this dataset is used to evaluate the performance of lithology identification in cross-well manner. There are 12 wells with same logs and 5 lithologies: mudstone, siltstone, muddy siltstone, silty muds
The dataset consists of three basic lithology categories, each represented by a separate folder. Within each folder, multiple CSV files correspond to different porosity levels. Each CSV file contains
The result of this classification is a predictive lithology map classified using machine learning random forest. The data used are Sentinel-2A satellite imagery, ALOS PALSAR satellite imagery, DEM, an
Lithologic segmentation process carried out by drawing poligon lines from the boundary lines manually. After creating and editing poligon_jeo shape file, type and quality score of lithologic formation
The density of rocks depends on the mineral contents and is therefore a helpful property for the discrimination of lithological units. It is also used for the calculation of...