Ahmed Elbanna
University of Southern California, CA·Earth & Environmental Science
Predicting how hard the ground will shake during an earthquake depends on knowing what the rock and soil beneath a region look like. That subsurface structure is captured in three-dimensional seismic velocity models, which describe how fast seismic waves travel through the Earth at each point.
- Fields
Earth & Environmental Science
Engineering
Computer Science
- Topics
seismic velocity model
earthquake science
cyberinfrastructure
- Grants
- 1 known grant, through Jun 2029
What they work on
- Rebuild the Unified Community Velocity Model as UCVM-Next, replacing legacy file formats with Zarr and TileDB array storage so that three-dimensional seismic velocity models carry versioning, provenance and validation information.
- Develop a lightweight Python interface that lets users query velocity models, inspect model parameters, compare models side by side, and export structured data subsets for high-performance computing simulations.
- Support ingestion of regional and high-resolution subsurface datasets, including distributed acoustic sensing from fiber-optic cables, ambient noise imaging, and geotechnical surveys of shallow soils.
- Enable systematic model validation and uncertainty characterization so velocity models can be reused and checked across academic, government and industry users, supported by example materials and workshops.
More about the research
These models are scattered across incompatible file formats and research groups, which makes them hard to compare, reuse or trust. This project builds shared software infrastructure so that researchers, engineers and public agencies can work with subsurface models in a consistent, reproducible way.
Why it matters
Ground-shaking forecasts depend on subsurface models that are currently difficult to compare or verify. Making these models findable, versioned and testable could give earthquake scientists and engineers more trustworthy inputs for simulating shaking, which in turn supports safer infrastructure planning in seismically active regions. Students, agencies and industry users would gain a common, documented way to access and check the same underlying data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Ahmed Elbanna
University of Illinois Urbana-Champaign, IL·Earth & Environmental Science
Build an extensible software framework and science gateway that gives a broad user community access to physics-based earthquake rupture forecast models without requiring local expertise or computing resources.
Willow Ahrens
Georgia Institute of Technology, GA·Computer Science
Form four coordinated working groups to develop core ecosystem components for sparse array programming — shared designs, data formats, performance metrics and optimization strategies — aligned through a joint committee.
Maureen Long
Yale University, CT·Earth & Environmental Science
Image the New England crust at high resolution using hundreds of nodal seismometers spaced 500 m apart, analyzing both distant earthquake waves and background seismic noise to reveal how supercontinent cycles and mountain-building shaped it.
The school
University of Southern California: 47 labs on PI Pages, $1.3B a year on research, 882 research doctorates a year.
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