Volume 43 Issue 4
Jul.  2023
Turn off MathJax
Article Contents
Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(4): 600-608 Open Access
MA Sen, MA Jiahui, TONG Jizhou, LI Yunlong. Analysis of Global Geomagnetic Main Field Model Order Based on Bayesian Evidence (in Chinese). Chinese Journal of Space Science, 2023, 43(4): 600-608 doi: 10.11728/cjss2023.04.2022-0009
Citation: MA Sen, MA Jiahui, TONG Jizhou, LI Yunlong. Analysis of Global Geomagnetic Main Field Model Order Based on Bayesian Evidence (in Chinese). Chinese Journal of Space Science, 2023, 43(4): 600-608 doi: 10.11728/cjss2023.04.2022-0009
shu

Analysis of Global Geomagnetic Main Field Model Order Based on Bayesian Evidence

doi: 10.11728/cjss2023.04.2022-0009 cstr: 32142.14.cjss2023.04.2022-0009
  • 1.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    National Space Science Data Center, Beijing 101407

  • Received Date: 2022-04-20
  • Accepted Date: 2022-07-19
  • Rev Recd Date: 2022-07-24
    • Available Online: 2022-07-24
    Abstract
  • The global main magnetic field model describes the space-time distribution characteristics of the main magnetic field. The order of the main magnetic field in the model is one of the key issues to build the main magnetic field model. This paper used Bayesian inference to analyze the global geomagnetic main field model and compares model orders based on Bayesian evidence. It provided a statistical perspective for the main field order selection. Using magnetic observations from Swarm satellites, the evidence for different orders of the main field model was estimated. The results show that order N = 12 has the global best evidence in model from 1 to 20. Referring to the threshold interval given by Jefrrey’s scale, the data preference for order N = 12 is significantly better than other orders. The experiment shows that the evidence reasoning of the main magnetic field order can be used to study the main magnetic field contribution, and the results match the power spectrum analysis of spherical harmonic of order N = 14.

     

    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    徐文耀. 地球电磁现象物理学[M]. 合肥: 中国科学技术大学出版社, 2009: 87-157

    XU Wenyao. Physics of Electromagnetic Phenomena of the Earth[M]. Hefei: University of Science and Technology of China Press, 2009: 87-157
    [2]
    ALKEN P, THÉBAULT E, BEGGAN C D, et al. International geomagnetic reference field: the thirteenth generation[J]. Earth, Planets and Space, 2021, 73(1): 1-25 doi: 10.1186/s40623-020-01288-x
    [3]
    YANG Y, HULOT G, VIGNERON P, et al. The CSES global geomagnetic field model (CGGM): an IGRF-type global geomagnetic field model based on data from the China seismo-electromagnetic satellite[J]. Earth, Planets and Space, 2021, 73(1): 1-21 doi: 10.1186/s40623-020-01323-x
    [4]
    HUDER L, GILLET N, FINLAY C C, et al. COV-OBS. x2: 180 years of geomagnetic field evolution from ground-based and satellite observations[J]. Earth, Planets and Space, 2020, 72(1): 1-18 doi: 10.1186/s40623-019-1127-2
    [5]
    FINLAY C C, KLOSS C, OLSEN N, et al. The CHAOS-7 geomagnetic field model and observed changes in the south Atlantic anomaly[J]. Earth, Planets and Space, 2020, 72(1): 1-31 doi: 10.1186/S40623-020-01252-9
    [6]
    SABAKA T J, OLSEN N, TYLER R H, et al. CM5, a pre-Swarm comprehensive geomagnetic field model derived from over 12 yr of CHAMP, Ørsted, SAC-C and observatory data[J]. Geophysical Journal International, 2015, 200(3): 1596-1626 doi: 10.1093/gji/ggu493
    [7]
    LI S Y, LI Y L, ZHANG T J, et al. Model-independent determination of cosmic curvature based on the Padé approximation[J]. The Astrophysical Journal, 2019, 887(1): 36 doi: 10.3847/1538-4357/ab5225
    [8]
    BURNHAM K P, ANDERSON D R. Multimodel inference: understanding AIC and BIC in model selection[J]. Sociological Methods & Research, 2004, 33(2): 261-304
    [9]
    BELTRÁN M, GARCIA-BELLIDO J, LESGOURGUES J, et al. Bayesian model selection and isocurvature perturbations[J]. Physical Review D, 2005, 71(6): 063532 doi: 10.1103/PhysRevD.71.063532
    [10]
    TROTTA R. Applications of Bayesian model selection to cosmological parameters[J]. Monthly Notices of the Royal Astronomical Society, 2007, 378(1): 72-82 doi: 10.1111/j.1365-2966.2007.11738.x
    [11]
    ARREGUI I, RAMOS A A, DÍAZ A J. Bayesian analysis of multiple harmonic oscillations in the solar corona[J]. The Astrophysical Journal Letters, 2013, 765(1): L23 doi: 10.1088/2041-8205/765/1/L23
    [12]
    BRIDGES M, LASENBY A N, HOBSON M P. A Bayesian analysis of the primordial power spectrum[J]. Monthly Notices of the Royal Astronomical Society, 2006, 369(3): 1123-1130 doi: 10.1111/j.1365-2966.2006.10351.x
    [13]
    BALBI A, BRUNI M, QUERCELLINI C. ΛαDM: observational constraints on unified dark matter with constant speed of sound[J]. Physical Review D, 2007, 76(10): 103519 doi: 10.1103/PhysRevD.76.103519
    [14]
    NESSERIS S, GARCIA-BELLIDO J. Is the Jeffreys’ scale a reliable tool for Bayesian model comparison in cosmology[J]. Journal of Cosmology and Astroparticle Physics, 2013, 2013(8): 036 doi: 10.1088/1475-7516/2013/08/036
    [15]
    胡传鹏, 孔祥祯, WAGENMAKERS E J, 等. 贝叶斯因子及其在JASP中的实现[J]. 心理科学进展, 2018, 26(6): 951-965 doi: 10.3724/SP.J.1042.2018.00951

