全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

频率域航空电磁数据变维数贝叶斯反演研究

DOI: 10.6038/cjg20140922, PP. 2971-2980

Keywords: 航空电磁,频率域,正演,权系数,变维数贝叶斯反演

Full-Text   Cite this paper   Add to My Lib

Abstract:

传统的梯度反演方法已经广泛应用于频率域航空电磁数据处理中,然而此类方法受初始模型影响较大,且容易陷入局部极小.为解决这一问题,本文采用改进的变维数贝叶斯反演方法实现航空电磁数据反演.该方法根据建议分布对反演模型进行随机采样,并依据接受概率筛选合理的候选模型,最终获得反演模型的概率分布和不确定度信息.为解决贝叶斯反演方法对深部低阻层反演效果不佳的问题,本文通过引入合理加权系数,调整对反演模型约束强度,在很大程度上改善了反演效果.通过对模型统计方法进行改进,在遵循原有模型采样方法和接受标准的基础上,将满足数据拟合要求的模型纳入统计范围,削弱不合理模型对统计结果的干扰.本文最后通过对含有高斯噪声的理论数据和实测数据进行反演,并与Occam反演结果进行对比,验证了该方法的有效性.

References

[1]  Agostinetti N P, Malinverno A. 2010. Receiver function inversion by trans-dimensional Monte Carlo sampling. Geophys. J. Int., 181(2): 858-872, doi:10.1111/j.1365-246X.2010. 04530.x.
[2]  Chen J S, Kemna A, Hubbard S. 2008. A comparison between Gauss-Newton and Markov-chain Monte Carlo-based methods for inverting spectral induced-polarization data for Cole-Cole parameters. Geophysics, 73(6): F247-F259, doi:10.1190/1.2976115.
[3]  Egbert G D, Kelbert A. 2012. Computational recipes for electromagnetic inverse problems. Geophys. J. Int., 189(1): 251-267, doi:10.1111/j.1365-246X.2011.05347.x.
[4]  Green P J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4): 711-732.
[5]  Guo R W, Dosso S E, Liu J X, et al. 2011. Non-linearity in Bayesian 1-D magnetotelluric inversion. Geophys. J. Int., 185(2): 663-675, doi:10.1111/j.1365-246X.2011.04996.x.
[6]  Huang H P, Fraser D C. 1996. The differential parameter method for multifrequency airborne resistivity mapping. Geophysics, 61(1): 100-109.
[7]  Lei D, Hu X Y, Zhang S F. 2006. Development status of Airborne Electromagnetic. Contributions to Geology and Mineral Resources Research, 21(1):40-53.
[8]  Minsley B J. 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophys. J. Int., 187(1): 252-272.
[9]  Mitsuhata Y, Uchida T, Amano H. 2002. 2.5-D inversion of frequency-domain electromagnetic data generated by a grounded-wire source. Geophysics, 67(6): 1753-1768.
[10]  Ray A, Key K. 2012. Bayesian inversion of marine CSEM data with a trans-dimensional self parametrizing algorithm. Geophys. J. Int., 191(3): 1135-1151, doi:10.1111/j.1365-246X. 2012.05677.x.
[11]  Sambridge M, Gallagher K, Jackson A, et al. 2006. Trans-dimensional inverse problems, model comparison and the evidence. Geophys. J. Int., 167(2): 528-542, doi:10.1111/j.1365-246X.2006.03155.x.
[12]  Sengpiel K P. 1988. Approximate inversion of airborne EM data from a multilayered ground. Geophysical Prospecting, 36(4): 446-459.
[13]  Shi X M, Wang J Y, Zhang S Y. 2000. Multiscale genetic algorithm and its application in magnetotelluric sounding data inversion. Chinese J. Geophys. (in Chinese), 43(1): 122-130.
[14]  Trainor-Guitton W, Hoversten G M. 2011. Stochastic inversion for electromagnetic geophysics: Practical challenges and improving convergence efficiency. Geophysics, 76(6): F373-F386, doi:10.1190/GEO2010-0223.1.
[15]  Xu H L, Wu X P. 2006. 2D resistivity inversion using the neural network method. Chinese J. Geophys. (in Chinese), 49(2): 584-589.
[16]  Yin C C, Hodges G. 2007. Simulated annealing for airborne EM inversion. Geophysics, 72(4): F189-F196.
[17]  Malinverno A. 2002. Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem. Geophys. J. Int., 151(3): 675-688.
[18]  Yao Y. 2002. The Basic Theory and Application of Geophysical Inversion Methods. Beijing: China University of Geosciences Press.
[19]  Yin C C. 2000. Geoelectrical inversion for a one-dimensional anisotropic model and inherent non-uniqueness. Geophys. J. Int., 140: 11-23.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133