A review on advancements in lithological map** utilizing machine learning algorithms and remote sensing data

MA El-Omairi, A El Garouani - Heliyon, 2023 - cell.com
Lithological map** is a fundamental undertaking in geological research, mineral resource
exploration, and environmental management. However, conventional methods for …

Map** of mineral resources and lithological units: A review of remote sensing techniques

R Rajan Girija, S Mayappan - … Journal of Image and Data Fusion, 2019 - Taylor & Francis
The remote sensing (RS) techniques have become a guiding and promising tool for mineral
exploration and map** of lithological units. The RS for mineral exploration begins with …

Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data

A Shebl, M Abdellatif, M Badawi, M Dawoud… - Scientific Reports, 2023 - nature.com
Integrating various tools in targeting mineral deposits increases the chance of adequate
detection and characterization of mineralization zones. Selecting a convenient dataset is a …

[HTML][HTML] Twenty years of ASTER contributions to lithologic map** and mineral exploration

M Abrams, Y Yamaguchi - Remote Sensing, 2019 - mdpi.com
The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five
instruments operating on the National Aeronautics and Space Administration (NASA) Terra …

[HTML][HTML] Stacked vector multi-source lithologic classification utilizing Machine Learning Algorithms: Data potentiality and dimensionality monitoring

A Shebl, Á Csámer - Remote Sensing Applications: Society and …, 2021 - Elsevier
Abstract Machine Learning Algorithms (MLAs) have recently introduced considerable
lithologic map**. Thus, this study scrutinizes the efficacy of Artificial Neural Network …

Advanced land imager superiority in lithological classification utilizing machine learning algorithms

A Shebl, T Kusky, Á Csámer - Arabian Journal of Geosciences, 2022 - Springer
Different types of remote sensing data are commonly used as inputs for lithological
classification schemes, yet determining the best data source for each specific application is …

Machine learning prediction of ore deposit genetic type using magnetite geochemistry

P Zhang, Z Zhang, J Yang, Q Cheng - Natural Resources Research, 2023 - Springer
Magnetite geochemistry is crucial for the discrimination of ore deposit genetic type.
Traditional two-dimensional discrimination diagrams based on particular data for limited …

Integrating remotely sensed and GIS data for the detailed geological map** in semi-arid regions: case of Youks les Bains Area, Tebessa Province, NE Algeria

F Tamani, R Hadji, A Hamad, Y Hamed - Geotechnical and Geological …, 2019 - Springer
Detailed geologic map** provides valuable informations about the spatial distribution of
lithological outcrops and lineaments; required to carry out the necessary investigations of …

Lithological discrimination and mineralogical map** using Landsat-8 OLI and ASTER remote sensing data: Igoudrane region, jbel saghro, Anti Atlas, Morocco

S Baid, A Tabit, A Algouti, A Algouti, I Nafouri, S Souddi… - Heliyon, 2023 - cell.com
This study aims to delineate the lithological formations, in addition to a mineralogical
map** comparing between the ASTER and Landsat-8 OLI sensors in the Igoudrane …

Data integration for lithological map** using machine learning algorithms

HS Manap, BT San - Earth Science Informatics, 2022 - Springer
The aim of this study is to compare and evaluate the performances of different classification
algorithms (Maximum Likelihood Classification [MLC], Random Forest [RF], Support Vector …