GNSS连续观测站构造运动变化特征自适应提取方法

doi:10.11947/j.AGCS.2023.20220675

摘要/Abstract

摘要： 随着GNSS连续观测站数据积累时长和空间密度的不断增加,坐标时间序列呈现出随时间变化的构造运动变化信息,因此基于现有函数模型对坐标时间序列建模已不具备普遍适用性。本文提出一种GNSS坐标时间序列中构造运动变化特征的自适应提取方法,以获取构造运动随时间变化特征。首先,构建表征GNSS坐标时间序列中构造运动随时间变化的改进函数模型,引入基于贝叶斯框架的马尔可夫链·蒙特卡洛算法自适应求解模型参数最优解及其误差;然后,试验模拟不同噪声水平和不同分段线性速率差值的坐标时间序列,分析函数模型中线性速率和时间节点参数的精度,结果表明在当前GNSS单日解水平分量定位精度下,水平分量线性速率精度为0.1 mm/a,可分辨出0.4 mm/a及以上随时间变化的差异性构造运动,时间节点的精度与分段线性速率差异值有关,差异值为0.4 mm/a时其精度为1.0 a;最后,利用青藏高原东北隅GNSS连续站观测数据对本文方法有效性进行验证,并在青藏高原东北隅最前沿区域提取出始于2017年东北向运动减缓的趋势性构造运动变化特征。

关键词: GNSS时间序列, 传统函数模型, 改进函数模型, 构造运动变化, 自适应提取

Abstract: With the increase of spatial density and time span of GNSS observations, more and more abundant information including the time-dependent tectonic deformation has been presented in GNSS coordinate time series, which makes the traditional function model of GNSS coordinate timeseries fitting no longer have universal applicability. To overcome this problem, we propose an adaptive algorithm to extract the characteristics of tectonic deformation in GNSS coordinate timeseries. Firstly, a modified functional model of coordinate time series is constructed to characterize the variation characteristics of tectonic motion of GNSS stations and the adaptive algorithm of Bayesian framework is introduced to solve the time node of linear rate change as well as the posterior probability density function of parameters to obtain the optimal solution of parameters. Secondly, the coordinate time series of different noise levels and different piecewise linear rate differences are simulated. The accuracy of the linear rate and time nodes are analyzed. The results show that with the current GNSS daily positioning accuracy for the horizontal component, the calculated linear rate accuracy is 0.1 mm/a, the tectonic motion of more than 0.4 mm/a can be distinguished. The resolution of the tectonic motion change time node is related to the difference value of the tectonic motion, and its accuracy is 1.0 year when the difference value is 0.4 mm/a. is 1.0 years. Finally, the effectiveness of the method is verified by using the GNSS continuous observations on the northeastern corner of the Tibetan Plateau. At the forefront of the northeastern corner of the Tibetan Plateau, the regional tectonic kinematic variation characteristics that occurred in 2017 and the obstruction of northeastward movement are extracted.

Key words: GNSS coordinate timeseries, traditional functional model, modified functional model, tectonic movement change, self-adaptive extraction

中图分类号:

P227

苏小宁, 石睿娟, 鲍庆华, 朱庆, 孟国杰, 闫浩文. GNSS连续观测站构造运动变化特征自适应提取方法[J]. 测绘学报, 2023, 52(8): 1245-1254.

SU Xiaoning, SHI Ruijuan, BAO Qinghua, ZHU Qing, MENG Guojie, YAN Haowen. Self-adaptive extraction method of tectonic movement change recorded by GNSS continuous observations[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1245-1254.

