基于改进蝴蝶优化算法的瑞利波频散曲线反演方法

doi:10.11720/wtyht.2024.1116

摘要
图/表
参考文献
相关文章
Metrics

全文: PDF(6312 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要

瑞利波频散曲线反演问题的多解性和反演目标函数的多极值特点,使得常规非线性优化算法可能会产生收敛不稳定、易陷入局部最优等现象。在基本蝴蝶优化算法的基础上采用动态开关概率,引入非线性自适应权重因子,既增加了算法前期的全局探索能力,也保证了后期的局部开发能力。同时,在迭代过程中对最优解进行逐维柯西变异,利用贪婪算法更新最优位置,引导种群向全局最优靠近,通过对4种常用的Benchmark函数的性能测试,表明改进的蝴蝶优化算法无论是在单峰函数还是多峰函数上的全局寻优能力明显优于遗传算法和粒子群算法。采用不同算法针对3种理论地质模型的频散曲线进行反演,发现改进蝴蝶优化算法在频散曲线含10%随机噪声的情况下仍然能够得到与理论模型更加接近的反演结果。最后将改进蝴蝶优化算法应用于实际瑞利波数据,反演结果与实际钻孔揭露的地层分布情况高度吻合。且与遗传算法和粒子群算法相比,改进蝴蝶优化算法在收敛速度、求解精度和稳定性方面都有显著的提升,具有一定的实用价值和应用前景。

	服务

	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	彭刘亚
	冯伟栋
	解惠婷
	李飞
	杨源源
	曹均锋
	任川

关键词 ：频散曲线, 改进蝴蝶优化算法, 反演, 非线性优化

Abstract：

Due to the multiplicity of solutions and the multiple extrema of the inversion objection functions,conventional nonlinear optimization algorithms are susceptible to unstable convergence and local optimum in the inversion of Rayleigh wave dispersion curves.This study improved the standard butterfly optimization algorithm by incorporating dynamic switch probability and nonlinear self-adaptive weight factors,yielding an elevated global exploration capacity in the early stage and a high local research ability in the latter stage.Furthermore,the dimension-by-dimension Cauchy mutation,along with a greedy algorithm,was employed to update the current best position during each iteration,ultimately directing the whole swarm population toward the global optimum.Tests of four commonly used benchmark functions demonstrate that the improved butterfly optimization algorithm(IBOA) outperformed other nonlinear algorithms,including the genetic algorithm and particle swarm optimization algorithm,in terms of the global research capacity of both unimodal and multimodal functions.Different algorithms were adopted for the inversion of the dispersion curves of three theoretical geological models.The results show that IBOA yielded inversion results that were closer to the models even when the dispersion curves contained 10% random noise.Finally,the IBOA was applied to actual Rayleigh wave data,and the inversion results were highly consistent with the strata revealed by drilling.Compared with the genetic algorithm and the particle swarm optimization algorithm,the IBOA significantly improved the convergence speed,as well as solution accuracy and stability.Therefore,the IBOA has a certain practical value and application prospects.

Key words： dispersion curve improved butterfly optimization algorithm inversion nonlinear optimization

收稿日期: 2023-04-22 修回日期: 2024-01-02 出版日期: 2024-06-20

ZTFLH:

P631.4

基金资助:国家自然科学基金项目(41802224);2022年安徽省重点研究与开发计划项目(2022m07020005)

作者简介: 彭刘亚(1990-),男,工程师,2014年毕业于中国矿业大学(徐州),主要从事工程地球物理勘探方面的研究工作。Email:414147651@qq.com

引用本文:

彭刘亚, 冯伟栋, 解惠婷, 李飞, 杨源源, 曹均锋, 任川. 基于改进蝴蝶优化算法的瑞利波频散曲线反演方法[J]. 物探与化探, 2024, 48(3): 705-720.
PENG Liu-Ya, FENG Wei-Dong, XIE Hui-Ting, LI Fei, YANG Yuan-Yuan, CAO Jun-Feng, REN Chuan. An improved butterfly optimization algorithm in the inversion of Rayleigh wave dispersion curve. Geophysical and Geochemical Exploration, 2024, 48(3): 705-720.

