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Description
Data Fitting with Nonstationary Statistics (October 2018)Image building in a cartesian space requires residuals transformed to uniformity in both physical space and fourier space. I provide both theory and pseudocode for a wide range of circumstances. In one widely studied industrial test (Marmousi) these methods draw nearly double the information from the data. The reason for such success is the absence of reliance on statistical stationarity.
Image building in a cartesian space requires residuals transformed to uniformity in both physical space and fourier space. I provide both theory and pseudocode for a wide range of circumstances. In one widely studied industrial test (Marmousi) these methods draw nearly double the information from the data. The reason for such success is the absence of reliance on statistical stationarity.
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