Results 261 to 270 of about 1,087,662 (308)

Estimation using multiple-point statistics

Computers & Geosciences, 2021
Abstract In the last two decades, several geostatistical simulation techniques have appeared that allow taking into account complex structural information, by quantifying a multiple-point statistical (MPS) model. The higher-order statistics are typically informed from a training image, sample model, or geological exposure.
Óli D. Jóhannsson, Thomas Mejer Hansen
openaire   +1 more source

Comparison of multiple point and statistical motor unit number estimation

Muscle and Nerve, 2000
This study compares two common techniques for motor unit number estimation, multiple point stimulation and statistical method, to determine which is more reproducible. Surface recorded motor unit action potentials (SMUPs) of the left hypothenar muscle group were measured on 20 controls and 10 ALS patients.
Catherine Lomen-Hoerth, Richard K Olney
exaly   +3 more sources

GPU-based SNESIM implementation for multiple-point statistical simulation

Computers and Geosciences, 2013
Among techniques applied to categorical variables simulation, multiple-point statistical simulation is widely used because of its non-iterative characteristic and powerful capability of curvilinear features reproduction. In current implementations, the multiple-point statistics (MPS) are inferred from the training image by storing all the observed ...
Detang Lu, Xue Li
exaly   +2 more sources

Pore space reconstruction using multiple-point statistics

Journal of Petroleum Science and Engineering, 2005
The reconstruction of porous media is of great interest in a wide variety of fields, including earth science and engineering, biology, and medicine. To predict multiphase flow through geologically realistic porous media it is necessary to have a three-dimensional (3D) representation of the pore space.
Hiroshi Okabe, Martin J Blunt
exaly   +2 more sources

Scaling multiple-point statistics to different univariate proportions

Computers & Geosciences, 2007
Multiple-point statistics are used in geostatistical simulation to improve forecasting of responses that are highly dependent on the reproduction of complex features of the phenomenon. Often, complex features cannot be captured by conventional two-point simulation methods, based on the variogram.
Julián M. Ortiz, Steven Lyster, Clayton V. Deutsch   +2 more
openaire   +1 more source

Indicator Simulation Accounting for Multiple-Point Statistics

Mathematical Geology, 2004
Geostatistical simulation aims at reproducing the variability of the real underlying phenomena. When nonlinear features or large-range connectivity is present, the traditional variogram-based simulation approaches do not provide good reproduction of those features.
Julián M. Ortiz, Clayton V. Deutsch
openaire   +1 more source

Reservoir Modeling Using Multiple-Point Statistics

SPE Annual Technical Conference and Exhibition, 2001
Abstract Two approaches are traditionally used to build numerical models for facies distributions within a reservoir. Pixel-based techniques aim at generating simulated realizations that honor the well data values, and reproduce a given variogram which models two-point spatial correlation.
Sebastien B. Strebelle, Andre G. Journel
openaire   +1 more source

Multiple-Point Statistics for Training Image Selection

Natural Resources Research, 2007
Selecting a training image (TI) that is representative of the target spatial phenomenon (reservoir, mineral deposit, soil type, etc.) is essential for an effective application of multiple-point statistics (MPS) simulation. It is often possible to narrow potential TIs to a general subset based on the available geological knowledge; however, this is ...
Jeff B. Boisvert, Michael J. Pyrcz, Clayton V. Deutsch   +2 more
openaire   +1 more source

Multiple-point statistics using multi-resolution images

Stochastic Environmental Research and Risk Assessment, 2020
Multiple-point statistics (MPS) is a simulation technique allowing to generate images that reproduce the spatial features present in a training image (TI). MPS algorithms consist in sequentially filling a simulation grid such that patterns around the simulated values come from the TI.
Julien Straubhaar, Philippe Renard, Tatiana Chugunova   +2 more
openaire   +1 more source