链接本文:

https://www.wutanyuhuatan.com/CN/10.11720/wtyht.2024.1116 或 https://www.wutanyuhuatan.com/CN/Y2024/V48/I3/705

Fig.1 动态开关概率

Fig.2 标准柯西分布概率密度函数

Fig.3 改进蝴蝶优化算法流程

函数名称	数学表达式	变量个数	搜索空间
Sphere	f(x)= $∑ i = 1 n x i 2$	30	[-100,100]
Schwefel 2.22	f(x)= $∑ i = 1 n x i$ + $∏ i = 1 n x i$	30	[-10,10]
Griewank	f(x)=1+ $14000 ∑ i = 1 n x i 2$ - $∏ i = 1 n$ cos( $x i i$ )	30	[-600,600]
Rastrigin	f(x)= $∑ i = 1 n x i 2 - 10 c o s (2 π x i) + 10$	30	[-5.12,5.12]

Table 1 测试函数

Fig.4 二维空间测试函数示意

Table 2 各算法优化结果

Fig.5 不同算法在测试函数上的迭代收敛特征
a—Sphere函数迭代收敛曲线;b—Schwefel 2.22函数迭代收敛曲线;c—Griewank函数迭代收敛曲线;d—Rastrigin函数迭代收敛曲线

Table 3 不同算法50次独立运算计算耗时

Table 4 理论模型参数及反演搜索范围

Fig.6 无噪声条件下IBOA算法对模型1的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Fig.7 无噪声条件下IBOA算法对模型2的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Fig.8 无噪声条件下IBOA算法对模型3的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Fig.9 10%随机噪声条件下IBOA算法对模型1的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Fig.10 10%随机噪声条件下IBOA算法对模型2的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Fig.11 10%随机噪声条件下IBOA算法对模型3的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面

Table 5 理论模型频散曲线反演结果统计

Fig.12 10%随机噪声条件下模型2反演结果分布概率直方图

Fig.13 10%随机噪声条件下不同算法对模型3的反演结果
a—频散曲线拟合情况;b—反演模型速度剖面;c—反演结果误差曲线;d—迭代收敛曲线;e—后期迭代收敛曲线(40~100次)

Fig.14 实测资料不同算法反演结果
a—最终反演结果计算得到的理论频散曲线与实测频散数据对比;b—不同算法的迭代收敛曲线;c—IBOA算法10次反演模型结果及平均值;d—最终反演模型与实际钻孔揭露地层对比

