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Journal of Central South University

, Volume 25, Issue 5, pp 1195–1212 | Cite as

A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data

  • Li Dong (董莉)
  • Di-quan Li (李帝铨)
  • Fei-bo Jiang (江沸菠)
Article
  • 31 Downloads

Abstract

The study of induced polarization (IP) information extraction from magnetotelluric (MT) sounding data is of great and practical significance to the exploitation of deep mineral, oil and gas resources. The linear inversion method, which has been given priority in previous research on the IP information extraction method, has three main problems as follows: 1) dependency on the initial model, 2) easily falling into the local minimum, and 3) serious non-uniqueness of solutions. Taking the nonlinearity and nonconvexity of IP information extraction into consideration, a two-stage CO-PSO minimum structure inversion method using compute unified distributed architecture (CUDA) is proposed. On one hand, a novel Cauchy oscillation particle swarm optimization (CO-PSO) algorithm is applied to extract nonlinear IP information from MT sounding data, which is implemented as a parallel algorithm within CUDA computing architecture; on the other hand, the impact of the polarizability on the observation data is strengthened by introducing a second stage inversion process, and the regularization parameter is applied in the fitness function of PSO algorithm to solve the problem of multi-solution in inversion. The inversion simulation results of polarization layers in different strata of various geoelectric models show that the smooth models of resistivity and IP parameters can be obtained by the proposed algorithm, the results of which are relatively stable and accurate. The experiment results added with noise indicate that this method is robust to Gaussian white noise. Compared with the traditional PSO and GA algorithm, the proposed algorithm has more efficiency and better inversion results.

Key words

Cauchy oscillation particle swarm optimization magnetotelluric sounding nonlinear inversion induced polarization (IP) information extraction compute unified distributed architecture (CUDA) 

基于二阶段CO-PSO 最小构造反演的MT 信号激电信息提取研究与CUDA 实现

摘要

从大地电磁测深资料中提取激发极化信息,对深部矿产、油气资源的开发具有极为重要的现实意义。 目前的IP 信息提取方法多以线性反演方法为主,主要存在以下3 个问题:1)依赖初始模型;2)容易陷入 局部极值;3)多解性严重。考虑到IP 信息提取的非线性和非凸性,本文提出了一种采用二阶段CO-PSO 最小构造反演方法来提取MT信号中的激电信息。在该方法中,一方面,运用柯西振荡粒子群优化(CO-PSO) 算法从MT 数据中非线性提取激电信息,并使用CUDA 架构进行并行实现;另一方面通过引入第二阶段反 演过程,增强反演时极化率对观测数据的影响,同时为了解决反演中的多解性问题,将正则化参数应用于 PSO 算法的适应度函数。通过对不同地电模型下极化层位于不同地层的反演结果表明,该算法可以得到电 阻率和极化率的光滑模型,其结果相对稳定,准确。加入噪声后的实验结果表明,该方法对高斯白噪声具 有鲁棒性。与传统的PSO 和GA 算法相比,该算法具有更高的反演效率以及更好的反演效果。

