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Fall Semester Classes Begin
Start:August 25, 2021 at 8:00 am
End:
August 25, 2021 at 5:00 pm
UTIG Seminar Series: Bhargav Boddupalli, UTIG
Start:August 27, 2021 at 10:30 am
End:
August 27, 2021 at 11:30 am
Location:
Zoom Meeting
Contact:
Constantino Panagopulos, costa@ig.utexas.edu, 512-574-7376
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Speaker: Bhargav Boddupalli, Postdoctoral Fellow, UTIG
Host: Shuoshuo Han
Title: Imaging of the Deep Galicia margin using ocean bottom seismic data
Abstract: Continental rifting and breakup is the first order tectonic process that initiates the plate tectonic cycle and results in the formation of passive rifted margins. The Galicia margin, west of Iberia, is archetypical for magma-poor rifted margins where a number of key concepts of rifting processes have been developed and tested. Seismic imaging has been instrumental in understanding rifting in the Galicia margin. In this talk, I present a high-resolution P-wave velocity model of the Deep Galicia margin (DGM) where the final breakup of the continental crust happened. The velocity model is derived employing a 3D acoustic full waveform inversion (FWI) technique in the time domain using sparsely acquired wide-angle ocean bottom seismometer (OBS) data. Comparison of the 3D FWI model result with 2D result derived along a profile through the 3D seismic volume highlighted the differences between the imaging methods in a real world setting. Differences in the data residuals of the 2-D, 2.5-D and 3-D inversions suggest that 2-D inversion can be prone to overfitting when using a sparse data set. Using the 3D FWI velocity model of hyper-thinned crust at the Deep Galicia Margin (DGM), we constrain the nature of the crust at this margin by comparing its velocity structure with those in other similar tectonic settings. Our velocity model also shows evidence for exhumation of the lower crust under the footwalls of fault blocks to accommodate the extension. We used our model to generate a serpentinization map for the uppermost mantle at the DGM, at a depth of 100 ms (~340m) below the S-reflector, a low-angle detachment that marks the base of the crust at this margin. Based on this map, we propose that serpentinization began during rifting and continued into a post-rift phase until the faults were sealed. We find a poor correlation between the fault heaves and the degree of serpentinization beneath the hanging- and foot-wall blocks, indicating that serpentinization was controlled by a complex mechanism during and after rifting. A good match between topographic highs of S and local highly serpentinized areas of mantle suggests that the serpentinization process resulted in variable uplift of the S-surface.
First-order multiples from the OBS data can be used to develop seismic images using a technique called mirror imaging. We developed seismic images of the DGM in time and depth domains using mirror imaging. In this technique, the seafloor along with the OBS is mirror imaged with respect to the sea-surface and placed at a depth of twice the water column depth. Such an adjustment allows incorporation of the multiples in to migration algorithms just like primary reflections. Mirror imaging can become a standard processing step in studies where no multichannel data are available.
Soft Rock Seminar: Zach Sickmann
Start:August 30, 2021 at 12:00 pm
End:
August 30, 2021 at 1:00 pm
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Accelerating sand mining for Bangladesh development threatens the sand chars of the Ganges
Dr. Zach Sickmann (UTIG)
Monday, August 30th 12-1 pm
Zoom link: https://utexas.zoom.us/j/94967658238
UTIG Discussion Hour: Gail Christeson, UTIG
Start:August 31, 2021 at 2:00 pm
End:
August 31, 2021 at 3:00 pm
Location:
Zoom Meeting
Contact:
Naoma McCall, nmccall@utexas.edu
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Speaker: Gail Christeson, Senior Research Scientist / Associate Director, UTIG
Title: South Atlantic Transect Project: Pre-Expedition Drilling during Covid-19 Pandemic
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. |