Table 6 各层反演参数搜索空间

[1]	李庆春, 邵广周, 刘金兰, 等. 瑞雷面波勘探的过去、现在和未来[J]. 地球科学与环境学报, 2006, 28(3):74-77.
[1]	Li Q C, Shao G Z, Liu J L, et al. Past,present and future of Rayleigh surface wave exploration[J]. Journal of Earth Sciences and Environment, 2006, 28(3):74-77.
[2]	夏江海, 高玲利, 潘雨迪, 等. 高频面波方法的若干新进展[J]. 地球物理学报, 2015, 58(8):2591-2605. doi: 10.6038/cjg20150801
[2]	Xia J H, Gao L L, Pan Y D, et al. New findings in high-frequency surface wave method[J]. Chinese Journal of Geophysics, 2015, 58(8):2591-2605.
[3]	Pan Y D, Gao L L, Bohlen T. High-resolution characterization of near-surface structures by surface-wave inversions:From dispersion curve to full waveform[J]. Surveys in Geophysics, 2019, 40(2):167-195.
[4]	宋先海, 张学强, 王一鸣, 等. 近地表弹性介质瑞雷波勘探研究进展与展望[J]. 地质科技通报, 2020, 39(5):173-182.
[4]	Song X H, Zhang X Q, Wang Y M, et al. Recent advances and prospects of near surface elastic Rayleigh waves[J]. Bulletin of Geological Science and Technology, 2020, 39(5):173-182.
[5]	Xia J H, Miller R D, Park C B, et al. Comparing shear-wave velocity profiles inverted from multichannel surface wave with borehole measurements[J]. Soil Dynamics and Earthquake Engineering, 2002, 22(3):181-190.
[6]	Xia J H. Estimation of near-surface shear-wave velocities and quality factors using multichannel analysis of surface-wave methods[J]. Journal of Applied Geophysics, 2014,103:140-151.
[7]	向晓松, 贾继标, 李士祥. 面波勘探在地基液化判别中的应用与探讨[J]. 矿产勘查, 2011, 2(1):97-101.
[7]	Xiang X S, Jia J B, Li S X. Application and study of surface wave exploration in distinguishing foundation liquefaction[J]. Mineral Exploration, 2011, 2(1):97-101.
[8]	李启成, 闫晓丹, 孙颖川, 等. 利用瑞利面波进行岩性分层[J]. 地球物理学进展, 2016, 31(5):2124-2127.
[8]	Li Q C, Yan X D, Sun Y C, et al. Measurement of superstratum wave velocity with reflection wave on dip section[J]. Progress in Geophysics, 2016, 31(5):2124-2127.
[9]	赵东, 王光杰, 王兴泰, 等. 用遗传算法进行瑞利波反演[J]. 物探与化探, 1995, 19(3):178-185.
[9]	Zhao D, Wang G J, Wang X T, et al. The application of genetic algorithm to Rayleigh wave inversion[J]. Geophysical and Geochemical Exploration, 1995, 19(3):178-185.
[10]	Yamanaka H, Ishida H. Application of genetic algorithms to an inversion of surface-wave dispersion data[J]. Bulletin of the Seismological Society of America, 1996, 86(2):436-444.
[11]	Song X H, Tang L, Lyu X C, et al. Application of particle swarm optimization to interpret Rayleigh wave dispersion curves[J]. Journal of Applied Geophysics, 2012,84:1-13.
[12]	张晓阳, 杜文凤, 卢勇旭. 粒子群算法在面波频散曲线反演中的应用[J]. 辽宁工程技术大学学报:自然科学版, 2016, 35(12):1527-1532.
[12]	Zhang X Y, Du W F, Lu Y X. The application of particle swarm optimization in the inversion of Rayleigh wave dispersion curve[J]. Journal of Liaoning Technical University:Natural Science Edition, 2016, 35(12):1527-1532.
[13]	蔡伟, 宋先海, 袁士川, 等. 利用粒子群优化算法快速、稳定反演瑞雷波频散曲线[J]. 石油地球物理勘探, 2018, 53(1):25-34.
[13]	Cai W, Song X H, Yuan S C, et al. Fast and stable Rayleigh-wave dispersion-curve inversion based on particle swarm optimization[J]. Oil Geophysical Prospecting, 2018, 53(1):25-34.
[14]	彭刘亚, 任川. 基于粒子群算法的瑞雷波频散曲线反演研究[J]. 地球物理学进展, 2018, 33(4):1682-1686.
[14]	Peng L Y, Ren C. Inversion of Rayleigh wave dispersion curve using particle swarm optimization algorithm[J]. Progress in Geophysics, 2018, 33(4):1682-1686.
[15]	彭刘亚, 任川, 冯伟栋. 多模式瑞雷波频散曲线的粒子群反演方法研究[J]. 地球物理学进展, 2018, 33(3):1262-1268.
[15]	Peng L Y, Ren C, Feng W D. Multimodal Rayleigh wave dispersion curve inversion by particle swarm optimization[J]. Progress in Geophysics, 2018, 33(3):1262-1268.
[16]	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.
[16]	Hou Z, Xiong S Q, Yang J, 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.
