11 - 14 of 14 results for: geochemistry

Overview of some of the most important data science methods (statistics, machine learning & computer vision) relevant for geological sciences, as well as other fields in the Earth Sciences. Areas covered are: extreme value statistics for predicting rare events; compositional data analysis for geochemistry; multivariate analysis for designing data & computer experiments; probabilistic aggregation of evidence for spatial mapping; functional data analysis for multivariate environmental datasets, spatial regression and modeling spatial uncertainty with covariate information (geostatistics). Identification & learning of geo-objects with computer vision. Focus on practicality rather than theory. Matlab exercises on realistic data problems.

How microorganisms shape the geochemistry of the Earth's crust including oceans, lakes, estuaries, subsurface environments, sediments, soils, mineral deposits, and rocks. Topics include mineral formation and dissolution; biogeochemical cycling of elements (carbon, nitrogen, sulfur, and metals); geochemical and mineralogical controls on microbial activity, diversity, and evolution; life in extreme environments; and the application of new techniques to geomicrobial systems. Recommended: introductory chemistry and microbiology such as CEE 274A.

Overview of some of the most important data science methods (statistics, machine learning & computer vision) relevant for geological sciences, as well as other fields in the Earth Sciences. Areas covered are: extreme value statistics for predicting rare events; compositional data analysis for geochemistry; multivariate analysis for designing data & computer experiments; probabilistic aggregation of evidence for spatial mapping; functional data analysis for multivariate environmental datasets, spatial regression and modeling spatial uncertainty with covariate information (geostatistics). Identification & learning of geo-objects with computer vision. Focus on practicality rather than theory. Matlab exercises on realistic data problems.