In this work, we present a new multiple-point statistics (MPS) method combining the direct sampling algorithm and the use of multiple-resolution representations of the training image (TI) through Gaussian pyramids. First, the pyramid is built by applying convolution with a Gaussian-like kernel, which provides versions of the TI at lower resolutions. Then, successive MPS simulations are performed within a pyramid: 1) a simulation is done in the lowest resolution level, 2) the result is used to condition a simulation in the next (finer) level, and 3) this last step is repeated until the initial resolution is simulated. This technique allows to guide the MPS simualtions and to obtain results that better reproduce the spatial statistics of the TI, compared to the results of MPS without pyramids.


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