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.
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**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**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。
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References
HE Ji-shan. Dual frequency induced polarization method [M]. Bei**g: Higher Education Press, 2006. (in Chinese)
WU Han-rong, WANG Shi-ming. The feasibility of nature source induced polarization [J]. Geophysical and Geochemical Exploration, 1978(1): 62–64. (in Chinese)
WARE G. Theoretical and field investigations of telluric currents and induced polarization [D]. Berkeley: University of California at Berkeley, 1974.
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.
GASPERIKOVA E, MORRISON H F. Map** of induced polarization using natural fields [J]. Geophysics, 2001, 66(1): 137–147. DOI: 10.1190/1.1444888.
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.
YUE An-**, 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.
HE Zhan-xiang, HU Zu-zhi, LUO Wei-feng, WANG Cai-fu. Map** 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.
YU Chuan-tao, LIU Hong-fu, ZHANG **n-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.
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.
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.
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.
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.
DIAS C A. A non-grounded method for measuring electrical induced polarization and conductivity [D]. Berkeley: University of California, 1968.
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.
KENNEDY J, EBERHART R. Particle swarm optimization [C]// IEEE International Conference on Neural Networks. Proceedings. IEEE Xplore, 1995: 1942–1948.
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.
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.
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.
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.
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.2012.11.027
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.
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.
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.
LIU Mei-ling, LIU **ang-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.
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.
LI Guang, TANG **g-tian, XIAO **ao, LI **, 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.
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.
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Foundation item: Projects(41604117, 41204054) supported by the National Natural Science Foundation of China; Projects(20110490149, 2015M580700) supported by the Research Fund for the Doctoral Program of Higher Education, China; Project(2015zzts064) supported by the Fundamental Research Funds for the Central Universities, China; Project(16B147) supported by the Scientific Research Fund of Hunan Provincial Education Department, China
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Dong, L., Li, Dq. & Jiang, Fb. A two-stage CO-PSO minimum structure inversion using CUDA for extracting IP information from MT data. J. Cent. South Univ. 25, 1195–1212 (2018). https://doi.org/10.1007/s11771-018-3818-4
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DOI: https://doi.org/10.1007/s11771-018-3818-4
Key words
- Cauchy oscillation particle swarm optimization
- magnetotelluric sounding
- nonlinear inversion
- induced polarization (IP) information extraction
- compute unified distributed architecture (CUDA)