参考文献

[1] 党亚民,杨强,王伟,等. 基于块体模型的青藏高原及邻区地壳三维构造形变分析[J]. 测绘学报,2022,51(7):1192-1205. DOI:10.11947/j.AGCS.2022.20220123. DANG Yamin, YANG Qiang, WANG Wei, et al. Analysis on 3D crustal deformation of Qinghai-Tibet Plateau and its surrounding areas based on block model[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1192-1205. DOI: 10.11947/j.AGCS.2022.20220123.
[2] LIANG Shiming, GAN Weijun, SHEN Chuanzheng, et al. Three-dimensional velocity field of present-day crustal motion of the Tibetan Plateau derived from GPS measurements[J]. Journal of Geophysical Research: Solid Earth, 2013, 118(10): 5722-5732.
[3] 王阅兵,甘卫军,陈为涛,等.北斗导航系统精密单点定位在地壳运动监测中的应用分析[J]. 测绘学报,2018,47(1):48-56. DOI: 10.11947/j.AGCS.2018.20170147. WANG Yuebing, GAN Weijun, CHEN Weitao, et al. The analysis of precise point positioning of BeiDou navigation satellite system application in crustal motion monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 48-56. DOI: 10.11947/j.AGCS.2018.20170147.
[4] GAN Weijun, MOLNAR P, ZHANG Peizhen, et al. Initiation of clockwise rotation and eastward transport of southeastern Tibet inferred from deflected fault traces and GPS observations[J].GSA Bulletin, 2022, 134(5/6): 1129-1142.
[5] WILLIAMS S D P. Error analysis of continuous GPS position time series[J]. Journal of Geophysical Research, 2004, 109(B3): B03412.
[6] 杨兵, 杨志强, 田镇, 等. 联合EMD-HD和小波分解的GNSS坐标时间序列降噪分析[J]. 测绘学报, 2022, 51(9):1881-1889. DOI: 10.11947/j.AGCS.2022.20210175. YANG Bing, YANG Zhiqiang, TIAN Zhen, et al. Denoising analysis of GNSS coordinate time series by combining EMD-HD and wavelet decomposition[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(9):1881-1889.DOI: 10.11947/j.AGCS.2022.20210175.
[7] 姜卫平, 李昭, 刘鸿飞, 等. 中国区域IGS基准站坐标时间序列非线性变化的成因分析[J]. 地球物理学报, 2013, 56(7):2228-2237. JIANG Weiping, LI Zhao, LIU Hongfei, et al. Cause analysis of the non-linear variation of the IGS reference station coordinate time series inside China[J]. Chinese Journal of Geophysics, 2013, 56(7):2228-2237.
[8] 郝明, 王庆良. GNSS空间大地测量技术在中国大陆活动地块划分中的应用和研究进展[J]. 地震地质, 2020, 42(2):283-296. HAO Ming, WANG Qingliang. Progress in application of GNSS to division of active tectonic blocks in continental China[J]. Seismology and Geology, 2020, 42(2):283-296.
[9] 王鹏,刘静,刘小利,等. GNSS在地表过程研究中的应用[J]. 武汉大学学报(信息科学版),1-24.[2023-07-03]. http://doi.org/10.13203/j.whugis20220113. WANG Peng, LIU Jing, LIU Xiaoli, et al. Application of GNSS in the study of earth surface processes[J]. Geomatics and Information Science of Wuhan University,1-24.[2023-07-03]. http://doi.org/10.13203/j.whugis20220113.
[10] ZHANG Jie, BOCK Y, JOHNSON H, et al. Southern California permanent GPS geodetic array: error analysis of daily position estimates and site velocities[J]. Journal of Geophysical Research: Solid Earth, 1997, 102(B8): 18035-18055.
[11] MAO Ailin, HARRISON C G A, DIXON T H. Noise in GPS coordinate time series[J]. Journal of Geophysical Research: Solid Earth, 1999, 104(B2): 2797-2816.
[12] WANG Leyang, WU Qiwen, WU Fei, et al. Noise content assessment in GNSS coordinate time-series with autoregressive and heteroscedastic random errors[J]. Geophysical Journal International, 2022, 231(2): 856-876.
[13] 梅世蓉. 地震前兆场物理模式与前兆时空分布机制研究(三):强震孕育时地震活动与地壳形变场异常及机制[J]. 地震学报,1996,18(2):170-178. MEI Shirong. Study on the physical mode and spatial-time distribution mechanism of earthquake precursor field(3): abnormal phenomenon and its mechanism of earthquake activity and crustal deformation[J], Acta Seismologica Sinica, 1996,18(2):170-178.
