M. T. Glasscoe

QuakeSim and the Solid Earth Research Virtual Observatory

We are developing simulation and analysis tools in order to develop a solid Earth Science framework for understanding and studying active tectonic and earthquake processes. The goal of QuakeSim and its extension, the Solid Earth Research Virtual Observatory (SERVO), is to study the physics of earthquakes using state-of-the-art modeling, data manipulation, and pattern recognition technologies. We are developing clearly defined accessible data formats and code protocols as inputs to simulations, which are adapted to high-performance computers. The solid Earth system is extremely complex and nonlinear, resulting in computationally intensive problems with millions of unknowns. With these tools it will be possible to construct the more complex models and simulations necessary to develop hazard assessment systems critical for reducing future losses from major earthquakes. We are using Web (Grid) service technology to demonstrate the assimilation of multiple distributed data sources (a typical data grid problem) into a major parallel high-performance computing earthquake forecasting code. Such a linkage of Geoinformatics with Geocomplexity demonstrates the value of the Solid Earth Research Virtual Observatory (SERVO) Grid concept, and advances Grid technology by building the first real-time large-scale data assimilation grid.

E-DECIDER: Using Earth Science Data and Modeling Tools to Develop Decision Support for Earthquake Disaster Response

Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) is a NASA-funded project developing new capabilities for decision making utilizing remote sensing data and modeling software to provide decision support for earthquake disaster management and response. E-DECIDER incorporates the earthquake forecasting methodology and geophysical modeling tools developed through NASA’s QuakeSim project. Remote sensing and geodetic data, in conjunction with modeling and forecasting tools allows us to provide both long-term planning information for disaster management decision makers as well as short-term information following earthquake events (i.e. identifying areas where the greatest deformation and damage has occurred and emergency services may need to be focused). This in turn is delivered through standards-compliant web services for desktop and hand-held devices.

Advantages to Geoscience and Disaster Response from QuakeSim Implementation of Interferometric Radar Maps in a GIS Database System

High-resolution maps of earth surface deformation are available in public archives for scientific interpretation, but are primarily available as bulky downloads on the internet. The NASA uninhabited aerial vehicle synthetic aperture radar (UAVSAR) archive of airborne radar interferograms delivers very high resolution images (approximately seven meter pixels) making remote handling of the files that much more pressing. Data exploration requiring data selection and exploratory analysis has been tedious. QuakeSim has implemented an archive of UAVSAR data in a web service and browser system based on GeoServer (http://geoserver.org). This supports a variety of services that supply consistent maps, raster image data and geographic information systems (GIS) objects including standard earthquake faults. Browsing the database is supported by initially displaying GIS-referenced thumbnail images of the radar displacement maps. Access is also provided to image metadata and links for full file downloads. One of the most widely used features is the QuakeSim line-of-sight profile tool, which calculates the radar-observed displacement (from an unwrapped interferogram product) along a line specified through a web browser. Displacement values along a profile are updated to a plot on the screen as the user interactively redefines the endpoints of the line and the sampling density. The profile and also a plot of the ground height are available as CSV (text) files for further examination, without any need to download the full radar file. Additional tools allow the user to select a polygon overlapping the radar displacement image, specify a downsampling rate and extract a modest sized grid of observations for display or for inversion, for example, the QuakeSim simplex inversion tool which estimates a consistent fault geometry and slip model.

Integrating remotely sensed and ground observations for modeling, analysis, and decision support

Earthquake science and emergency response require integration of many data types and models that cover a broad range of scales in time and space. Timely and efficient earthquake analysis and response require automated processes and a system in which the interfaces between models and applications are established and well defined. Geodetic imaging data provide observations of crustal deformation from which strain accumulation and release associated with earthquakes can be inferred. Data products are growing and tend to be either relatively large in size, on the order of 1 GB per image with hundreds or thousands of images, or high data rate, such as from 1 second GPS solutions. The products can be computationally intensive to manipulate, analyze, or model, and are unwieldy to transfer across wide area networks. Required computing resources can be large, even for a few users, and can spike when new data are made available or when an earthquake occurs. A cloud computing environment is the natural extension for some components of QuakeSim as an increasing number of data products and model applications become available to users. Storing the data near the model applications improves performance for the user.

Imaging of earthquake faults using small UAVs as a pathfinder for air and space observations

Large earthquakes cause billions of dollars in damage and extensive loss of life and property. Geodetic and topographic imaging provide measurements of transient and long-term crustal deformation needed to monitor fault zones and understand earthquakes. Earthquake-induced strain and rupture characteristics are expressed in topographic features imprinted on the landscapes of fault zones. Small UAVs provide an efficient and flexible means to collect multi-angle imagery to reconstruct fine scale fault zone topography and provide surrogate data to determine requirements for and to simulate future platforms for air- and space-based multi-angle imaging.

GeoGateway: A system for analysis of UAVSAR data products

GeoGateway is a web-enabled map-based system for analysis, modeling, and response of geodetic imaging products for studying earthquakes and crustal deformation. The system provides a data product search and analysis gateway for scientific discovery, field use, and disaster response. To be effective users require data overlay and visualization, interactive analysis features, and data product download. The data products of focus in this project are NASAs UAVSAR and spaceborne interferometric radar, (InSAR), geologic earthquake faults, Global Positioning System (GPS) position time series, and seismicity.

Understanding earthquake fault systems using QuakeSim analysis and data assimilation tools

We are using the QuakeSim environment to model interacting fault systems. One goal of QuakeSim is to prepare for the large volumes of data that spaceborne missions such as DESDynI will produce. QuakeSim has the ability to ingest distributed heterogenous data in the form of InSAR, GPS, seismicity, and fault data into various earthquake modeling applications, automating the analysis when possible. Virtual California simulates interacting faults in California. We can compare output from long time-history Virtual California runs with the current state of strain and the strain history in California. In addition to spaceborne data we will begin assimilating data from UAVSAR airborne flights over the San Francisco Bay Area, the Transverse Ranges, and the Salton Trough. Results of the models are important for understanding future earthquake risk and for providing decision support following earthquakes. Improved models require this sensor web of different data sources, and a modeling environment for understanding the combined data.