A new leveling method without the direct use of crossover data and its application in marine magnetic surveys: Weighted spatial averaging and temporal filtering

Abstract

The author has developed a new leveling method for use with magnetic survey data, which consists of adjusting each measurement using the weighted spatial average of its neighboring data and subsequent temporal filtering. There are two key parameters in the method: the 'weight distance' represents the characteristic distance of the weight function and the 'filtering width' represents the full width of the Gaussian filtering function on the time series. This new method was applied to three examples of actual marine survey data. Leveling using optimum values of these two parameters for each example was found to significantly reduce the standard deviations of crossover differences by one third to one fifth of the values before leveling. The obtained time series of correction values for each example had a good correlation with the magnetic observatory data obtained relatively close to the survey areas, thus validating this new leveling method.