关键词

柯西振荡粒子群优化 大地电磁测深 非线性反演 激电信息提取 CUDA 

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References

  1. [1]
    HE Ji-shan. Dual frequency induced polarization method [M]. Beijing: Higher Education Press, 2006. (in Chinese)Google Scholar
  2. [2]
    WU Han-rong, WANG Shi-ming. The feasibility of nature source induced polarization [J]. Geophysical and Geochemical Exploration, 1978(1): 62–64. (in Chinese)Google Scholar
  3. [3]
    WARE G. Theoretical and field investigations of telluric currents and induced polarization [D]. Berkeley: University of California at Berkeley, 1974.Google Scholar
  4. [4]
    MURALI S, RAO I B R, BHIMASANKARAM V L S, Comparison of anomalous effects determined using telluric fields and time domain IP technique (test results) [J]. Exploration Geophysics, 1980, 11(2): 45–46. DOI: 10.1071/EG980045.CrossRefGoogle Scholar
  5. [5]
    GASPERIKOVA E, MORRISON H F. Mapping of induced polarization using natural fields [J]. Geophysics, 2001, 66(1): 137–147. DOI: 10.1190/1.1444888.CrossRefGoogle Scholar
  6. [6]
    GHORBANI A, CAMERLYNCK C, FLORSCH N. CR1Dinv: A matlab program to invert 1D spectral induced polarization data for the Cole–Cole model including electromagnetic effects [J]. Computers & Geosciences, 2009, 35(2): 255–266. DOI: 10.1016/j.cageo.2008.06.001.CrossRefGoogle Scholar
  7. [7]
    YUE An-ping, DI Qing-yun, WANG Miao-yue. 1-D forward modeling of the CSAMT signal incorporating IP effect [J]. Chinese Journal of Geophysics, 2009, 52(7): 1937–1946. (in Chinese). DOI: 10.1002/cjg2.1411.Google Scholar
  8. [8]
    HE Zhan-xiang, HU Zu-zhi, LUO Wei-feng, WANG Cai-fu. Mapping reservoirs based on resistivity and induced polarization derived from continuous 3D magnetotelluric profiling: Case study from Qaidam basin, China [J]. Geophysics, 2010, 75(1): B25–B33. DOI: 10.1190/1. 3279125.CrossRefGoogle Scholar
  9. [9]
    YU Chuan-tao, LIU Hong-fu, ZHANG Xin-jun. The analysis on IP signals in TEM response based on SVD [J]. Applied Geophysics, 2013, 10(1): 79–87. DOI: 10.1007/S11770-013-0366-4.CrossRefGoogle Scholar
  10. [10]
    TANG Rui, XU Peng, XIANG Yang, ZHANG Xu. The sensitivity analysis of different induced polarization models used in magnetotelluric method [J]. Acta Geodaetica et Geophysica, 2014, 49(2): 225–233. DOI: 10.1007/s40328-014-0050-z.CrossRefGoogle Scholar
  11. [11]
    SHAW R, SRIVASTAVA S. Particle swarm optimization: A new tool to invert geophysical data [J]. Geophysics, 2007, 72(2): F75–F83. DOI: 10.1190/1.2432481.CrossRefGoogle Scholar
  12. [12]
    YUAN San-yi, WANG Shang-xu, TIAN Nan. Swarm intelligence optimization and its application in geophysical data inversion [J]. Applied Geophysics, 2009, 6(2): 166–174. DOI: 10.1007/s11770-009-0018-x.CrossRefGoogle Scholar
  13. [13]
    JIANG Fei-bo, DAI Qian-wei, DONG Li. An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging [J]. Journal of Central South University, 2016, 23(8): 2129–2138. DOI: 10.1007/s11771-016-3269-8.CrossRefGoogle Scholar
  14. [14]
    DIAS C A. A non-grounded method for measuring electrical induced polarization and conductivity [D]. Berkeley: University of California, 1968.Google Scholar
  15. [15]
    DIAS C A. Developments in a model to describe lowfrequency electrical polarization of rocks [J]. Geophysics, 2000, 65(2): 437–451. DOI: 10.1190/1.1444738.CrossRefGoogle Scholar
  16. [16]
    KENNEDY J, EBERHART R. Particle swarm optimization [C]// IEEE International Conference on Neural Networks. Proceedings. IEEE Xplore, 1995: 1942–1948.CrossRefGoogle Scholar
