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A flexible true plurigaussian code for spatial facies simulations   总被引:3,自引:0,他引:3  
The current forms of plurigaussian simulation have serious limitations for applications to large numbers of geological facies, or units, which have complex contact relations. In this paper the authors present a true plurigaussian simulation (PGS) method, which can be applied in a simple way to any number of geological facies by using any number of Gaussians. A recursive technique is used for multi-dimensional integration of the Gaussian functions, which forms the major part of the PGS computation. A binary, dynamic contact matrix (DCM) is used to specify the contact relations among the facies; this method has proved to be simple, flexible and capable of dealing with general, complex contact relations. A method for incorporating into PGS multivariate correlations among any number of random variables is also included. A simulated example is used to demonstrate the application of the generalised PGS. This example shows that PGS is more robust to under-sampling than traditional direct indicator simulation.  相似文献
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The stochastic structure of images, especially individual medical images as they are reconstructed nowadays from arrays of medical imaging sensors, is becoming steadily better understood. Less attention has been paid to the parallel notion of estimation error for the deformations that convey relations among these images, such as localized abnormality or growth prediction. The dominant current formalism for the biostatistics of deformations deals solely with the shape of a set of landmarks parameterizing the deformation, not otherwise with its behaviour inbetween the landmarks. This paper attempts to fit a rigorous stochastic model for a deformation between landmarks and to assess the error of the fitted deformation. The relation between two images is modelled as a stochastic deformation, i.e. as an identity map plus a stochastic process whose value at every point is a vector-valued displacement. There are two common strategies for fitting deformations given information at a set of landmarks. One involves minimizing a roughness penalty, e.g. for a thin-plate spline, and another involves prediction for a stochastic process, e.g. for a self-similar intrinsic random field. The stochastic approach allows parameter estimation and confidence limits for the predicted deformation. An application is presented from a study of breast images and how they deform as a function of the imaging procedure.  相似文献
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