This paper compares different results for overburden volume estimation
assisted by geoestatistics, particularly using kriging methods. The main
goal is to obtain the most reliable digital terrain model for the calculation
of the overburden volume. The data was collected on an irregularly
spaced grid and interpolated values were calculated to provide a denser,
more regular data set before volume estimation. The field work was
developed at the Butia-Leste Coal Mine, southern most Brazil, where an
accurate survey was done using a total station theodolite with electronic
data collector. The study was developed in five main steps: data
acquisition, exploratory data analysis, spatial continuity measurements,
point and block kriging estimation and verification of the results. Data
acquisition was carried out on a 5 x 5 in grid. This dense grid provides an
exhaustive data set of the real terrain against which data subsets can be
compared for the various interpolation procedures and volume
calculation. This provides an interesting insight into the influence of the
number of data on the estimation error. Univariate statistics were used to
compare each data subset and the exhaustive dataset. Ordinary kriging on
the data subsets followed by contouring was carried out using Geoeas and
Surfer software respectively. A subroutine written in C provided the link
between the files generated in Geoeas and subsequent contouring routines
embedded into the Surfer package. Omnidirectional and directional
variograms were modelled defining the parameters for kriging estimation
using Geoeas. Different variogram models were checked through cross
validation prior to kriging. A comparison between the magnitude of the
differences in volumes calculated with the different DTMs was attained.
These final result indicates the sensitivity of the volume obtained to the
number of data points. As the number of data points increases the
estimation error decreases, but at some point there is no significant
decrease in the final volume with the collection of additional data.