[17]	于东凯, 宋先海, 江东威, 等. 改进蜂群算法及其在面波频散曲线反演中的应用[J]. 地球物理学报, 2018, 61(4):1482-1495. doi: 10.6038/cjg2018L0424
[17]	Yu D K, Song X H, Jiang D W, et al. Improvement of Artificial Bee Colony and its application in Rayleigh wave inversion[J]. Chinese Journal of Geophysics, 2018, 61(4):1482-1495.
[18]	于东凯, 宋先海, 张学强, 等. 蚱蜢算法在瑞雷波频散曲线反演中的应用[J]. 石油地球物理勘探, 2019, 54(2):288-301.
[18]	Yu D K, Song X H, Zhang X Q, et al. Rayleigh wave dispersion inversion based on grasshopper optimization algorithm[J]. Oil Geophysical Prospecting, 2019, 54(2):288-301.
[19]	高旭, 于静, 李学良, 等. 自适应权重蜻蜓算法及其在瑞雷波频散曲线反演中的应用[J]. 石油地球物理勘探, 2021, 56(4):745-757.
[19]	Gao X, Yu J, Li X L, et al. Rayleigh wave dispersion curve inversion based on adaptive weight dragonfly algorithm[J]. Oil Geophysical Prospecting, 2021, 56(4):745-757.
[20]	崔建文. 一种改进的全局优化算法及其在面波频散曲线反演中的应用[J]. 地球物理学报, 2004, 47(3):521-527.
[20]	Cui J W. An improved global optimization method and its application to the inversion of surface wave dispersion curves[J]. Chinese Journal of Geophysics, 2004, 47(3):521-527.
[21]	杨博, 熊章强, 张大洲, 等. 利用自适应混沌遗传粒子群算法反演瑞雷面波频散曲线[J]. 石油地球物理勘探, 2019, 54(6):1217-1227.
[21]	Yang B, Xiong Z Q, Zhang D Z, et al. Rayleigh surface-wave dispersion curve inversion based on adaptive chaos genetic particle swarm optimization algorithm[J]. Oil Geophysical Prospecting, 2021, 56(4):2019, 54(6):1217-1227.
[22]	蔡伟, 宋先海, 袁士川, 等. 基于萤火虫和蝙蝠群智能算法的瑞雷波频散曲线反演[J]. 地球物理学报, 2018, 61(6):2409-2420. doi: 10.6038/cjg2018L0322
[22]	Cai W, Song X H, Yuan S C, et al. Inversion of Rayleigh wave dispersion curves based on firefly and bat algorithms[J]. Chinese Journal of Geophysics, 2018, 61(6):2409-2420.
[23]	王一鸣, 宋先海, 张学强. 瑞雷面波频散曲线的粒子群蚁群混合优化反演[J]. 石油地球物理勘探, 2022, 57(2):303-310.
[23]	Wang Y M, Song X H, Zhang X Q. Inversion of Rayleigh wave dispersion curves based on particle swarm and ant colony hybrid optimization[J]. Oil Geophysical Prospecting, 2022, 57(2):303-310.
[24]	李翠琳, Stan E Dosso, Hefeng Dong. 根据非线性贝叶斯理论的界面波频散曲线反演[J]. 声学学报, 2012, 37(3):225-231.
[24]	Li C L, Dosso S, Dong H F. Interface-wave dispersion curves inversion based on nonlinear Bayesian theory[J]. Acta Acustica, 2012, 37(3):225-231.
[25]	付代光, 刘江平, 周黎明, 等. 基于贝叶斯理论的软夹层多模式瑞雷波频散曲线反演研究[J]. 岩土工程学报, 2015, 37(2):321-329.
[25]	Fu D G, Liu J P, Zhou L M, et al. Inversion of multimode Rayleigh-wave dispersion curves of soft interlayer based on Bayesian theory[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(2):321-329.
[26]	付代光, 肖国强, 周黎明, 等. 基于非线性贝叶斯理论和BIC准则的防渗墙高精度瑞雷波反演研究[J]. 水利水电技术, 2018, 49(8):64-70.
[26]	Fu D G, Xiao G Q, Zhou L M, et al. Nonlinear Bayesian theory and BIC criterion-based study on high precision Rayleigh wave inversion of cutoff-wall[J]. Water Resources and Hydropower Engineering, 2018, 49(8):64-70.
[27]	刘辉, 李静, 曾昭发, 等. 基于贝叶斯理论面波频散曲线随机反演[J]. 物探与化探, 2021, 45(4):951-960.
[27]	Liu H, Li J, Zeng Z F, et al. Stochastic inversion of surface wave dispersion curves based on Bayesian theory[J]. Geophysical and Geochemical Exploration, 2021, 45(4):951-960.
[28]	刘昊楠, 张致付, 陈凯. 瑞利面波频散曲线贝叶斯反演及其在活断层调查中的应用[J]. 地球物理学进展, 2020, 35(4):1584-1589.
[28]	Liu H N, Zhang Z F, Chen K. Bayesian inversion of Rayleigh surface wave dispersion and its application in active fault investigation[J]. Progress in Geophysics, 2020, 35(4):1584-1589.
[29]	贺懿, 张进, 刘怀山. 基于神经网络的面波迭代反演应用研究[J]. 西南石油大学学报:自然科学版, 2010, 32(1):40-44.
[29]	He Y, Zhang J, Liu H S. Study on the application of iterative inversion of surface wave based on artificial neural network[J]. Journal of Southwest Petroleum University:Science & Technology Edition, 2010, 32(1):40-44.
