1 - 10 of 14 results for: geochemistry

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.

Chemistry of the Earth and Planets Couse Description: Introduction to chemical principles with an emphasis on applications in the Earth Sciences. Topics include the origin and distribution of the elements in the solar system and on Earth, the origin and structure of the Earth, its oceans, and atmosphere; crystal chemistry, structure, and transformations; predicting and balancing reactions; thermodynamics, phase diagrams, high temperature and aqueous geochemistry, weathering, isotope geochemistry, and organic geochemistry. Students will also be exposed to analytical methods used in the Earth sciences.

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.

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.

Chemistry of the Earth and Planets Couse Description: Introduction to chemical principles with an emphasis on applications in the Earth Sciences. Topics include the origin and distribution of the elements in the solar system and on Earth, the origin and structure of the Earth, its oceans, and atmosphere; crystal chemistry, structure, and transformations; predicting and balancing reactions; thermodynamics, phase diagrams, high temperature and aqueous geochemistry, weathering, isotope geochemistry, and organic geochemistry. Students will also be exposed to analytical methods used in the Earth sciences.

(Formerly GEOLSCI 135 and 235) Introduction to research methods in sedimentary geochemistry. Proper laboratory techniques and strategies for generating reliable data applicable to any future labwork will be emphasized. This research-based course will examine how the geochemistry of sedimentary rocks informs us about local and global environmental conditions during deposition. Students will collect geochemical data from a measured stratigraphic section in the western United States. These samples will be collected during a four-day field trip at the end of spring break (attendance encouraged but not required). In lab, students will learn low-temperature geochemical techniques focusing on the cycling of biogeochemical elements (O, C, S, and Fe) in marine sediments throughout Earth history. The focus will be on geochemistry of fine-grained siliciclastic rocks (shale) but the geochemistry of carbonates will also be explored. This is a lab-based course complemented with lectures. Students who wish to take the course for less than 4 units must receive approval from the instructor. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit. Change of Department Name: Earth and Planetary Science (Formerly Geologic Sciences).

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.