Volume 42 Issue 3
May  2023
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Liu Naizheng, Zhu Peimin, Du Liming. Three-dimensional gravity inversion based on improved FCM clustering algorithm[J]. Bulletin of Geological Science and Technology, 2023, 42(3): 338-349. doi: 10.19509/j.cnki.dzkq.tb20210606
Citation: Liu Naizheng, Zhu Peimin, Du Liming. Three-dimensional gravity inversion based on improved FCM clustering algorithm[J]. Bulletin of Geological Science and Technology, 2023, 42(3): 338-349. doi: 10.19509/j.cnki.dzkq.tb20210606

Three-dimensional gravity inversion based on improved FCM clustering algorithm

doi: 10.19509/j.cnki.dzkq.tb20210606
  • Received Date: 08 Mar 2022
  • In gravity inversion, traditional inversion methods usually generate smooth inversion results, that is, there are no obvious boundaries between different geological units. Fuzzy C-Means (FCM) algorithm is introduced into the inversion to solve the problem mentioned above to improve the accuracy and spatial resolution of inversion results. However, when the volume of an anomalous body is much smaller than that of the surrounding rock, and the weight coefficient of the FCM clustering term in the objective function is not selected properly, the algorithm is prone to cause uniform shrinkage of the anomaly inversion results, resulting in lower inversion accuracy, or even failure of the inversion.The main reason for the inversion failure is usually because the total volume of the anomalous bodies is much smaller than the volume of the surrounding rock.For this reason, in this paper, the scaling factor is introduced into the FCM clustering term of the objective function to balance the membership degree of the model parameters to each cluster, so as to reduce the influence of small anomalous body volume compared with the surrounding rock volume. By establishing a simple positive correlation between the scaling exponent ek and the distance snormal from the normalized clustering center and the real clustering center, the scaling factor ρk is continuously updated during the inversion process, which significantly reduces the difficulty in selecting the weight coefficient of the FCM clustering term in the objective function, and avoids the problem of volume shrinkage of the inverted anomalous bodies, thus enhancing the stability of the inversion. The numerical experiments of inversion with theoretical gravity anomaly data and actual data inversion show that the improved algorithm has higher inversion stability and accuracy compared with the previous FCM method.

     

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  • [1]
    Oldenburg D W. The inversion and interpretation of gravityanomalies[J]. Geophysics, 1974, 39(4): 526-536. doi: 10.1190/1.1440444
    [2]
    Li Y, Oldenburg D W. 3-D inversion of gravity data[J]. Geophysics, 1998, 63(1): 109-119. doi: 10.1190/1.1444302
    [3]
    于炳飞, 罗恒, 李端, 等. 金牛火山岩盆地重磁异常综合分析及找矿预测[J]. 地质科技通报, 2022, 41(3): 282-299. doi: 10.19509/j.cnki.dzkq.2021.0029

    Yu B F, Luo H, Li D, et al. Comprehensive analysis of gravity and magnetic anomalies in Jinniu volcanic basin for prediction of ore deposits[J]. Bulletin of Geological Science and Technology, 2022, 41(3): 282-299(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0029
    [4]
    马杰, 王万银, 纪晓琳. 利用重力场研究塞萨尔盆地及邻区构造特征[J]. 地质科技情报, 2019, 38(1): 285-294. https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201901033.htm

