1 Eyes in the Sky II Workshop Jet Propulsion Laboratory Maggi Glasscoe Solid Earth Group 12 July 2010 Using QuakeSim Tools with GoogleEarth to explore earthquakes

2 About Me Scientist in Solid Earth group since 2004 Bachelor of Arts in Print Journalism, Bachelor of Science in Geological Sciences, Master of Science in Geology, currently completing a PhD in Geology Research focused on long-term effects of earthquakes, fault interactions, and the mechanical behavior of the crust Study areas include the San Francisco Bay area (specifically the 1906 earthquake), the Los Angeles Basin, the Ventura Basin and the Eastern California Shear Zone Current projects include QuakeSim, E-DECIDER (Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response), and DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice)

Earthquake Modeling Multiple fault model segment geometry Multiple fault model meshMultiple segment SAF model mesh  The QuakeSim tools and portal are being utilized to investigate the effects of large earthquakes over time  Large-scale simulations are using portal tools, QuakeSim codes, and supercomputing time on Project Columbia in order to produce model results that enhance our understanding of the earthquake process  More complex (realistic) models show postseismic effects of the 1906 earthquake of 1 mm/yr rather than the 2–5 mm/yr for the less complex models  The QuakeSim tools and portal are being utilized to investigate the effects of large earthquakes over time  Large-scale simulations are using portal tools, QuakeSim codes, and supercomputing time on Project Columbia in order to produce model results that enhance our understanding of the earthquake process  More complex (realistic) models show postseismic effects of the 1906 earthquake of 1 mm/yr rather than the 2–5 mm/yr for the less complex models Model geometry for complex 1906 earthquake postseismic deformation models featured the use of QuakeSim portal tools in order to generate initial and subsequent meshes for input into GeoFEST viscoelastic simulation software

4 Statistical Analysis of Virtual California Data Log of correlation score matrix 400 yr time window. Raw score matrix is saturated at values around zero, so a log function is applied to highlight interesting features. Analysis above features 59 faults (639 elements) and does not include the “creeping” section of the San Andreas fault because of computational considerations Virtual California produces a very large synthetic seismic record Systematic observations over a long time period provide an opportunity to observe emergent behavior and fault interactions in the system We examine 40,000 years of VC data and apply our method to calculate the correlation of events on an “initiating” fault segment with subsequent events on a second fault element The analysis produces a correlation score matrix that shows the relative amount of correlation between events on two elements Analysis has been expanded to include processing of data at different time window lengths Examining correlation score matrices at different time window lengths can provide insight into the relationships between faults at different time scales  Analysis shows that events on the Eastern California Shear Zone typically precede, but do not follow events on the southern San Andreas fault

5 Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) E-DECIDER will deliver web-based infrastructure designed for ease-of-use by decision makers, including: Rapid and readily accessible UAVSAR interferograms after earthquakes (as well as before) Standards-compliant map data products Deformation modeling and earthquake forecasting results The decision support tools will be developed working in partnership with end users in first response and disaster management agencies, including: –USGS –California Geological Survey –California Office of Emergency Services E-DECIDER will provide decision support for earthquake disaster management and response utilizing NASA remote sensing data and modeling software end users UAVSAR images Map and modeling tools

6 DESDynI: Mission Goals and Objectives Determine the likelihood of earthquakes, volcanic eruptions, and landslides US annualized losses from earthquakes are $4.4B/yr yet current hazard maps have an outlook of 30–50 years over hundreds of square kilometers. Determine the likelihood of earthquakes, volcanic eruptions, and landslides US annualized losses from earthquakes are $4.4B/yr yet current hazard maps have an outlook of 30–50 years over hundreds of square kilometers. Monitor the migration of fluids associated with hydrocarbon production and groundwater resources Management of our hydrological resources is applicable to every state in the union. Monitor the migration of fluids associated with hydrocarbon production and groundwater resources Management of our hydrological resources is applicable to every state in the union. Characterize the effects of changing climate and land use on species habitats and carbon budget The rate of increase [of atmospheric CO 2 ] over the past century is unprecedented, at least during the past 20,000 years. The structure of ecosystems is a key feature that enables quantification of carbon storage. Characterize the effects of changing climate and land use on species habitats and carbon budget The rate of increase [of atmospheric CO 2 ] over the past century is unprecedented, at least during the past 20,000 years. The structure of ecosystems is a key feature that enables quantification of carbon storage. Predict the response of ice sheets to climate change and impact on sea level [Ice sheets and glaciers] are exhibiting dramatic changes that are of significant concern for science and international policy. These indicators of climate remain one of the most under-sampled domains in the system. Predict the response of ice sheets to climate change and impact on sea level [Ice sheets and glaciers] are exhibiting dramatic changes that are of significant concern for science and international policy. These indicators of climate remain one of the most under-sampled domains in the system. Application

7 Geophysical Sensor Web { QuakeSim Background - QuakeSim is a project to develop a solid Earth science framework for modeling and understanding earthquake and tectonic processes The multi-scale nature of earthquakes requires integrating many data types and models to fully simulate and understand the earthquake process QuakeSim focuses on modeling the interseismic process through various boundary element, finite element, and analytic applications, which run on various platforms including desktop and high end computers

8 QuakeSim Environment Objectives –Solid Earth science framework for modeling and understanding Goal toward improved earthquake forecasting –Make NASA crustal deformation and seismic and geologic data and various earthquake simulation models available to the broader earthquake science community –Provide infrastructure for UAVSAR and other missions (e.g. DESDynI) Impacts –Provides analysis infrastructure for GPS Networks (Plate Boundary Observatory, SCIGN, BARD) UAVSAR International SAR missions –Tools for science definition and mission design (e.g. DESDynI) Repeat interval analysis Mission duration analysis –Improved understanding of earthquakes

9 QuakeSim Portal Data End-to-End Flow Fault Data Interferograms GPS Data Seismicity Data HPC Processing Center Desktop Computer Supercomputer DESDynI InSAR Data Modeling and Analysis

10 QuakeSim Capabilities Optimized use of spaceborne crustal deformation for studying earthquake fault systems Federated databases of different data sources –Fault –GPS –InSAR Data access through –High performance computing software –Web and grid services Efficient ingestion of spaceborne InSAR and UAVSAR data into geophysical models –Can be run over the compute grid

11 Applications ApplicationPurposeData SourceCompared To Virtual CaliforniaInteracting fault model Faults, frictionEarthquakes GeoFESTFinite element deformation model FaultsGPS and InSAR surface deformation DislocSurface displacements from fault motion FaultsGPS and InSAR surface deformation SimplexInversion for fault motion from deformation data InSAR and GPS surface deformation Fault data constrain model RIPISeismic pattern analysis SeismicityEarthquake faults RDAHMMTime series analysis GPSEarthquake, aquifer sources

12 NameInstitution Jun JiIndiana University For creating the fabulous tutorial movie and providing support for the demo Xiaoming GaoIndiana University For the wonderful RDAHMM kml movie generator and providing support for the demo Marlon PierceIndiana University For creating the training accounts and providing support for the demo Andrea DonnellanJet Propulsion LaboratoryQuakeSim Principal Investigator Thanks to…