Recognition and early warning of deformation information in GNSS coordinate series based on the LWR-Pettitt method

doi:10.13474/j.cnki.11-2246.2021.0156

Abstract

Abstract: GNSS deformation monitoring data usually has no specific distribution characteristics, which is not conducive to the establishment of universal statistics. As a non-parametric test method, the Pettitt test is introduced to establish statistics of GNSS deformation monitoring data, and then the deformation information identification and early warning are carried out. However, the statistics constructed by the Pettitt test always have large fluctuation, and can only be used to identify the single deformation information. Therefore, a new method named LWR (Locally Weighted Regression)-Pettitt is proposed to optimize the statistics and threshold judgment method in the Pettitt test, and is used for GNSS deformation information identification and early warning in this paper. The experimental results show that the proposed method can identify the location of deformation information effectively, especially for trend-type deformation information, but for the abrupt-type deformation information, it can only identify the continuous deformation information more than two-fold standard deviation equivalent deformation.

Key words: GNSS, Locally Weighted Regression, Pettitt, deformation monitoring, deformation early warning

CLC Number:

P208

DUAN Wei, WANG Min, WU Hao, LIU Chao. Recognition and early warning of deformation information in GNSS coordinate series based on the LWR-Pettitt method[J]. Bulletin of Surveying and Mapping, 2021, 0(5): 124-128,154.

References

[1] 陈永奇, LUTES J. 单历元GPS变形监测数据处理方法的研究[J]. 武汉测绘科技大学学报, 1998, 23(4):324-328,363.
[2] IM S B, HURLEBAUS S, KANG Y J. Summary review of GPS technology for structural health monitoring[J]. Journal of Structural Engineering, 2013, 139(10):1653-1664.
[3] 姜卫平, 刘鸿飞, 刘万科, 等. 西龙池上水库 GPS 变形监测系统研究及实现[J]. 武汉大学学报(信息科学版), 2012, 37(8):949-952,1009.
[4] LEACH M P, HYZAK M. GPS structural monitoring as applied to cable-stayed suspension bridge[C]//Proceedings of the 20th FIG Congress. Melbourne:[s.n.], 1994.
[5] LOVSE J W, TESKEY W F, LACHAPELLE G, et al. Dynamic deformation monitoring of tall structure using GPS technology[J]. Journal of Surveying Engineering, 1995, 121(1):35-40.
[6] GUO J J, GE S J. Research of displacement and frequency of tall building under wind load using GPS[C]//Proceedings of the 10th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 1997). Kansas:[s.n.],1997.
[7] 刘超, 林鹏, 赵兴旺, 等. 矿山井塔GPS动态变形监测方法研究[J]. 中国安全科学学报, 2014, 24(9):109-115.
[8] 张玉祥, 王玉浚, 陆士良, 等. 巷道围岩非线性时空演变神经元网络预报模型[J]. 中国矿业大学学报, 1997, 26(4):8-13.
[9] 王尚庆, 徐进军, 罗勉. 三峡库区白水河滑坡险情预警方法研究[J]. 武汉大学学报(信息科学版), 2009, 34(10):1218-1221.
[10] 王涛, 田林亚, 孔令杰, 等. GPS高层建筑物动态特性的小波分析提取[J]. 测绘科学, 2018,43(4):100-104,108.
[11] MIAO C Q, WANG M, TIAN H J, et al. Damage alarming of long-span suspension bridge based on GPS-RTK monitoring[J]. Journal of Central South University, 2015, 22 (7):2800-2808.
[12] 王坚, 刘超, 高井祥, 等. 构筑物变形短期预警与完备性监测算法研究[J]. 武汉大学学报(信息科学版), 2011, 36(6):660-664.
[13] PETTITT A N. A non-parametric approach to the change-point problem[J]. Journal of The Royal Statistical Society Series C-Applied Statistics, 1979, 28(2):126-135.
[14] ROUGE C, GE Y, CAI X M. Detecting gradual and abrupt changes in hydrological records[J]. Advances in Water Resources, 2013, 53:33-44.
[15] 蔺鹏飞, 张晓萍, 刘二佳, 等. 黄土高原典型流域水沙关系对退耕还林(草)的响应[J]. 水土保持学报, 2015, 29(1):1-6.
[16] 王兴梅, 张勃, 张凯, 等. 石羊河流域极端干旱事件的时空变化特征[J]. 地理科学进展, 2011, 30(3):299-305.
[17] RYBSKI D, NEUMANN J. A review on the Pettitt test[M]//KROPP J, SCHELLWHUBER H J. In Extremis. Berlin:Springer, 2011:203-213.
[18] ZHANG S R, LU X X, HIGGITT D L, et al. Recent changes of water discharge and sediment load in the Zhujiang (Pearl River) Basin, China[J]. Global and Planetary Change, 2008, 60(3):365-380.
[19] CLEVELAND W S. Robust locally weighted regression and smoothing scatterplots[J]. Journal of the American Statistical Association, 1979, 74(368):829-836.
[20] CLEVELAND W S, DEVLIN S J. Locally weighted regression:an approach to regression analysis by local fitting[J]. Journal of the American Statistical Association, 1988, 83(403):596-610.
[21] VERBESSELT J, HYNDMAN R, NEWNHAM G, et al. Detecting trend and seasonal changes in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(1):106-115.
[22] VERBESSELT J, HYNDMAN R, ZEILEIS A, et al. Phenological change eetection while accounting for abrupt and gradual trends in satellite image time series[J]. Remote Sensing of Environment, 2010, 114(12):2970-2980.