Constrained indicator data resampling — A parameter constrained irregular resampling method for scattered point data

Abstract

Resampling of high-resolution data sets is often required for real-time applications in geosciences, e.g., interactive modeling and 3D visualization. To support interactivity and real-time computations, it is often necessary to resample the data sets to a resolution adequate to the application. Conventional resampling approaches create uniformly distributed results, which are not always the best possible solution for particular applications. I have developed a new resampling method called constrained indicator data resampling (CIDRe). This method results in irregular point distributions that are adapted to local parameter signal wavelengths of the given data. The algorithm identifies wavelength variations by analyzing gradients in the given parameter distribution. A higher point density is ensured in areas with larger gradients than in areas with smaller gradients, and thus the resulting data set shows an irregular point distribution. A synthetic data test showed that CIDRe is able to represent a data set better than conventional resampling algorithms. In a second application, CIDRe was used to reduce the number of gravity stations for interactive 3D density modeling, in which the resulting point distribution still allows accurate interactive modeling with a minimum number of data points.