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Scientific Visualization for Earthquake Science and Simulation. Louise Kellogg, Tony Bernardin , Eric Cowgill , Oliver Kreylos , Mike Oskin , John Rundle , Donald L. Turcotte , M. Burak Yikilmaz UC Davis: Geology, Computer Science, & KeckCAVES. - PowerPoint PPT Presentation
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Scientific Visualization for Earthquake Science and Simulation
Louise Kellogg, Tony Bernardin, Eric Cowgill, Oliver Kreylos, Mike Oskin, John Rundle, Donald L. Turcotte,
M. Burak YikilmazUC Davis: Geology, Computer Science, & KeckCAVES
Earthscope data Seismic Tomography model (Obrebski, et al 2010)
Scientific visualization research for natural hazards at the KeckCAVES
Virtual Reality User Interface (VRUI)
A platform-independent foundation for development of virtual reality
applications
Lidar Viewer
EarthViewer Crusta 3D
Visualizer
CAVES3D TV
Desktop Laptop
Haiti: January 12, 2010Mw 7.0
• 200,000 – 300,000 fatalities.
• Massive damage from building collapse including houses, govt. buildings, UN headquarters, airport.
Analysis of high-resolution airborne and terrestrial LIDAR after recent events
• Goal:– support rescue and recovery
first – and then to support science
• ~2.7 billion individual point measurements in (3D) space; 66.8 GB on disk
• January 21 – 27, 2010, an area of 850 km2 surveyed using airborne LiDAR at an average density of ~3.2 points/m2
• Funded by World Bank, coordinated by USGS, collected by Rochester Institute of Technology
Working with LIDAR point cloud data
Mapping the fault system
Remote mapping
• Guided field work• Gave consistent
results as found in the field
• Can improve quality and quantity of rapid scientific response
We concluded that the 2010 earthquake was a relatively small event between the 1751
and 1770 ruptures.
El Mayor-Cucapah M 7.2 April 2010
El Mayor-Cucapah M 7.2 April 2010
Credit: Mike Oskin, Ramon Arrowsmith, Alejandro Hinojosa, and Javier Gonzalez
Removing vegetation from LIDAR data
Interactive scientific visualization for rapid response
• Interactive visualization in a VR environment has the potential to completely change rapid scientific response to events
• Visualization of these very large datasets is challenging, but feasible, using octree data representation.
• Human-in-the-loop is essential to interpretation (combined with automated methods)
• Underway: change detection (time series)• Future developments: Coupling data interpretations
with simulations