Use of some Methods in Detecting Gross Errors in Surveying

Abstract

Most of the surveying tasks involve acquisition and analysis of measurements. The method of least squares estimation is commonly used to process the measurements. In practice, redundant measurements are made to provide quality control and check the presence of errors that might affect the results. Therefore, an insurance of the quality of these measurements is an important issue. Measurement errors of collected data have different levels of influence due to their number, measured accuracy and redundancy. The methods used in this study were to collect data from examples of typical surveying measurements to illustrate the least- squares adjustment of complex networks of data and statistical analysis of the results. This thesis explores the issue of gross error detection in surveying control networks. The methods of detecting these gross errors are used in conjunction with developed programs by the candidate to calculate probabilities and critical values for the distributions (in real time) rather than looking for them in statistical tables, and perform basic statistical analysis, as well as performing specific least-squares adjustment. The study of gross error’s detection form an interest of many researchers, besides the technological development in field instruments and processing techniques have become widely accepted over the last twenty years. Therefore, to investigate these, the candidate introduced three new methods, involving the Delta (Δ) Method for calculating the size of a gross error, Kappa (κ) Test for detecting single gross errors and location, and Absolute Delta (Δo) Method (A.D.M) for multiple gross errors detection and locations. The usage of (A.D.M) depends on redundancy of the system under the test. This puts a constrain on gross errors, in observed quantities that can be detected for the system to remain a strong system free of gross errors and at the same time reliable. The main conclusion reached is that the (A.D.M) is the most sensitive in detecting the presence of gross errors with the least number of redundant observations; therefore, it is the one recommended to be used in multiple gross error detection in surveying control networks. It is worth mentioning how to use the developed program is shown in Appendix B.