Well Log-Based Prediction of Elemental Composition: A Machine Learning Approach for Classifying Stratigraphic Members in the Epeiric Homoclinal Ramps
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Elemental composition prediction shows significant accuracy gains over traditional methods.
Classification results indicate over 85% accuracy in identifying stratigraphic members using machine learning techniques.
Machine learning strategies were employed to analyze well log data for accurate classification purposes.
Findings may enable better geological exploration and resource management in homoclinal ramps.
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Well Log-Based Prediction of Elemental Composition: A Machine Learning Approach for Classifying Stratigraphic Members in the Epeiric Homoclinal Ramps | Synapse