Hydrogeophysics at societally relevant scales: Airborne electromagnetic applications and model structural uncertainty quantification
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Abstract
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 groundbased 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. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Hydrogeophysics at societally relevant scales: Airborne electromagnetic applications and model structural uncertainty quantification |
| DOI | 10.1190/segam2018-2989187.1 |
| Year Published | 2018 |
| Language | English |
| Publisher | Society of Exploration Geophysicists |
| Contributing office(s) | Geology and Geophysics Science Center |
| Description | 5 p. |
| Larger Work Type | Book |
| Larger Work Subtype | Monograph |
| Larger Work Title | SEG technical program expanded abstracts 2018 |
| First page | 4894 |
| Last page | 4898 |
| Google Analytic Metrics | Metrics page |