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- W3210198314 abstract "A method (Ember) for nonstationary spatial modeling with multiple secondary variables by combining Geostatistics with Random Forests is applied to a three-dimensional Reservoir Model. It extends the Random Forest method to an interpolation algorithm retaining similar consistency properties to both Geostatistical algorithms and Random Forests. It allows embedding of simpler interpolation algorithms into the process, combining them through the Random Forest training process. The algorithm estimates a conditional distribution at each target location. The family of such distributions is called the model envelope. An algorithm to produce stochastic simulations from the envelope is demonstrated. This algorithm allows the influence of the secondary variables, as well as the variability of the result to vary by location in the simulation." @default.
- W3210198314 created "2021-11-08" @default.
- W3210198314 creator A5063853046 @default.
- W3210198314 date "2021-04-15" @default.
- W3210198314 modified "2023-09-23" @default.
- W3210198314 title "presentation1_An Application of an Embedded Model Estimator to a Synthetic Nonstationary Reservoir Model With Multiple Secondary Variables.pdf" @default.
- W3210198314 doi "https://doi.org/10.3389/frai.2021.624697.s001" @default.
- W3210198314 hasPublicationYear "2021" @default.
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