Attend the Near-Surface Geophysics Reception and Award Ceremony, a 52-minute event hosted by AGU. Witness the presentation of the Edward A. Flinn III Award and explore cutting-edge research on landslide processes at the Hollin Hill Landslide Observatory. Discover geophysical characterization techniques, long-term geoelectrical monitoring methods, and innovative approaches to slope stability assessment. Learn about distributed sensor networks, machine learning applications for predicting critical parameters, and the integration of multiple methods for landslide hazard assessment. Gain insights into Berkeley Lab's unique playground for developing sensing technology and the use of low-cost, low-powered distributed sensor networks for adaptive, long-term monitoring. Explore the potential of combining various techniques to achieve a predictive understanding of landslide behavior and the importance of network architecture and data visualization using open-source packages.
Overview
Syllabus
Intro
Near-Surface Geophysics Executive Committee (2022)
2022 HONORS
Edward A. Flinn III Award
"Coming together is a beginning. Keeping together is progress. Working together is success." Henry Ford
The Hollin Hill Landslide Observatory A long-standing research site for understanding landslide processes
Geophysical characterization of the Hollin Hill Landslide Observatory Importance of estimating elastic parameters
Long-term geoelectrical monitoring of Hollin Hill Translating models of electrical resistivities into moisture content
Long-term geoelectrical monitoring of Hollin Hill Imaging hydrological processes that control landslide behavior
Nested sensing for slope stability assessment and early warning Utilize and integrate recent advances in various fields
Distributed sensor network to assess landslide probability over time Using geophysics, drilling, and remote sensing to assess the landslide hazard
Monitoring landslide reactivation in response to Winter storm event Moisture infiltration causes reactivation
Berkeley Lab - A unique playground for developing sensing technology Distributed wireless sensor network
Low-cost, low-powered distributed sensor network Adaptive, long-term monitoring using in-house sensor developments
Machine Learning to predict critical parameters Essential for LEWS and adaptive sensing strategies
A Step Closer Towards Predictive Understanding? Combining multiple methods and scales
Network architecture and data visualization Using open-source packages
Taught by
AGU
