ABSTRACT

Seismology is the most powerful tool for investigating the interior structure of Earth?from its surface down to the inner core?and its wide range of processes, including earthquakes, volcanic activity, glacial processes, oceanic and environmental processes, and human-caused processes such as nuclear explosions or hydraulic fracturing in oil and gas exploration. Seismology cannot achieve its greatest potential without harnessing state-of-the-art computing capabilities for the dual purpose of scientific modeling and analysis of rapidly increasing data sets. The SCOPED (Seismic COmputational Platform for Empowering Discovery) project establishes a computing platform that delivers data, computation, and service to the seismological community in a way that promotes education, innovation, and discovery, and enables efficient solutions to outstanding scientific problems in geophysics. By focusing on openly available data, openly available software, and virtual training, SCOPED opens seismological research to a broad range of users. Four research components emphasize openly available software for the purpose of characterizing Earth's subsurface structure and the wide range of natural and man-made events that are recorded by seismometers every day. Training of seismologists is a central focus of the project. SCOPED training workshops (seismoHackweeks) are open to the community. Emphasis on virtual research and training diversifies strategies to engage minority groups entering computational geosciences. The project trains a new generation of seismologists to harness the latest capabilities for processing and modeling large data sets.

The SCOPED project establishes cyberinfrastructure that provides fast access to large seismic archives from a suite of containerized open-source computational tools for big data analysis, machine learning, and high-performance simulations. The implementation focuses on four interconnected, compute- and data-intensive research components: seismic imaging of Earth?s interior, waveform modeling of earthquakes and Earth structure, monitoring of Earth structure using ambient noise, and precision monitoring of earthquakes and faults. Each research component is enabled by open-source codes that meet, or aspire to meet, best practices for software development. The project contains several transformative components. First, it offers compute performance for both model- and data-driven seismological problems. Hundreds of terabytes of waveform data are directly accessible both to modelers?for data assimilation problems?and to data scientists for processing, analysis, and exploration. Second, it establishes a direct collaborative link among four teams of seismologists at four institutions and a team of computational scientists at Texas Advanced Computing Center. This unity reflects the necessity of both groups to achieve research-ready codes that can exploit high-performance computing (HPC) and Cloud systems. Third, it establishes a gateway with ready-to-run (or adapt) container images and data as a service for the seismological community. Fourth, it develops computational tools that promote the democratization of HPC/Cloud with cutting-edge data processing and modeling software through their scalability from laptops to HPC or Cloud systems and through their portability with containerization. Finally, although the development of cyberinfrastructure is the main priority, ancillary scientific results from advanced techniques are expected to offer insights into fundamental seismological problems. The project has the potential for discoveries across fields (seismology, Earth science, computer science, data science, material science), as well as societal relevance in the realms of seismic hazard assessment, environmental science, cryosphere, earthquake early warning, energy systems, and geophysical detection of nuclear proliferation.

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.

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