CAREER: Scalable Computational Seismology for All

Computational seismology methods analyze data that measure vibrations of the Earth. These methods allow scientists to understand earthquake hazards, to measure stability of the ground underneath structures, to monitor groundwater systems, to study changes in threatened Earth systems such as glaciers and permafrost, to safely and efficiently explore natural resources underground, and to monitor civil infrastructure health, among other applications.

Seismology has undergone a radical shift recently; new sensor technologies have made data collection much easier, enabling hundreds to thousands of times larger datasets that can be used for detailed studies of larger regions for long periods of time. Most scientists cannot use these data because: (1) data are only shared internally among groups that have new sensors, (2) public seismology data storage facilities cannot support such large data quantities, and (3) most geoscientists do not have the computational resources to analyze the data.

Because of these three issues, there is an inequitable research environment, much data remains unexplored, and important geoscience discoveries cannot occur. While there are ongoing efforts to address the first issue, without major cyberinfrastructure advances addressing the second and third issues newly acquired data is unlikely to be fully analyzed.

This project aims to create new computational algorithms, software and models of open data sharing to ensure that any geoscientist can glean valuable insights from large-scale seismology data. The education and outreach program will create opportunities for more people to participate in mathematical modeling and large-scale data analysis for science and engineering applications.

The project PI will develop and strengthen existing efforts to support diverse and inclusive research and learning environments. She will continue to develop a program to introduce women undergraduates to mathematics research, growing it to be a sustainable multi-faculty course serving more students from underrepresented groups. The project will increase the impact of the annual data science conference led by the PI.

The conference features research by women data scientists and tutorials on modern data science techniques, and connects the interdisciplinary data science community on a rural campus.

The project will derive and analyze new geoscience algorithms, develop community software and explore models of open data distribution. The project goal is to ensure that any seismologist can gain valuable geophysical insights from extreme-scale seismic data in the field, at institutions with limited computing resources, and on modern high performance computing (HPC) systems.

Expertise in large-scale seismic sensing, mathematics, high-throughput computational science, and algorithm design are necessary to achieve these advances. The project proposes a new model for public seismology data archives that allows for the storage of lossy-compressed data and data products, thus creating a new capacity to host ultra-high-density and large-scale seismic data, without displacing existing systems for high-quality seismometer data.

To address large-scale data analysis, the PI has previously created several scalable algorithms, and theoretical analyses suggest that a complete suite of scalable, parallelizable algorithms for multiple types of passive seismic data processing can be developed. Many of the algorithms operate directly on compressed information without reconstructing the original data, which reduces costly data movement.

The project will develop fast serial and parallel software algorithm implementations, and investigate the use of accelerator hardware for high computational efficiency. For each algorithm the project will theoretically derive and computationally verify trends governing tradeoffs between computational efficiency, memory footprint, and end-to-end accuracy specific to the geophysical analyses.

The algorithms will incorporate error bounds for realistic non-idealized data and will be included in predictive software for geoscientists to make informed decisions prior to requests for compressed data or data products. The new methods will be tested by applying them to cutting-edge passive seismic data at the scale of tens to hundreds of terabytes.

The data will enable seismology analyses in for urban hydrology and geotechnical engineering, and also analyses to aid in understanding glacier movements to improve climate models via improved boundary conditions and mechanistic understanding. In addition to earthquake seismology, hydrology, geotechnical engineering, and cryosphere studies, the methods developed can be applied to many high-throughput computational science problems utilizing sparse or compressed representations (e.g., structural health monitoring, imaging science, solar physics, radioastronomy, wireless communications, industrial facility monitoring).

To increase adoption of new methods by geoscientists, the project will develop tutorials, promote scientific community collaboration, and organize research workshops.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.