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Geophysics Department Seminar - Burke Minsley: Subsurface Mapping at Societally Relevant Scales: Airborne Electromagnetic Applications and Model Structural Uncertainty Quantification

Date and Time: 
February 22, 2018 -
12:00pm to 1:15pm
Location: 
Mitchell 350/372
Contact Email: 
coreyann@stanford.edu
Contact Phone: 
650.497.3498
Event Sponsor: 
Geophysics Department

Speaker: Burke Minsley, U.S. Geological Survey, Menlo Park, CA

There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with ground-based measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. 

I will provide some background on the AEM method and its recent application across a range of hydrologic, cryosphere, and infrastructure studies. In addition, I will discuss methods and algorithms recently developed for quantifying geological model structural uncertainty using AEM and other auxiliary data.  Model structural uncertainty estimates account for both geophysical parameter uncertainty using a trans-dimensional Bayesian Markov chain Monte Carlo algorithm, and also uncertainty in the physical property relationship that links geophysical parameters with different lithologic units.  I will show how AEM-derived estimates of model structural uncertainty, along with separate hydrologic observations, are used in a sequential hyrogeophysical approach to characterize hydrologic parameter and prediction uncertainty.