• GEON research and education activities:– Highlights given in talks– Some details provided in posters– Presentations available at the end of the day
• GEON infrastructure and applications: mostly prototypes
GEON Principal Investigators
• Ramon Arrowsmith Arizona State University
• Chaitan Baru San Diego Supercomputer Center / University of California, San Diego
• Maria Luisa Crawford Bryn Mawr College
• Karl Flessa University of Arizona
• Randy Keller University of Texas at El Paso
• Mian Liu University of Missouri, Columbia
• Chuck Meertens UNAVCO, Inc.
• John Oldow University of Idaho
• Dogan Seber San Diego Supercomputer Center / University of California, San Diego
• Paul Sikora University of Utah
• Krishna Sinha Virginia Tech
GEON Mission and Goals “Enabling scientific discoveries and improving
education in Earth Sciences through information technology research.”
• Develop cyberinfrastructure for Geoscience research– Integrate, analyze and model 4-D data– Research and development in data integration systems, computing
environments, and ontologic frameworks– Facilitate knowledge discovery for the geosciences
• Promote leadership within geoscience education reform• Revolutionize how earth scientists do their science
– democratize access to services and data– allow on-line replication of results – increase awareness of scientific knowledge “pathways”
• Facilitate a cultural change
Challenges
Many databases and models: Interpretations limited by existing knowledge Capture of concepts and relationships needed for
computational tractability Creation of community knowledge base: Required to support knowledge discovery Assists in hypothesis generation
A. K. Sinha
An important slide emphasizing the need for scientists to think beyond their sub- domain datasets; emphasize points 3 and 4. Also a lead in to the phrase that concept spaces and ontologies will be the heart of this presentation
The Pathway…Partnership with Information Technology
Access /share data and products Access /develop smart tools Access computational resources Access/apply knowledge management Preserve data Become educational leaders
GEON supports such activities
Access to data representing scales of phenomenon and processes will be available within the infrastructure for discovering new
knowledge (remember EarthScope)
Lithosphere thickness (schematic) based on Zoback and Mooney (2003), Geologic Map ( USGS), fault distribution from Sinha (unpubl.)
Distribution of faults and earthquakes in mid-Atlantic region
Surface geology
Cratonal lithosphereDeep mantle
GEON TestbedScience Themes
CRUSTAL EVOLUTION: ANATOMY OF AN
OROGEN
The Appalachian Orogen is a continental scale mountain belt that provides a geologic template to examine the growth and breakup of continents through plate tectonic processes .
Role of accretionary orogens in the growth of continents1. Major site of juvenile continental crust production at
convergent plate margins2. Addition of crust through accretion (terranes)3. Recycling of continental and oceanic crust
The Appalachian orogen provides a natural laboratory to develop methods for integration of data, tools and models with
an emphasis on 4-D management of data and knowledge
First Order Science Question:What is the geologic history of accretionary
orogens ?
Appalachian Mountains: Recording 1000 Ma Of Earth History
Geologic phenomena
• Assembly and dispersal of super-continents: Rodinia , the Grenville record
The rock record preserves processes associated with crustal evolution of continents
A. K. Sinha
GIS based geologic map of the central Appalachian orogen compiled at Virginia Tech. The rock record provides all the neccessary evidence for understanding fundamental processes that result in growth of continents through terrane accretion within the concept of the Wilson Cycle
Access, analysis and modeling of the igneous rock record is a pre-requisite for understanding crustal evolution through time-space
Scales of georeferenced observations contained in Virginia Tech database: facilitating analysis of orogens
Conceptual Model for Igneous Rock Properties (static) and Genesis (dynamic)
Design/Information Flow for Analysis of Igneous Rocks
Schema Development
Components of the Virginia Tech field based schema: deploying data across multiple scales of observation and analysis
Design/Information Flow for Analysis of Igneous Rocks
Ontology Development
Igneous Rock Database Schema and Linked Ontology
Results of query displayed geographically and used in spatial analyses of terranes
Based on SDSC (KR research group)
Prototype web based access and application of tools
Query results displayed in tables and in Query results displayed in tables and in classification diagramsclassification diagrams
Point-in-polygon routine classifies sample as Chrysolite. Sample can now participate in additional ontologically-driven comparative, statistical and data mining analyses.
Based on SDSC (KR research group)
Design and Information Flow for Analysis of Igneous Rocks
Tool Development
Ontology Based Data Mining
Ontology Driven Data Mining
GEOROC : UNIQUE DATABASE FOR DATA MINING RESEARCH
Create reusable “Knowledge Base”Iterate over experiment to refine the knowledge baseMinimize data handling/Maximize researchAllow different levels of knowledge discovery: Hidden, Deep
Adapted from Ramachandran, (2003)
Ontology Driven Data Mining
• Ontology assists in structuring the data
• Data sets associated with concepts in ontology
• User navigates ontology to choose data sets
• Helps to apply data mining at different levels of abstraction
• Spatial and temporal variables are represented in the data
