The importance of data QC even on FINAL data is shown in this example. Data that should’ve been removed from the raw data was processed and created artifacts in the final data. It would be nice if we could automate everything, but there are many times when an experienced data reviewer may recognize something suspicious in a data set. Ideally, we like to find these issues during data acquisition as they are easier to fix. However, on occasion a data review of final data will come across issues needing attention. In this example strange responses in the final magnetic data (bottom profiles) were traced back to data points in the raw magnetic data (upper profiles) that were not removed. Subsequent data interpolation and filtering turned these data points into false responses. We tend to rely heavily on gridded data images, but there is still tremendous value in the original profile data which have the advantage of being higher resolution than interpolated gridded data sets.
