Adjustment Computations, 6th Edition

CHAPTER 21BLUNDER DETECTION IN HORIZONTAL NETWORKS

21.1 INTRODUCTION

Up to this point, data sets were assumed to be free of blunders. However, when adjusting real observations, the data sets are seldom blunder free. Not all blunders are large, but no matter their sizes, it is desirable to remove them from the data set. In this chapter, methods used to detect blunders before and after an adjustment are discussed.

Many examples can be cited that illustrate mishaps that have resulted from undetected blunders in survey data. However, few could have been more costly and embarrassing than a blunder of about 1 mile that occurred in an early nineteenth-century survey of the border between the United States and Canada near the north end of Lake Champlain. Following the survey, construction of a US military fort was begun. The project was abandoned two years later when the blunder was detected, and a resurvey showed that the fort was actually located on Canadian soil. The abandoned facility was subsequently named “Fort Blunder!”

As discussed in previous chapters, observations are normally distributed. This means that occasionally, large random errors will occur. However, in accordance with theory, this seldom happens. Thus, large errors in data sets are more likely to be blunders than random errors. Common blunders in data sets include number transposition, entry and recording errors, station misidentifications, and others. When blunders are present in a data set, a least squares adjustment ...