[30]	魏继祖, 丁彦礼, 单娜琳, 等. 多阶模态瑞利面波频散曲线的BP神经网络反演[J]. 工程地球物理学报, 2012, 9(6):646-653.
[30]	Wei J Z, Ding Y L, Shan N L, et al. Study on BP inversion of the multi-modes Rayleigh wave[J]. Chinese Journal of Engineering Geophysics, 2012, 9(6):646-653.
[31]	曹旭, 熊章强, 张大洲. 基于BP神经网络的瑞雷面波智能优化反演[J]. 工程地球物理学报, 2015, 12(4):514-519.
[31]	Cao X, Xiong Z Q, Zhang D Z. The Rayleigh surface wave intelligent inversion based on the BP artificial neural network[J]. Chinese Journal of Engineering Geophysics, 2015, 12(4):514-519.
[32]	王一鸣, 宋先海, 张学强. 应用人工神经网络算法的地震面波非线性反演[J]. 石油地球物理勘探, 2021, 56(5):979-991.
[32]	Wang Y M, Song X H, Zhang X Q. Research on nonlinear inversion of seismic surface waves based on artificial neural network algorithm[J]. Oil Geophysical Prospecting, 2021, 56(5):979-991.
[33]	张志厚, 石泽玉, 马宁, 等. 瑞雷波频散曲线的深度学习反演方法[J]. 地球物理学报, 2022, 65(6):2244-2259.
[33]	Zhang Z H, Shi Z Y, Ma N, et al. Deep learning inversion of Rayleigh dispersion curves[J]. Chinese Journal of Geophysics, 2022, 65(6):2244-2259.
[34]	Arora S, Singh S. Butterfly algorithm with Levy flights for global optimization[C]// Waknaghat: 2015 International Conference on Signal Processing,Computing and Control(ISPCC).IEEE,2015: 220-224.
[35]	Arora S, Singh S. An improved butterfly optimization algorithm with chaos[J]. Journal of Intelligent & Fuzzy Systems, 2017, 32(1):1079-1088.
[36]	Arora S, Singh S. Butterfly optimization algorithm:A novel approach for global optimization[J]. Soft Computing, 2019, 23(3):715-734.
[37]	Raguso R A. Wake up and smell the roses:The ecology and evolution of floral scent[J]. Annual Review of Ecology,Evolution,and Systematics, 2008,39:549-569.
[38]	宁杰琼, 何庆. 混合策略改进的蝴蝶优化算法[J]. 计算机应用研究, 2021, 38(6):1718-1723,1738.
[38]	Ning J Q, He Q. Mixed strategy to improve butterfly optimization algorithm[J]. Application Research of Computers, 2021, 38(6):1718-1723,1738.
[39]	刘凯, 代永强. 融合变异策略的自适应蝴蝶优化算法[J]. 计算机应用研究, 2022, 39(1):134-140,145.
[39]	Liu K, Dai Y Q. Adaptive butterfly optimization algorithm based on mutation strategies[J]. Application Research of Computers, 2022, 39(1):134-140,1455.
[40]	郑洪清, 冯文健, 周永权. 融合正弦余弦算法的蝴蝶优化算法[J]. 广西科学, 2021, 28(2):152-159.
[40]	Zheng H Q, Feng W J, Zhou Y Q. Butterfly optimization algorithm based on sine cosine algorithm[J]. Guangxi Sciences, 2021, 28(2):152-159.
[41]	高文欣, 刘升, 肖子雅, 等. 柯西变异和自适应权重优化的蝴蝶算法[J]. 计算机工程与应用, 2020, 56(15):43-50. doi: 10.3778/j.issn.1002-8331.1907-0048
[41]	Gao W X, Liu S, Xiao Z Y, et al. Butterfly optimization algorithm based on cauchy variation and adaptive weight[J]. Computer Engineering and Applications, 2020, 56(15):43-50. doi: 10.3778/j.issn.1002-8331.1907-0048
[42]	凡友华, 刘家琦. 层状介质中瑞雷面波的频散研究[J]. 哈尔滨工业大学学报, 2001, 33(5):577-581.
[42]	Fan Y H, Liu J Q. Research on the dispersion of rayleigh waves in multilayered media[J]. Journal of Harbin Institute of Technology, 2001, 33(5):577-581.
[43]	董智开, 段文胜, 肖承文, 等. 基于快速标量传递算法的瑞雷波频散曲线反演研究[J]. 北京大学学报:自然科学版, 2020, 56(4):614-628.
[43]	Dong Z K, Duan W S, Xiao C W, et al. Inversion research of Rayleigh wave dispersion curve based on fast scalar transfer algorithm[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(4):614-628.
[44]	Dal Moro G, Pipan M, Gabrielli P. Rayleigh wave dispersion curve inversion via genetic algorithms and Marginal Posterior Probability density estimation[J]. Journal of Applied Geophysics, 2007, 61(1):39-55.
[45]	王天琦, 于东凯, 蔡润. 基于改进蚁群算法在面波频散曲线反演中的应用[J]. 地震工程学报, 2020, 42(6):1523-1533.
[45]	Wang T Q, Yu D K, Cai R. Application of the improved ant colony algorithm in the inversion of Rayleigh wave dispersion curves[J]. China Earthquake Engineering Journal, 2020, 42(6):1523-1533.

