Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

GNSS receiver autonomous integrity monitoring(RAIM)algorithm based on robust estimation

Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.

BDS/GPS deformation analysis of a long-span cable-stayed bridge based on colored noise filtering

Combining GPS and BDS technology to monitor the deformation of long-span railway bridges with stricter deformation control requirements is of significance to the safety and control of the bridge and the safety of railway traffic.Previous studies have ignored the influence of coloured noise in the deformation time series.This is not conducive to accurate deformation analysis of long-span railway bridges.Therefore,GPS,BDS and GPS/BDS monitoring data of Ganjiang Bridge located in Ganzhou city,Jiangxi Province,China are adopted in this paper to filter the coloured noise in the deformation time series by principal component analysis(PCA),and the influence of coloured noise on the deformation analysis results of railway bridge is analysed.The experimental results show that the diurnal temperature difference causes the mid-span and the tower of the railway cable-stayed bridge to deform with a period of about one day in the vertical and longitudinal directions,respectively.Ignoring colored noise will make the uncertainty of the deformation parameter estimation overly optimistic.PCA can significantly reduce the coloured noise,and thus reduce the uncertainty of deformation parameter estimation by about 73%.Moreover,the average difference between the daily periodic motion amplitudes of the monitoring points obtained by using GPS and BDS deformation time series is 1.65 mm.The use of GPS/BDS deformation time series is not only helpful to reduce the influence of coloured noise,but also can reduce the difference between amplitude analysis results obtained from GPS and BDS deformation time series.