Interpolation method based on pattern-feature correlation

Abstract

Reasonable low-wavenumber initial models are essential for reducing the nonuniqueness of seismic inversion. A traditional approach to estimate the low-wavenumber models of elastic parameters is well-log interpolation. However, complex geologic structures decrease the accuracy of this method. To overcome these challenges in building prior models, we have developed an interpolation method based on pattern-feature correlation (PFC) inspired by multiple-point geostatistics (MPG). In our interpolation method, we scan a stacked seismic profile using a predefined data template to obtain a geologic pattern around each node in the seismic profile. Each pattern is then converted into several filter scores with the filters defined in the MPG algorithm of the filter-based simulation. We calculate the correlation coefficients of the filter scores among different patterns for the various nodes and define them as PFCs. We construct the initial models from well-log data based on the weighted interpolation method, in which the weighting factors are precisely determined by the PFCs. We build the initial models using our method for synthetic and field data to demonstrate its effectiveness. To verify the validity of the initial models, we apply them to Bayesian linearized inversion. The accuracy of the interpolation and inversion results verifies the excellent performance of our interpolation method. Our method provides a novel and convenient approach that combines seismic and well-log data, which contributes to seismic exploration and geologic modeling.