Monte Carlo Simulation for Outlier Identification Studies in Geodetic Network: An Example in A Levelling Network Using Iterative Data Snooping

Abstract

Today with the fast and powerful computers, large data storage systems and modern softwares, the probabilities distribution and efficiency of statistical testing algorithms can be estimated using computerized simulation. Here, we use Monte Carlo simulation (MCS) to investigate the power of the test and error probabilities of the Baarda’s iterative data snooping procedure as test statistic for outlier identification in the Gauss-Markov model. The MCS discards the use of the observation vector of Gauss-Markov model. In fact, to perform the analysis, the only needs are the Jacobian matrix; the uncertainty of the observations; and the magnitude intervals of the outliers. The random errors (or residuals) are generated artificially from the normal statistical distribution, while the size of outliers is randomly selected using standard uniform distribution. Results for simulated closed leveling network reveal that data snooping can locate an outlier in the order of magnitude 5σ with high success rate. The lower the magnitude of the outliers, the lower is the efficiency of data snooping in the simulated network. In general, considering the network simulated, the data snooping procedure was more efficient for α=0.01 (1%) with 82.8% success rate.