  17. [17]
    NABAVI-KERIZI S H, ABADI M, KABIR E. A PSO-based weighting method for linear combination of neural networks [J]. Computers & Electrical Engineering, 2010, 36(5): 886–894. DOI: 10.1016/j.compeleceng.2008.04.006.CrossRefzbMATHGoogle Scholar
  18. [18]
    MUSSI L, DAOLIO F, CAGNONI S. Evaluation of parallel particle swarm optimization algorithms within the CUDA™ architecture [J]. Information Sciences, 2011, 181(20): 4642–4657. DOI: 10.1016/j.ins.2010.08.045.CrossRefGoogle Scholar
  19. [19]
    OUYANG Ai-jia, TANG Zhou, ZHOU Xu, XU Yu-ming, PAN Guo, LI Ke-qin. Parallel hybrid PSO with CUDA for lD heat conduction equation [J]. Computers & Fluids, 2015, 110(30): 198–210. DOI: 10.1016/j.compluid.2014.05.020.MathSciNetCrossRefGoogle Scholar
  20. [20]
    DALI N, BOUAMAMA S. GPU-PSO: Parallel particle swarm optimization approaches on graphical processing unit for constraint reasoning: case of Max-CSPs [J]. Procedia Computer Science, 2015, 60(1): 1070–1080. DOI: 10.1016/j.procs.2015.08.152.CrossRefGoogle Scholar
  21. [21]
    UGOLOTTI R, NASHED Y S G, MESEJO P, LVEKOUI, MUSSI L, CAGONI S. Particle swarm optimization and differential evolution for model-based object detection [J]. Applied Soft Computing, 2013, 13(6): 3092–3105. DOI: 10.1016/j.asoc.2012.11.027CrossRefGoogle Scholar
  22. [22]
    JIANG Fei-bo, DAI Qian-wei, DONG Li. Ultra-high density resistivity nonlinear inversion based on principal component-regularized ELM [J]. Chinese Journal of Geophysics-Chinese Edition, 2015, 58(9): 3356–3369. (in Chinese). DOI:10.6038/cjg20150928.Google Scholar
  23. [23]
    CONSTABLE S C, PARKER R L, CONSTABLE C G. Occam’s inversion: A practical algorithm for generating smooth models from electromagnetic sounding data [J]. Geophysics, 1987, 52(3): 289–300. DOI: 10.1190/1. 1442303.CrossRefGoogle Scholar
  24. [24]
    JIANG Fei-bo, DAI Qian-wei, DONG Li. Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks [J]. Applied Geophysics, 2016, 13(2): 267–278. DOI: 10.1007/s11770-016-0561-1.CrossRefGoogle Scholar
  25. [25]
    LIU Mei-ling, LIU Xiang-nan, WU Men-xin. Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model [J]. Computers & Geosciences, 2011, 37(10): 1642–1652. DOI: 10.1016/j.cageo.2011.03.009.CrossRefGoogle Scholar
  26. [26]
    JIANG Fei-bo, DONG Li, DAI Qian-wei, DAVID C N. Using wavelet packet denoising and ANFIS networks based on COSFLA optimization for electrical resistivity imaging inversion [J]. Fuzzy Sets and Systems, 2018, 337: 93–112. DOI: 10.1016/j.fss. 2017.07.009.MathSciNetCrossRefGoogle Scholar
  27. [27]
    LI Guang, TANG Jing-tian, XIAO Xiao, LI Jin, ZHU Hui-jie, ZHOU Cong, YAN Fa-bao. Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering [J]. Applied Geophysics, 2017, 14(4): 581–589. DOI: 10.1007/s11770-017-0645-6.CrossRefGoogle Scholar
  28. [28]
    JIANG Fei-bo, DONG Li, DAI Qian-wei. Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach [J]. Neural Networks, 2018, in press. DOI: 10.1016/j.neunet. 2018.04.012.Google Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Geosciences and Info-PhysicsCentral South UniversityChangshaChina
  2. 2.School of Information Science and EngineeringHunan International Economics UniversityChangshaChina
  3. 3.College of Information Science and EngineeringHunan Normal UniversityChangshaChina
  4. 4.School of Computer and Information EngineeringHunan University of CommerceChangshaChina

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