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 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 Virtual Reality User Interface (VRUI) A platform-independent foundation for development of virtual reality applications Lidar Viewer Earth Viewe r Earth Viewe r Crusta 3D Visualizer 3D Visualizer CAVES 3D TV Desktop Laptop

Haiti: January 12, 2010 Mw ,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 km 2 surveyed using airborne LiDAR at an average density of ~3.2 points/m 2 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

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