[14] 顾国华, 王武星. GNNS观测与大地震前兆地壳形变研究进展[J]. 科技成果管理与研究, 2020,15(12):85-86. GU Guohua, WANG Wuxing.GNNS observation and research progress of crustal deformation before large earthquakes[J]. Management and Research on Scientific & Technological Achievements, 2020, 15(12):85-86.
[15] 吴云, 韩键, 于品清. 中长期地震危险区地壳形变场标志的研究[J]. 地壳形变与地震, 1994(2): 28-34. WU Yun, HAN Jian, YU Pinqing. Study on characteristics of crustal deformation field of seismic risk area in longintermediate period[J]. Crustal Deformation and Earthquake, 1994(2): 28-34.
[16] 杨建文, 张鹏映, 何应文, 等. 2014年云南地区GNSS最大剪应变格网时序异常与M≥6.0地震关系分析[J]. 大地测量与地球动力学, 2020, 40(5): 446-451. YANG Jianwen, ZHANG Pengying, HE Yingwen, et al. Analysis of the relationship between GNSS maximum shear strain grid time series anomaly and M≥6.0 earthquakes in Yunnan region, 2014[J]. Journal of Geodesy and Geodynamics, 2020, 40(5): 446-451.
[17] CHENC H, SU X, CHENG K C, et al. Seismo-deformation anomalies associated with the M6.1 Ludian earthquake on August 3, 2014[J]. Remote Sensing, 2020, 12(7): 1067.
[18] CHENC H, LIN L C, YEH T K, et al. Determination of epicenters before earthquakes utilizing far seismic and GNSS data: insights from ground vibrations[J]. Remote Sensing, 2020, 12(19): 3252.
[19] 顾国华, 王武星. 2016年新西兰7.8级大地震GPS观测结果与弹性回跳模型[J]. 武汉大学学报(信息科学版), 2017, 42(11): 1673-1680. GU Guohua, WANG Wuxing. Results of GPS observations for M7.8 earthquake in 2016 in New Zealand and discussion on elastic rebound model[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11): 1673-1680.
[20] 顾国华. 2016年日本九州岛7.3级地震前及同震地壳运动[J]. 地震, 2017, 37(3): 28-37. GU Guohua. Pre-and Co-seismic crustal movements of the 16 April, 2016 Kyushu M7.3 earthquake, Japan[J]. Earthquake, 2017, 37(3): 28-37.
[21] WANG Leyang, SUN Longxiang, XU Guangyu. An improved Bayesian von Karman regularization method for the joint inversion of GNSS and InSAR data[J]. Journal of Surveying Engineering, 2023,149(1):04022016.
[22] WANG Leyang, SUN Longxiang, XU Guangyu.An improved Bayesian von Karman regularization method for the joint inversion of GNSS and InSAR data[J]. Journal of Surveying Engineering,2022,148(2): 04021032.
[23] GOODMAN J, WEARE J. Ensemble samplers with affine invariance[J]. Communications in Applied Mathematics and Computational Science, 2010, 5(1): 65-80.
[24] SU Xiaoning, YAO Lianbi, WU Weiwei, et al. Crustal deformation on the northeastern margin of the Tibetan Plateau from continuous GPS observations[J]. Remote Sensing, 2018, 11(1): 34.
[25] 王琪, 牛之俊, 石俊成. 中国地壳运动观测网络基本网观测精度研究[J]. 大地测量与地球动力学, 2003, 23(3): 9-13. WANG Qi, NIU Zhijun, SHI Juncheng. Observation precision of basic network of CMONOC[J].Journal of Geodesy and Geodynamics, 2003, 23(3): 9-13.
[26] 甘卫军,李强,张锐,等. 中国大陆构造环境监测网络的建设与应用[J]. 工程研究:跨学科视野中的工程,2013,4(4):324-331. GAN Weijun, LI Qiang, ZHANG Rui. Construction and application of tectonic and environmental observation network of mainland China[J]. Journal of Engineering Studies, 2013, 4(4): 324-331.
[27] HERRING T A, KING R W, MCCLUSKY S C. GLOBK reference manual: global Kalman filter VLBI and GPS analysis program, release 10.6[R]. Massachusetts Institute of Technology:Cambridge, MA,2015.
[28] HERRING T A, KING R W, MCKLUSKY S C. GAMIT reference manual: GPS analysis at MIT, Version 10.7[R]. Massachusetts Institute of Technology:Cambridge, MA, 2018.