Application of GNSS-PPP on Dynamic Deformation Monitoring of Offshore Platforms

The real-time dynamic deformation monitoring of offshore platforms under environmental excitation is crucial to their safe operation.Although Global Navigation Satellite System-Precise Point Positioning(GNSS-PPP)has been considered for this purpose,its monitoring accuracy is relatively low.Moreover,the influence of background noise on the dynamic monitoring accuracy of GNSS-PPP remains unclear.Hence,it is imperative to further validate the feasibility of GNSS-PPP for deformation monitoring of offshore platforms.To address these concerns,vibration table tests with different amplitudes and frequencies are conducted.The results demonstrate that GNSS-PPP can effectively monitor horizontal vibration displacement as low as±30 mm,which is consistent with GNSS-RTK.Furthermore,the spectral characteristic of background noise in GNSS-PPP is similar to that of GNSS-RTK(Real Time Kinematic).Building on this observation,an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)has been proposed to de-noise the data and enhance the dynamic monitoring accuracy of GNSS-PPP.Field monitoring application research is also undertaken,successfully extracting and analyzing the dynamic deformation of an offshore platform structure under environmental excitation using GNSS-PPP monitoring in conjunction with improved CEEMDAN de-noising.By comparing the de-noised dynamic deformation trajectories of the offshore platform during different periods,it is observed that the platform exhibits reversible alternating vibration responses under environmental excitation,with more pronounced displacement deformation in the direction of load action.The research results confirm the feasibility and potential of GNSS-PPP for dynamic deformation monitoring of offshore platforms.

黄土高原植被生长和气候变化的多尺度分析

黄土高原的气候特征具有明显的时空差异,生态环境脆弱,因此研究黄土高原地区植被生长与气候之间的关系对改善黄土高原的生态环境至关重要。植被指数和气候因子的长期变化均是非平稳、非线性的,包含不同的频率信息,具有不同时间尺度上的变化。因此,采用改进的具有自适应噪声的完整集合经验模式分解算法(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,Improved CEEMDAN)对NDVI和气候(降水、温度)长时间序列进行分解,在不同尺度下分别进行回归分析,从而揭示植被生长与气候变化之间的规律。结果表明:采用Improved CEEMDAN方法对不同的数据集分解得到的分量具有不同的特性,气候因素对植被生长过程的影响不是单一尺度的,而是多尺度的,在不同尺度下,气候变化对植被生长的影响是有差别的。总体上,温度变化对植被生长过程的影响大于降水。利用多尺度分析方法可以揭示不同尺度下植被生长与气候之间的联系,为探究植被与气候等因素的关系提供了新的思路。