Events
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Bureau of Economic Geology Seminar Series
Start:May 6, 2025 at 10:00 am
End:
May 6, 2025 at 11:00 am
Contact:
Dena Miller, dena.miller@beg.utexas.edu, 512-471-2677
Bureau of Economic Geology Seminar Series
Start:May 13, 2025 at 10:00 am
End:
May 13, 2025 at 11:00 am
Contact:
Dena Miller, dena.miller@beg.utexas.edu, 512-471-2677
MG&G Field Course Presentation Day
Start:May 30, 2025 at 10:00 am
End:
May 30, 2025 at 12:00 pm
Location:
ROC 1.603
Contact:
Kristen Tucek, ktucek@jsg.utexas.edu, 5124712223
Each Maymester, the University of Texas Institute for Geophysics (UTIG) offers a field course designed to provide hands-on instruction for graduate and upper-level undergraduate students in the collection and processing of marine geological and geophysical data. The course covers high-resolution air gun and streamer seismic reflection, CHIRP seismic reflection, multibeam bathymetry, sidescan sonar, sediment coring, grab sampling and the sedimentology of resulting seabed samples (e.g., core description, grain size analysis, x-radiography, etc.).
Scientific and technical experts in each of the techniques first provide students with several days of classroom instruction. The class then travels to the Gulf Coast for a week of at-sea field work and on-shore lab work. Two small research vessels are used concurrently: one for multibeam bathymetry, sidescan sonar, and sediment sampling, and the other for high-resolution seismic reflection and CHIRP sub-bottom profiling. Students rotate daily between the two vessels and lab work.
Upon returning to Austin, students work in teams to integrate data and techniques into a final project that examines the geologic history and/or sedimentary processes as typified by a small area of the Gulf Coast continental shelf. Students spend one week learning interpretation methods using industry-standard, state-of-the-art software (Focus, Landmark, Caris, Fledermaus). On the last day, students present their final project to the class and industry sponsor representatives.
HydroML 2026 SymposiumMay, 19 2026Time: 12:00 AM - 12:00 AMLocation: POB 2.302 The HydroML 2026 symposium will explore how AI/ML concepts can be used to enhance the predictive understanding of complex systems in hydrological and geological sciences. The overarching goal is to discuss process-based scientific principles that can help integrate AI/ML with earth system science. In essence, the symposium seeks to stimulate discussions that will help develop physically guided AI/ML approaches which are explainable, interpretable, and improve the mechanistic understanding of earth system science. It will foster collaborations among researchers who are both new to the field and already involved, thereby strengthening ties within the community of AI/ML researchers. |
Environmental Science Institute’s Community-Based Research SymposiumMay, 19 2026Time: 12:00 AM - 12:00 AMLocation: WCP 2.302 Community-based research is essential for understanding and addressing challenges that reflect real community needs. For example, rapid urban growth and increasing weather extremes are already straining communities, and these pressures are expected to intensify in the years ahead. This in-person symposium will bring together university researchers and students, community organizations and members, government entities, industry representatives, and other interested stakeholders to explore the opportunities and benefits of Community-based research in Texas and beyond. |
Urban Climate LectureMay, 22 2026Time: 12:00 PM - 1:30 AMLocation: Barrow Conference Room (JGB 4.102) Capturing Spatial Variability of Urban Microclimate in Process-Based and Machine Learning Models by Dr. Tirthankar \"TC\" Chakraborty, Earth Scientist at the Pacific Northwest National Laboratory (PNNL) Abstract: Cities modify their local microclimate via process-level changes and through alterations in bulk radiative, morphological, and thermal properties. Cities are also highly heterogeneous, leading to spatial variability in environmental hazards, with potential disparities in climate risks for different urban residents. While significant efforts have been made to improve urban representation in models to isolate broader urban climate signals, current models often struggle to resolve intra-urban variability due to poor structural and parameter constraints at the neighborhood scale. In this seminar, I will provide an overview of this urban spatial variability and its importance, our current limitations in capturing this variability, and potential ways forward by leveraging current-generation fine-grained satellite observations. Specifically, I will highlight our past and ongoing research involving both process-based numerical modeling and machine learning approaches to capture the spatial distribution of urban heat hazards. The lessons learned from these studies can guide future urban model development efforts to enable more accurate neighborhood-scale climate mitigation and adaptation strategies. |