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GEOSCIENCE NEEDS & CHALLENGES

Dogan SeberSan Diego Supercomputer Center

University of California, San Diego, USA

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Earth science research is moving towards a “systems approach”. To understand the Earth we need to look at it as a whole. Scientists have expertise in specific areas in their sub-disciplines and knowledge about sister disciplines is limited.

Can cyberinfrastructure help?

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Some common IT problems in the Geosciences

• Exponential increase in data volumes• Diversity and complexity of data sets• Data storage, access and preservation• Data integration (semantic and syntactic)• Computational challenges and access to HPC • Advance visualization (3D/4D)• Archiving publications with reusable

components

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A Scientific Effort Vector

Background Background ResearchResearch

Data Collection and Data Collection and Compilation/ Compilation/ Software issuesSoftware issues

ScienceScience

Back-Back- groundground

ResearchResearch

Data Collection Data Collection and and

Compilation/Compilation/Software IssuesSoftware Issues

ScienceScience

ScienceScience - Analysis, Modeling, Interpretation, Discovery

Source: R. Keller

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Enabling Scientific Discoveries:Pathway to Discovery

Access Process Analyze Interpret Discovery

Data Knowledge

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Large/Complex Data Volumes

• National/International Observatories/projects EarthScope

ES is a US project to collect data across the entire US over the next 10 years. Includes seismic, GPS and drill hole data

LiDAR dataAirborne and ground based data collection (large volumes of data sets)

Global Observations A variety of satellites gathering data at different resolutions

Hydrology, Environmental, Natural resource development projects, etc.

• Small projectsIndividual researchers maintain a lot of data sets, such as geology maps,

geochemistry databases, earthquake catalogues, etc. Collectively reusable data reach large volumes and complex dimensions

Challenge: How to manage these data so that vast amounts of datacan be used by all scientists in an easy-to-use environment

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Data Storage, Access and Preservation

• Preservation of digital and legacy data sets• Since research needs and styles of each

scientist vary, each researcher has his/her own data with their own “flavors”

• Access to other scientist’s data is limited• When scientists do not continue to maintain

their data, it is lost forever!

Challenge: How to build a framework to exchange data and helppreserving collected data sets

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Data Integration Issues

Integration requires both syntactic and semantic level integration. e.g., How can a geologist merge multiple geology maps to make a seamless (“integrated”) map that overlaps with national and international boundaries.

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Integrate Geologic Data From Multiple Sources

What is available is multiple distinct data sets

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Integration Across DisciplinesIntegration Across Disciplines

Earthquakes

Aquifers

Tectonics

Moho depth

Geology

Faults

Magnetics

Mines

Topography

Focal Mechanisms

Sediment thickness

Gravity

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Computational Challenges in Geosciences

• Developing/Accessing community codes• Parallelizing software for efficient runs• Accessing small to very large clusters • Technical expertise to use high-end

systems/clusters

Challenges: How to build a system that helps scientists run advance software without having access to significant resources (computers and technical)

How to build a system that helps scientists to focus on science rather than technological challenges/problems

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(Goldstein 2001)

Example:

Can we build a system that not only a few privileged, but also the entire community could use to run 3D seismic modeling?

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Geosciences are Visualization Oriented

• Once large volume data sets are accessed, how can we visualize them to get a better understanding of each data set?

• To build an effective visualization environment powerful software and hardware needed.

Challenge: How to build a visualization system that helps scientists analyze large and complex data sets dynamically.

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Archiving results and publications with reusable components

• Science progresses incrementally. New knowledge is built on top of existing knowledge.

• Scientific validity is shown by repeatability.

Challenges: How to preserve scientific results and help others to repeatthe analysis as efficiently as possible?

How to share algorithms and processing flows with others?

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Efforts underway…

• Numerous projects are funded to address these questions• E.g., GEON, SCEC ITR, CUAHSI, EarthChem• NSF funding opportunities in GEO and CISE directorates

• Professional societies getting involved in CI• GSA Geoinformatics Division• AGU Earth and Space informatics focus group

• Extensive level of outreach and learning activities taking place

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Lessons Learned 1/2• Building cyberinfrastructure resources is a

“social experimentation”• Equal partnerships between domain and IT is a

must• Understand the needs of the domain sciences• Community outreach is critical (workshops,

seminars, scientific meetings, etc)• Get it right the first time!• Define the goals clearly, and publicize them• Learn to differentiate “a system that works” and

“a system that is usable”

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• Work with those who are willing and interested • Identify “killer apps”, use them to attract more

interest• Teach! Help building a community of users and

resource builders• Problems are similar. Work with other

communities, solutions may be out there

Lessons Learned 2/2