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顾及随机特征的GNSS连续站坐标时间序列建模方法

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GNSS观测技术为研究板块运动、地震同震和震后形变以及无震慢滑移等地壳物理现象提供了重要的观测数据,其中的关键步骤是在充分考虑时间序列随机特征的基础上,精准提取时间序列的构造运动信息和非构造运动信息.本文提出一种基于贝叶斯框架的GNSS连续站坐标时间序列建模方法,顾及随机特征精准提取出模型参数的最优解及其误差.首先,引入基于贝叶斯框架的参数解算方法,获得模型参数的最优解及误差;其次,系统分析观测数据噪声随机特征和模型参数随机特征对模型解算结果的影响;最后,将该方法应用于青藏高原东北隅GNSS观测数据构造运动与非构造变形的有效提取.相比基于最小二乘拟合的GNSS连续站坐标时间序列建模传统方法,本文引入基于贝叶斯框架的参数最优解解算的GWMCMC算法,顾及随机特征精准高效提取出GNSS坐标时间序列中测站的构造运动趋势与非构造运动信息,能够更加准确获取参数的最优解及误差.同时相较传统的MCMC算法,GWMCMC算法通过并行计算改进算法的性能并提高了计算效率.本文的研究成果为后续利用构造变形开展地壳形变运动学特征和动力学机制提供数据支撑.
Modeling method of GNSS continuous station coordinate time series considering random characteristics
GNSS technology provides important observations for the study of crustal physical phenomena such as plate motion,co-seismic and post-seismic deformation,and slow slip without earthquake,among which the key step is to accurately extract the tectonic and non-tectonic movement information of time series fully considering the random characteristics of time series.In this paper,we proposed a modeling method of GNSS continuous station coordinate time series based on Bayesian framework to accurately extract the optimal solution and error of model parameters.The optimal solution and error of the model parameter were obtained from the parameters were obtained from the parameter calculation method based on Bayesian framework.While,the influence of the random characteristics of the model parameters and observation on the model solution results is systematically analyzed.Then,we applied the method to the northeastern corner of the Qinghai-Tibet Plateau,the extraction of the constructive and non-constructive deformations of the GNSS data was effective.Compared with the traditional method of GNSS continuous station coordinate time series modeling based on least squares fitting,this paper introduces GWMCMC algorithm,a parameter optimal solution method based on Bayesian framework,which takes into account the random characteristics to precisely and efficiently extract the tectonic movement trend and non-tectonic movement,and can more accurately obtain the optimal solution of the parameter and their error.Meanwhile,compared with the traditional MCMC algorithm,the GWMCMC algorithm improves its performance and computational efficiency through parallel computing.The research results of this paper provide data support for the subsequent use of tectonic deformation to carry out the kinematic characteristics and dynamic mechanism of crustal deformation.

GNSS coordinate time seriesBayesian frameworkAccurate extractionTectonic movementNon-structural deformation

石睿娟、苏小宁、鲍庆华、李毓照

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兰州交通大学测绘与地理信息学院,兰州 730070

GNSS坐标时间序列 贝叶斯框架 精准提取 构造运动 非构造变形

国家自然科学基金国家自然科学基金甘肃省杰出青年基金兰州市人才创新创业项目兰州交通大学青年科学基金甘肃省教育厅高等学校教师创新基金

421740034160400722JR5RA3152023-RC-3120210032023A-035

2024

10.6038/cjg2023R0435

地球物理学报
中国地球物理学会 中国科学院地质与地球物理研究所

地球物理学报

CSTPCD北大核心
影响因子：3.703
ISSN：0001-5733
年,卷(期)：2024.67(6)
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