Tag: Support Vector Machine
Sample clustering in Isatis.neo has proven to be efficient with big datasets
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Isatis.neo quickly groups borehole samples into homogeneous classes (e.g., facies, geological or mining domains) in an automatic way. Those who have seen the tool run qualifies it as impressive.
Mining (8)
Oil & Gas (5)
Nuclear Decommissioning (3)
Hydrogeology (3)
Contaminated sites (1)
TAGS:
2D erodability map (1)
3D (1)
Airborne flight line (1)
Complex geometric relationships (1)
Conditional Simulations (4)
Contamination diagnostic (1)
Conversions & Uncertainties (1)
Data analysis (1)
Domaining (1)
Facies (1)
Flight line spacing (1)
Flow path lines (1)
Flumy (1)
Genetic model (1)
Geostatistical Hierarchical Clustering (1)
Geostatistics (1)
Image Filtering (1)
Isatis (8)
Isatis.neo (9)
Kartotrak (2)
Kriging (1)
Mapping (2)
Merge data (1)
MIK (2)
Mineral resources (2)
MPS (1)
Multi-Acquisition Factorial Kriging (1)
Multivariate geostatistics (1)
Post-accidental situation (2)
Post-processing (1)
Recoverable resource estimation (3)
Reservoir modeling (1)
Resource classification (2)
Resources workflow (3)
Risk analysis (1)
Sampling optimization (2)
Scripting procedures (3)
Seismic cubes (1)
Soil contamination mapping (1)
Statistics (1)
Support Vector Machine (1)
turbiditic depositional system (1)
Uncertainty analysis (1)
Uniform Conditioning (3)
Variography (2)
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DATES:
2020 (2)
2019 (7)
2018 (2)
2017 (2)
2016 (2)