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Machine learning methods and applications integrate large sets of information with the goal of efficient pattern recognition and the capability of leveraging accurate decision making.The objective of this study is to apply machine learning methods to the supervised classification of lithologies using multivariate log parameter data from.
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But training a machine vision system to distinguish between a cliff, a boulder, and a plateau of some kind is much harder.Current machine vision systems are fed data about paved roads that have stop signs and traffic lights, not cliffs one could drive off of or giant boulders lying in the middle of the sand.
Using Artificial Neural Networks To Scientific Reports
Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem.Given the potential for currently stabilised dune systems to.
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Machine Learning Applied To Geophysical Well Log Data
The individual sand beds encountered in this well ranges from 10150 ft 3.0445.72m separated by thick or thin shale beds.The reservoir quality of the sand is measure by clay percent.The best reservoirs have lowest clay content.It would be great if lithology can be classified based on clay content.
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