Empirical Method of Error Analysis in Volume Estimation: Monte Carlo Simulation Under Kriging and Triangulation

Abstract

Volume estimation packages often lack tools for measuring and analyzing the effect of input data uncertainty and possible error propagation in the volume estimation process. This paper proposes and develops an empirical procedure for error modeling in volume estimation using spatial simulation techniques. The procedure can be used to study the stability of interpolation methods in the presence of error, in other words finding a method that predicts best surface fit and volume estimates from imperfect data. The Monte Carlo simulation technique is used to construct an effective algorithm to study the effect of input data uncertainty on volumes estimated with perturbed Kriged surfaces and triangulated surfaces. The results are compared for consistency with a volume generated from a triangulated surface that holds the location points fixed.