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3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy
Planetary exploration depends heavily on 3D image data to characterize the static and dynamic properties of the rock and environment.
Digital Rock Typing DRT Algorithm Formulation with Optimal Supervised Semantic Segmentation
Each grid block in a 3D geological model requires a rock type that represents all physical and chemical properties of that block.
Morphology Decoder: A Machine Learning Guided 3D Vision Quantifying Heterogenous Rock Permeability for Planetary Surveillance and Robotic Functions
Permeability has a dominant influence on the flow properties of a natural fluid.
Deducing Optimal Classification Algorithm for Heterogeneous Fabric
For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose.
Machine Learning Guided 3D Image Recognition for Carbonate Pore and Mineral Volumes Determination
Notably, IROGA and MLDGRF have produced porosity results with an accuracy of 96. 2% and 97. 1% on the training set and 91. 7% and 94. 4% on blind test validation, respectively, in comparison with the three experimental measurements.
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