
NSF Org: |
CMMI Division of Civil, Mechanical, and Manufacturing Innovation |
Recipient: |
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Initial Amendment Date: | September 1, 2010 |
Latest Amendment Date: | September 1, 2010 |
Award Number: | 1029022 |
Award Instrument: | Standard Grant |
Program Manager: |
Eduardo Misawa
emisawa@nsf.gov (703)292-5353 CMMI Division of Civil, Mechanical, and Manufacturing Innovation ENG Directorate for Engineering |
Start Date: | September 1, 2010 |
End Date: | October 31, 2012 (Estimated) |
Total Intended Award Amount: | $400,000.00 |
Total Awarded Amount to Date: | $400,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
Sponsor Congressional District: |
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Primary Place of Performance: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | CDI TYPE II |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.041 |
ABSTRACT
Seismic and geodynamic observational data will be employed to infer a unified dynamic earth model through solution of the joint nonlinear inverse problem governed by high-resolution mantle convection and seismic wave propagation models. This will lead to a merging of the diverse data used to constrain plate tectonics and mantle convection, providing a new 4-D picture of earth's surface and interior over the last 50 million years. The inverse problem entails severe mathematical, computational, and geophysical challenges, which conventional methods are incapable of addressing. There are several parts of this project that are planned to overcome these challenges. We will devise inverse methods that can extract full information from the large volumes of seismic and geodynamic data by creating algorithms to solve the coupled full waveform seismic and geodynamic inverse problem, and use those methods to invert for global and regional earth models from broadband seismic and geodynamic data. We will develop inversion algorithms that can scale to the large numbers of CPU cores and complex memory hierarchies characterizing emerging multi-petaflop systems. We will also extend adaptive mesh refinement (AMR) ideas from large-scale forward simulation to the setting of large-scale inverse problems. Beyond the scientific impact, the project has a program of outreach and education that is highlighted by dissemination of 4-D animations of dynamic earth models. This project fits within the "From Data to Knowledge" and "Understanding Complexity" themes of the CDI Program.
The earth is a four-dimensional dynamic system where mantle convection drives plate tectonics and continental drift and, in turn, controls much activity ranging from the occurrence of earthquakes and volcanoes to mountain building and long-term sea level change. Despite the central role mantle convection plays in our understanding of earth, we have enormous first-order gaps in our knowledge, with questions that are as basic as what are the principal driving and resisting forces on plate tectonics to what is the energy balance of the planet as a whole. However, rapidly-expanding volumes of geophysical data, the arrival of the petaflop computing era, and the emergence of high-resolution forward model simulation capabilities now provide an opportunity to merge the geophysical data into dynamic earth models to greatly enhance our understanding of earth structure. This project could catalyze a shift in the field of geodynamics, since it will lead to rigorous inference of earth models from data employing high-resolution forward models. Moreover, the project could be transformative for many other fields with similar needs, through the development of parallel mesh algorithms for large-scale inverse problems, scalable methods for large-scale nonlinear inverse problems, and inverse methods for joint inversion of data for large complex multi-physics forward models. All of these computational/mathematical advances will benefit a much wider community of scientists working on a much broader set of problems than the ones encountered in this project.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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