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Session Proposal: Geological Modeling from Geophysical Data for Water Resources Investigations: Integrating AI, Machine Learning and Predictive Modeling (AGU25)

NS009
Geological Modeling from Geophysical Data for Water Resources Investigations: Integrating AI, Machine Learning and Predictive Modeling

Session ID#: 252682

Session Description:
Effective sustainable water resources management requires comprehensive characterization of the subsurface geology to create the most accurate geological models. A major constraint to advancing the accuracy of geological models is the limited capacity to identify subsurface heterogeneity of geological properties that can be used to constrain, test or conceptualize the models. Advancement in the use of AI, machine learning and predictive modeling in geophysical data processing and interpretation offer the potential to improve subsurface geological models. This session aims to engage the geophysical community to discuss advances in geological modeling using machine learning, AI and predictive modeling of geophysical data. Contribution can address geological modeling using various types of geophysical data and modeling methods, model visualization and uncertainty estimation.
Co-Sponsor(s):
  • GC - Global Environmental Change
  • H - Hydrology
  • NH - Natural Hazards
  • S - Seismology
Index Terms:

1829 Groundwater hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1859 Rocks: physical properties [HYDROLOGY]
1873 Uncertainty assessment [HYDROLOGY]
Primary Convener:  Kisa E Mwakanyamale Gilkie, Illinois State Geological Survey, Champaign, United States
Convener:  Donald Allen Keefer, University of Illinois at Urbana Champaign, Prairie Research Institute, Urbana, United States
 
The Use of Airborne Electromagnetic Data to Inform Sustainable Groundwater Management in California (Invited) (1979875)
Rosemary J Knight1, Seogi Kang1,2, Meredith Goebel3, Rebecca Prentice1, Jonathan Mells1, Sylvia Zhang4 and Wes Neely5, (1)Stanford University, Department of Geophysics, Stanford, CA, United States, (2)University of Manitoba, Earth Sciences, Winnipeg, Canada, (3)Stanford University, Department of Geophysics, Stanford, United States, (4)Stanford University, Geophysics, Stanford, United States, (5)INTERA Incorporated, San Diego, United States
 
A Knowledge-Driven Method for Generating Multiple High-Resolution Geologic Models from HTEM, Core, Wireline, and ERT Data (2004021)
Donald Allen Keefer1, Kisa E Mwakanyamale Gilkie2,3, Jason Thomason3, Steven Young4, Timothy Larson5 and Flemming Jørgensen6, (1)University of Illinois at Urbana Champaign, Prairie Research Institute, Urbana, United States, (2)Illinois State Geological Survey, Champaign, United States, (3)University of Illinois at Urbana Champaign, Urbana, United States, (4)University of Illinois at Urbana Champaign, Illinois State Geological Survey, Urbana, United States, (5)University of Illinois at Urbana-Champaign, Prairie Research Institute, Champaign, United States, (6)FVJ Consult, Aarhus, Denmark
See more of: Near Surface Geophysics
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