Distinguishing features from outliers in automatic Kriging-based filtering of MBES data: a comparative study

Multi beam echo sounding is the state of the art way for surveying sea bottoms. The sea floor elevation is obtained strip wise by measuring the time it takes for sound signals, emitted simultaneously in different directions, to travel to the sea bottom and back. We compare various ways of filtering erroneous soundings from MBES data sets, all based on Kriging. This research was initiated because of the problems a classic 1D cross validation method had with distinguishing blunders from features, like pipelines. We show that part of the problems can be solved by extending the 1D method to 2D and that most problems are solved by a robust, iterative filter method.