Volcanic Lithology Logging Identification Based on ADASYN-KNN-Random Forest Ensemble Model Taking the Carboniferous System on the Hanging Wall of Kebai Fault Zone as an Example

This dataset contains the following files to support the research paper "Volcanic Lithology Logging Identification Based on ADASYN-KNN-Random Forest Ensemble Model Taking the Carboniferous System on the Hanging Wall of Kebai Fault Zone as an Example":

Raw_Data.xlsx:

Thin-Section Data: High-resolution measurements/images of volcanic rock samples with lithology labels (e.g., basalt, andesite).

Logging Data: Corresponding well-logging responses (gamma ray, density, neutron porosity) for each sample.

Columns: Sample_ID, Depth (m), GR (API), DEN (g/cm³), CNL (%), Lithology_Label, Mineral_Composition (%).

ADASYN_Resampled_Data.xlsx:

Balanced dataset generated after applying ADASYN (Adaptive Synthetic Sampling) oversampling to address class imbalance.

Includes synthetic samples for minority lithology classes.

ML_Code.zip:

ADASYN_Oversampling.py: Python script for adaptive oversampling (uses imbalanced-learn).

KNN_RF_Classification.py: Combined script for KNN and Random Forest training/prediction.

Requirements.txt: Dependencies (e.g., Python 3.13, pandas, scikit-learn).