Sentence examples for multiple point statistics from inspiring English sources

The rock-physics transforms provide soft data for multiple point statistics (MPS) simulations.

Conditional expectation theory and projections in Hilbert spaces; parametric versus non-parametric geostatistics; Boolean, Gaussian, fractal, indicator, and annealing approaches to stochastic imaging; multiple point statistics inference and reproduction; neural net geostatistics; Bayesian methods for data integration; techniques for upscaling hydrodynamic properties.

The two systems are reconstructed in two ways: (I), the matrix-vug and matrix-fracture systems are generated by improved simulated annealing method (ISAM) and multiple point statistics algorithm (MPS), respectively; (II), both of the matrix-vug and matrix-fracture systems are generated by MPS.

Training images can provide multiple-point statistics which describe the statistical relation between multiple spatial locations considered jointly.

Thin-section images can provide multiple-point statistics, which describe the statistical relation between multiple spatial locations.

The selection of multiple-point statistics is a key issue and is discussed in detail.

Various heterogeneity descriptions ranging from multi-Gaussian fields to fields with complex multiple-point statistics inferred from outcrops are considered.

In this study, multiple-point statistics (MPS) was applied to reproduce karst features and to improve groundwater flow modeling.

The multiple-point statistics are borrowed from a training image that is designed to reflect the prior geological conceptualization.

Stochastic reservoir simulation then consists of anchoring the borrowed geo-structures in the form of multiple-point statistics to the actual subsurface hard and soft data.

In this paper, we propose the use of multiple-point statistics simulation to integrate the time series of reliable evaporation estimates.