Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
V Rodriguez-Galiano, M Sanchez-Castillo… - Ore Geology …, 2015 - Elsevier
Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …
Multi-and hyperspectral geologic remote sensing: A review
Geologists have used remote sensing data since the advent of the technology for regional
map**, structural interpretation and to aid in prospecting for ores and hydrocarbons. This …
map**, structural interpretation and to aid in prospecting for ores and hydrocarbons. This …
Potential of ESA's Sentinel-2 for geological applications
Sentinel-2 is ESA's medium spatial resolution (10–60 m) super-spectral instrument aimed at
ensuring data continuity for global land surface monitoring of Landsat and SPOT. Several …
ensuring data continuity for global land surface monitoring of Landsat and SPOT. Several …
Sentinel-2A MSI and Landsat 8 OLI provide data continuity for geological remote sensing
Sentinel-2A MSI is the Landsat-like spatial resolution (10–60 m) super-spectral instrument of
the European Space Agency (ESA), aimed at additional data continuity for global land …
the European Space Agency (ESA), aimed at additional data continuity for global land …
Receiver operating characteristics (ROC) as validation tool for prospectivity models—A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt …
A prospectivity model for magmatic Ni–Cu deposits was created by integrating spatially
referenced geophysical and geochemical datasets based on a simple and practical …
referenced geophysical and geochemical datasets based on a simple and practical …
Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain
Mineral exploration activities require robust predictive models that result in accurate
map** of the probability that mineral deposits can be found at a certain location. Random …
map** of the probability that mineral deposits can be found at a certain location. Random …
Mineral prospectivity map** using deep self-attention model
Multi-source data integration for mineral prospectivity map** (MPM) is an effective
approach for reducing uncertainty and improving MPM accuracy. Multi-source data (eg …
approach for reducing uncertainty and improving MPM accuracy. Multi-source data (eg …
Sentinel-2 for map** iron absorption feature parameters
Iron is an indicator for soil fertility and the usability of an area for cultivating crops. Remote
sensing is the only suitable tool for surveying large areas at a high temporal and spatial …
sensing is the only suitable tool for surveying large areas at a high temporal and spatial …
Spatial agreement of predicted patterns in landslide susceptibility maps
The aim of the study is to assess the degree of spatial agreement among different patterns of
landslide susceptibility maps with almost similar success and prediction rate curves …
landslide susceptibility maps with almost similar success and prediction rate curves …
Wavelength feature map** as a proxy to mineral chemistry for investigating geologic systems: An example from the Rodalquilar epithermal system
The final product of a geologic remote sensing data analysis using multi spectral and
hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER …
hyperspectral images is a mineral (abundance) map. Multispectral data, such as ASTER …