STAR*NET V6 - Circe

6.4 Blunder Detect
Chapter 6 Running Adjustments
This is a modified form of adjustment that can be used to find various types of blunders
in your input data file. Choose Run>Blunder Detect to run this routine. Blunder Detect
is never used in place of a normal adjustment, it is a data debugging tool only! When you
run Blunder Detect, a modified version of the listing file is generated. Your listing file
will contain a section entitled “Differences from Observations” for each data type. In the
example shown below, the best fit angles are listed in the Angle column, followed by
their differences from the input values. The largest difference is listed at the end of each
data type. Or, if you have chosen in the listing options to have output observations sorted
by residual size, your output will be sorted by observations having the largest differences
shown first.
Differences From Observations
=============================
Differences from Observed Angles
At From To Angle Difference
3 4 5 153-38-56.31 1-00-17.19
9 1 7 159-51-49.11 0-00-00.76
11 10 12 4-59-09.78 -53-00-35.34
15 14 16 42-48-09.82 0-00-05.68
Largest Difference from Observed Angle
11 10 12 4-59-09.78 -53-00-35.34
Blunder Detect
Blunder Detect functions by performing several iterations, successively deweighting
observations that do not fit into the network in an attempt to isolate those observations
that contain blunders. The routine works best in a situation where you have a strong,
over-determined network. This means that you should have as many redundant distances
and angles as possible. The Blunder Detect routine will be of little or no help in
determining blunders in simple traverses. To utilize Blunder Detect is a strong argument
for collecting additional field data wherever possible. If you can tie across traverses to
another stations, or observe angles to distant stations in the network, your chances of
detecting gross errors are much improved. Of course, you will also improve the
adjustment results.
Using the Blunder Detect routine can be very effective under certain circumstances in
locating multiple gross errors in your input data. However it is not useful in analyzing
survey networks that contain the normal small random errors present in all field
measurements. You should not run Blunder Detect “just to see if things are OK”,
because it may give you misleading indications of problems when there are none. You
should reserve its use for those cases where you have a number of large residuals in your
input data you cannot account for.
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