    HU Chuanpeng, KONG Xiangzhen, WAGENMAKERS E J, et al. The Bayes factor and its implementation in JASP: a practical primer[J]. Advances in Psychological Science, 2018, 26(6): 951-965 doi: 10.3724/SP.J.1042.2018.00951
    [16]
    MACKAY D J C. A practical Bayesian framework for backpropagation networks[J]. Neural Computation, 1992, 4(3): 448-472 doi: 10.1162/neco.1992.4.3.448
    [17]
    MACKAY D J C. Information-based objective functions for active data selection[J]. Neural Computation, 1992, 4(4): 590-604 doi: 10.1162/neco.1992.4.4.590
    [18]
    徐文耀, 区加明, 杜爱民. 地磁场全球建模和局域建模[J]. 地球物理学进展, 2011, 26(2): 398-415 doi: 10.3969/j.issn.1004-2903.2011.02.002

    XU Wenyao, OU Jiaming, DU Aimin. Geomagnetic field modelling for the globe and a limited region[J]. Progress in Geophysics, 2011, 26(2): 398-415 doi: 10.3969/j.issn.1004-2903.2011.02.002
    [19]
    SKILLING J. Nested sampling[J]. AIP Conference Proceedings, 2004, 735(1): 395-405
    [20]
    FEROZ F, HOBSON M P, BRIDGES M. MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics[J]. Monthly Notices of the Royal Astronomical Society, 2009, 398(4): 1601-1614 doi: 10.1111/j.1365-2966.2009.14548.x
    [21]
    BUCHNER J. Collaborative nested sampling: Big data versus complex physical models[J]. Publications of the Astronomical Society of the Pacific, 2019, 131(1004): 108005 doi: 10.1088/1538-3873/aae7fc
    [22]
    BUCHNER J. Nested sampling methods[OL]. arXiv preprint arXiv: 2101.09675, 2021
    [23]
    KASS R E, RAFTERY A E. Bayes factors[J]. Journal of the American Statistical Association, 1995, 90(430): 773-795 doi: 10.1080/01621459.1995.10476572
    [24]
    JEFFREYS H. The Theory of Probability[M]. 3 rd ed. Oxford: Oxford University Oxford, 1998
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(705) PDF Downloads(125) Cited by()
    Proportional views
    Related

    Journal Introduction

    ISSN 0254-6124       CN 11-1783/V

    Copyright © Editorial Office of Chinese Journal of Space Science.  京ICP备08100722号

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    x Close Forever Close

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return