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Full-Text Articles in Physical Sciences and Mathematics
Surficial And Deep Earth Material Prediction From Geochemical Compositions, Hassan Talebi, Ute Mueller, Raimon Tolosana-Delgado, Eric C. Grunsky, Jennifer M. Mckinley, Patrice De Caritat
Surficial And Deep Earth Material Prediction From Geochemical Compositions, Hassan Talebi, Ute Mueller, Raimon Tolosana-Delgado, Eric C. Grunsky, Jennifer M. Mckinley, Patrice De Caritat
Research outputs 2014 to 2021
Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based …
On The Spatial Modelling Of Mixed And Constrained Geospatial Data, Hassan Talebi
On The Spatial Modelling Of Mixed And Constrained Geospatial Data, Hassan Talebi
Theses: Doctorates and Masters
Spatial uncertainty modelling and prediction of a set of regionalized dependent variables from various sample spaces (e.g. continuous and categorical) is a common challenge for geoscience modellers and many geoscience applications such as evaluation of mineral resources, characterization of oil reservoirs or hydrology of groundwater. To consider the complex statistical and spatial relationships, categorical data such as rock types, soil types, alteration units, and continental crustal blocks should be modelled jointly with other continuous attributes (e.g. porosity, permeability, seismic velocity, mineral and geochemical compositions or pollutant concentration). These multivariate geospatial data normally have complex statistical and spatial relationships which should …