[1]	姚禹, 张志厚. 基于改进DenseNet的大地电磁智能反演[J]. 物探与化探, 2024, 48(3): 759-767.
[2]	王震, 计智锋, 张艺琼, 王雪柯, 蒋黎, 林雅平, 孔令洪, 张明军. 滨里海盆地东缘速度建模方法研究与应用[J]. 物探与化探, 2024, 48(3): 794-803.
[3]	杨敏, 徐新强, 陈明, 纪晓琳, 王万银, 赵东明, 周巍, 张义蜜. 基于高精度重力勘探对地下空洞的探测研究[J]. 物探与化探, 2024, 48(3): 876-883.
[4]	梁志强, 李弘. 不同方位各向异性反演技术对比和总结[J]. 物探与化探, 2024, 48(2): 443-450.
[5]	苏朝阳, 沈金松, 罗辉. 基于PARDISO直接求解器的三维自然电位正反演[J]. 物探与化探, 2024, 48(2): 451-460.
[6]	张婧, 汪勇, 赵慧言, 衡德, 黄君, 张晓丹, 王文文, 贺燕冰. 基于全局自适应MCMC算法的裂缝型储层缝隙流体因子叠前地震反演[J]. 物探与化探, 2024, 48(1): 105-112.
[7]	付兴, 谭捍东, 董岩, 汪茂. 基于监督下降法的大地电磁二维反演及应用研究[J]. 物探与化探, 2024, 48(1): 175-184.
[8]	董健, 李肖鹏, 付超, 党智财, 赵晓博, 曾庆斌, 胡雪平, 王金辉. 高精度重磁方法寻找隐伏矽卡岩型铁矿[J]. 物探与化探, 2024, 48(1): 31-39.
[9]	孔繁祥, 谭捍东, 刘建勋. 海洋可控源电磁法与地震全波形二维联合反演研究[J]. 物探与化探, 2024, 48(1): 67-76.
[10]	牛丽萍, 胡华锋, 周单, 郑晓东, 耿建华. 基于精确Zoeppritz方程的贝叶斯叠前地震随机反演[J]. 物探与化探, 2024, 48(1): 77-87.
[11]	史全党, 孔令业, 吴超, 丁艳雪, 刘泽民, 于雪, 王江. 基于小波边缘分析与井—震联合建模的波阻抗反演技术在陆梁隆起带储层预测中的应用[J]. 物探与化探, 2023, 47(6): 1425-1432.
[12]	付宇, 艾寒冰, 姚振岸, 梅竹虚, 苏可嘉. 基于正余弦算法的瑞利波频散曲线反演[J]. 物探与化探, 2023, 47(6): 1467-1478.
[13]	李思平, 刘彩云, 熊杰, 田慧潇, 王方. 基于改进残差网络的大地电磁反演研究[J]. 物探与化探, 2023, 47(6): 1508-1518.
[14]	刘湘浩, 刘四新, 胡铭奇, 孙中秋, 王千. 基于OMAGA-BP算法的高密度电阻率法反演研究[J]. 物探与化探, 2023, 47(6): 1519-1527.
[15]	王康, 刘彩云, 熊杰, 王永昌, 胡焕发, 康佳帅. 基于全卷积残差收缩网络的地震波阻抗反演[J]. 物探与化探, 2023, 47(6): 1538-1546.

Viewed

Full text

Abstract

Cited

Shared

Discussed