEN Xiao, et al. An improved adaptive regularized parameter selection in magnetotelluric inversion[J]. Journal of Tongji University (Natural Science Edition), 2013, 41(9):1429-1434.
[28]
陈晓, 张磊, 郭曼, 等. 大地电磁测深和重力数据同步正则化联合反演[J]. 科学技术与工程, 2016, 16(8):54-58.CHEN Xiao, ZHANG Lei, GUO Man, et al. Regula-rized synchronous joint inversion of magnetotelluric and gravity data[J]. Science Technology and Engineering, 2016, 16(8):54-58.
[29]
于鹏, 王家林, 吴健生. 二度半长方体组合模型的重力模拟退火反演[J]. 地球物理学报, 2007, 50(3):882-889.YU Peng, WANG Jialin, WU Jiansheng. An inversion of gravity anomalies by using a 2.5 dimensional rectangle gridded model and the simulated annealing algorithm[J]. Chinese Journal of Geophysics, 2007, 50(3):882-889.
[30]
陈晓, 于鹏, 邓居智, 等. 地球物理联合反演新框架研究[J]. 石油地球物理勘探, 2017, 52(4):851-858, 883.CEHN Xiao, YU Peng, DENG Juzhi, et al. Research on new framework of geophysical joint inversion[J]. Oil Geophysical Prospecting, 2017, 52(4):851-858, 883.
[31]
郭曼, 邓居智, 陈晓, 等. 基于岩石物性约束的大地电磁与重力贝叶斯联合反演[J]. 地球物理学进展, 2018, 33(5):1897-1902.GUO Man, DENG Juzhi, CHEN Xiao, et al. Bayesian joint inversion of magnetotelluric and gravity based on rock property constraints[J]. Progress in Geophy-sics, 2018, 33(5):1897-1902.
[32]
曾志文, 陈晓, 杨海燕, 等. 基于改进差分进化算法的大地电磁测深和重力宽范围物性约束联合反演[J]. 地球物理学报, 2020, 63(12):4565-4577.ZENG Zhiwen, CHEN Xiao, YANG Haiyan, et al. Joint inversion of magnetotelluric and gravity based on improved differential evolution algorithm and wide-range petrophysical constraints[J]. Chinese Journal of Geophysics, 2020, 63(12):4565-4577.
[33]
李华东, 于鹏, 刘振友. 基于随机分布共网格模型的重磁电震联合反演技术及应用[J]. 石油地球物理勘探, 2015, 50(4):742-748.LI Huadong, YU Peng, LIU Zhenyou. Joint inversion technology and application of gravity, magnetotelluric and seismic data based on random distributed common grid model[J]. Oil Geophysical Prospecting, 2015, 50(4):742-748.
