Modeling and fitting mineral microstructures by multinary random fields


Modeling and fitting mineral microstructures by multinary random fields

Teichmann, J.; Menzel, P.; Heinig, T.; van den Boogaart, K. G.

Modeling mineral microstructures is of high importance in geostatistics in order to render realistic geological patterns. An appropriate model should be applicable to varying microstructures and account for correlations within the facies, i.e., the shape and size of the grains as well as for dependencies between the facies, e.g., facies A lies within facies B, or facies A and B are not connected. This allows to simulate the geometry of a microstructure in combination with other microstructural properties like mineralogy, crystall lattice orientation, (locally varying) chemical composition, inclusions, grain boundaries, subgrain boundaries and defects.

The common plurigaussian method, a valuable approach in geostatistics, can account for correlations within each facies and in principle be extended to correlations between the facies. Founded on particular case of this model, formulas for first- and second-order characteristics, such as volume fraction, correlation function and cross-correlation function can be given by a multivariate normal distribution, which makes model fitting more feasible. Based on first- and second-order statistics which can easily
be estimated by convolution, model fitting requires only numerical inversion of several one-dimensional monotone functions in this model.

The applicability is demonstrated for the two-dimensional case by modeling the microstructure
from a Mineral Liberation Analyzer image data set and evaluated by a deviation test.

  • Contribution to proceedings
    18th Annual Conference IAMG 2017, 02.09.2017, Perth, Australia

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Publ.-Id: 25153