| Surface collapse has become one of the most common and widespread disasters due to human activities and natural environmental impact.Once deformation occurs,large displacement will occur in a short time,which will bring huge economic and human losses.It is necessary to monitor the ground subsidence in real time to prevent accidents.At present,the monitoring methods are mostly as follows: 1.Monitoring with traditional monitoring instruments(such as leveling and total station);2.Static measurement using Global Navigation Satellite System(GNSS)technology;3.Micro-motion or change measurement using differential interferometric radar(D-InSAR)technology.These methods have some problems,such as too long measuring period and insufficient realtime performance,and can not monitor the collapse area of rapid settlement.The dynamic measurement using GNSS technology has characteristic of short measurement period and the real-time requirement is satisfied.However,due to the influence of various factors,the observation value may contain larger observation error.In order to eliminate gross errors and weaken the influence of accidental errors in the height sequence of GNSS dynamic deformation monitoring,a theoretical study on settlement monitoring in fast subsidence area is carried out in this paper.The research results include the following aspects:1.The existing Kalman filtering algorithms are not suitable for the processing of monitoring data in fast subsidence area.When using GNSS dynamic measurement technology to monitor the rapid settlement area,the observed values may contain gross errors.At the same time,because of the two motion states of stable settlement and rapid settlement,the motion model also changes greatly.Robust Kalman filter can eliminate the gross errors in the observed values,but cannot correct the model errors;the adaptive Kalman filter can correct the model errors,but cannot resist the measurement gross errors;the robust adaptive Kalman filter,the combination of the robust Kalman filter and the adaptive Kalman filter cannot effectively deal with the different motion states in the fast subsidence area,so a new filtering algorithm is needed.2.A robust adaptive Kalman filtering scheme for monitoring data processing in fast subsidence area is proposed.Catastrophe point test is used to identify stable subsidence and fast subsidence,to correct model errors and resist gross errors of measurement,and to improve the overall filtering accuracy.By calculating a real project data of subsidence monitoring in a mining area,the filtering scheme designed in this paper could effectively reduce the influence of model errors and observation anomalies on the filtering results,and also maintained a high accuracy when rapid subsidence occurred.The maximum difference between filtering value and true value is no more than 3.5mm,and the posterior median error is σ=1.80 mm.3.An automatic monitoring data processing software was developed based on C # language.The following functions are realized: the real-time data are sent back to the server through the Internet;the data are solved on the server automatically;threedimensional coordinates of each monitoring terminal are stored in the database;the settlement results of monitoring points are drawn into a broken line chart for users to view. |
