Gps Technology In The Application Of Slope Deformation Monitoring And Data Processing

Landslide hazard brings great harm to the safety of people’s life and property, therefore, it is of great significance to use technical means for slope monitoring as well as processing and analyzing the monitoring data collected. Due to the advantages of real-time, high precision, all-weather, continuous operation and high degree of automation, GPS is used in the field of slope deformation monitoring more and more widely. The measurements collected by GPS technology will inevitably be affected by undetectable gross errors. According to the robustness equation, this paper will use the theory of robust degree analysis to enhance the robustness of the GPS net-shape. At the same time use the method of maximum displacement vector to simplify the calculation procedure. New models of data processing built on combining the original GPS observation data can eliminate some relevant error in relative positioning. Therefore, depending on the basis of study and analysis on the error of GPS measurement, we can discuss the influence of combination form of GPS observations to the processing result, and then we can find out the optimal method of GPS observations through comparative test. Summarizing and analyzing the sources of GPS observations error. Trying to detect and handle systematic error and gross error that may appear in slope deformation monitoring system, this paper will study the characteristics of systematic error and gross error, as well as the influence on the results of finding and eliminating them. At the same time dealing with the multi-dimensional gross error. With the aid of the Excel component and the error processing software, this paper will do experiments to verify the effect of process. There is a complex non-deterministic, nonlinear relationship between slope deformation and the impact factors. This paper will use the wavelet toolbox in MATLAB on data de-noising of GPS deformation monitoring. Experimental results shows that wavelet analysis can well separate the influence of noise and improve the accuracy of monitoring results. Wavelet transform can effectively extract the local information of the data by multi-scale analysis. The neural network, as a kind of universal function approximator, has the advantage of learning, adaptive and fault-tolerant. These two methods have respective advantage in deformation analysis. The combination of the wavelet transform and neural network calls wavelet neural network. It has good time-frequency localization as wavelet transformation, and also has the self-learning function like traditional neural network. Learning and training this model continuously, it can be used as a kind of effective nonlinear combination tool to forecast deformation.