    Ma J, Wang W Y, Ji X L. Tectonic characteristics of Cesar Basin and its adjacent areas according to gravity field[J]. Geological Science and Technology Information, 2019, 38(1): 285-294(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-DZKQ201901033.htm
    [5]
    Portniaguine O, Zhdanov M S. Focusing geophysical inversion images[J]. Geophysics, 1999, 64(3): 874-887. doi: 10.1190/1.1444596
    [6]
    Zhdanov M S, Ellis R, Mukherjee S. Three-dimensional regularized focusing inversion of gravity gradient tensor component data[J]. Geophysics, 2004, 69(4): 925-937. doi: 10.1190/1.1778236
    [7]
    Zhdanov M S. New advances in regularized inversion of gravity and electromagnetic data[J]. Geophysical Prospecting, 2009, 57(4): 463-478. doi: 10.1111/j.1365-2478.2008.00763.x
    [8]
    Commer M. Three-dimensional gravity modelling and focusing inversion using rectangular meshes[J]. Geophysical Prospecting, 2011, 59(5): 966-979. http://publications.lbl.gov/islandora/object/ir:156804/datastream/PDF/view
    [9]
    Paasche H, Tronicke J, Holliger K, et al. Integration of diverse physical-property models: Subsurface zonation and petrophysical parameter estimation based on fuzzy C-means cluster analyses[J]. Geophysics, 2006, 71(3): 33-44. http://adsabs.harvard.edu/abs/2006Geop...71...33P
    [10]
    Lelièvre P G, Farquharson C G, Hurich C A. Joint inversion of seismic traveltimes and gravity data on unstructured grids with application to mineral exploration[J]. Geophysics, 2012, 77(1): K1-K15. http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SEGEAB000029000001001758000001&idtype=cvips&gifs=Yes
    [11]
    Sun J, Li Y. Joint inversion of multiple geophysical data: A petrophysical approach using guided fuzzy C-means clustering[M]//Anon. SEG Technical Program Expanded Abstracts 2012. [S. l. ]: Society of Exploration Geophysicists, 2012: 1-5.
    [12]
    Sun J, Li Y. Joint inversion of multiple geophysical data using guided fuzzy C-means clustering[J]. Geophysics, 2016, 81(3): 37-57. doi: 10.1190/geo2015-0457.1
    [13]
    Colombo D, Rovetta D. Coupling strategies in multiparameter geophysical joint inversion[J]. Geophysical Journal International, 2018, 215(2): 1171-1184. doi: 10.1093/gji/ggy341
    [14]
    Liu S, Jin S. 3-D gravity anomaly inversion based on improved guided fuzzy C-means clustering algorithm[J]. Pure and Applied Geophysics, 2020, 177(2): 1005-1027. doi: 10.1007/s00024-019-02306-0
    [15]
    Farquharson C G, Oldenburg D W. Non-linear inversion using general measures of data misfit and model structure[J]. Geophysical Journal International, 1998, 134(1): 213-227. doi: 10.1046/j.1365-246x.1998.00555.x
    [16]
    Peng G, Liu Z. 3D inversion of gravity data using reformulated Lp-norm model regularization[J]. Journal of Applied Geophysics, 2021: 104378. http://www.sciencedirect.com/science/article/pii/S0926985121001257
    [17]
    Kim H J, Kim Y H. A unified transformation function for lower and upper bounding constraints on model parameters in electrical and electromagnetic inversion[J]. Journal of Geophysics and Engineering, 2011, 8(1): 21-26. doi: 10.1088/1742-2132/8/1/004
    [18]
    Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973, 3(3): 32-57. doi: 10.1080/01969727308546046
    [19]
    Bezdek J C, Ehrlich R, Full W. FCM: The fuzzy C-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2/3): 191-203. http://www.u-aizu.ac.jp/course/bmclass/documents/FCM%20-%20The%20Fuzzy%20c-Means%20Clustering%20Algorithm.pdf
    [20]
    Pham D L. Spatial modelsfor fuzzy clustering[J]. Computer Vision and Image Understanding, 2001, 84(2): 285-297. doi: 10.1006/cviu.2001.0951
    [21]
    Davari A, Marhaban M H, Noor S B M, et al. Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering[J]. Fuzzy Sets and Systems, 2011, 163(1): 45-53. http://core.kmi.open.ac.uk/download/pdf/12226788.pdf
    [22]
    Li Y, Oldenburg D W. 3-D inversion of magnetic data[J]. Geophysics, 1996, 61(2): 394-408. http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SEGEAB000012000001000400000001&idtype=cvips&gifs=Yes
    [23]
    杜利明. 基于重磁联合反演的金岭矿田深部找矿预测[D]. 武汉: 中国地质大学(武汉), 2020.

    Du L M. Deep prospecting and reserve prediction of Jinling Orefield based on gravity and magnetic joint inversion[D]. Wuhan: China University of Geosciences(Wuhan), 2020(in Chinese with English abstract).
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