Multiple point statistical simulation using uncertain (soft) conditional data

TL;DR: A conditional MPS-based simulation method which considers the distribution characteristics of sample data adequately and avoids the excessive concentration of the nodes in a data event, which demonstrates their applicability in characterizing heterogeneous phenomena when faced with unevenly distributed conditioning data.

TL;DR: The comparative results indicated that the hybrid model of the direct sampling algorithm and convolutional neural network, which considered the uncertainty of evidence layers, achieved a higher success rate and prediction accuracy than the deterministic framework.

TL;DR: This study demonstrates a novel strategy for 3D MPS modelling characterized by realistic 3D training images, and an effective workflow for incorporating a diverse group of geological and geophysical data sets, and develops a novel method to effectively create observed sand/clay spatial trends.

TL;DR: In this paper, the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted, Denmark, using the snesim algorithm was studied.

TL;DR: In this paper, the authors present a set of programs that summarize data with histograms and other graphics, calculate measures of spatial continuity, provide smooth least-squares-type maps, and perform stochastic spatial simulation.

TL;DR: It is shown that in many problems, including some of the most important in practice, this ambiguity can be removed by applying methods of group theoretical reasoning which have long been used in theoretical physics.

TL;DR: In inverse problems, obtaining a maximum likelihood model is usually not sucient, as the theory linking data with model parameters is nonlinear and the a posteriori probability in the model space may not be easy to describe.

TL;DR: The inverse problem may be formulated as a problem of combination of information: the experimental information about data, the a priori information about parameters, and the theoretical information, and it is shown that the general solution of the non-linear inverse problem is unique and consistent.