III: Medium: Collaborative Research: Scaling Time Series Analytics to Massive Seismology Datasets

University Of California-San Diego Scripps Inst Of Oceanography, La Jolla CA

Investigators

Abstract

This project will enable a team of computer scientists and earth scientists at the University of California-Riverside, the University of California-San Diego and the University of New Mexico to develop novel tools to search existing seismographic databases for subtle earthquakes that may have evaded detection. These more complete earthquake catalogues will allow more accurate hazard analysis and risk reduction. The intellectual merit of the proposed work is in creating novel data representations, definitions, algorithms (and ultimately, highly usable open-source code) that will allow the seismological community greatly to expand both the type and the scale of the analytics that they can perform, both offline and in real time. The broader impacts of this project results from the more comprehensive and complete earthquake catalogs created. These have the potential to affect multiple branches of earthquake science. For example, the more accurate hazard and risk models derived from the catalogs can be used by governments and private industry to plan for and mitigate economic and human losses, e.g., by mandating resilient construction and infrastructure, and by accurately assessing insured risk. In addition, the projects comprehensive educational and outreach activities have already been piloted on a small scale and include detailed plans to reach out to underserved communities at the K-12 and college levels, and to create grade-level appropriate teaching resources that exploit the natural interest most K-12 students have in the drama of earthquakes. Humans notice large earthquakes, but the frequently occurring smaller quakes caused by the constant slipping of fault lines typically go unnoticed, even by skilled seismologists with access to telemetry. However, these imperceptible quakes could help us understand the physical processes that trigger hazardous earthquakes, assisting in hazard-reduction efforts. Recently, a novel data structure called the Matrix Profile has emerged as a very promising technique for pattern discovery in large datasets. The PIs, an interdisciplinary team of computer scientists and seismologists, propose to investigate techniques to use Matrix Profile the scale up the size of datasets that can be investigated by 100X magnitude, to find 20X more earthquakes. The intellectual merit of this project will result in novel data representations, definitions, algorithms (and ultimately, highly usable open-source code) that will allow the seismological community to expand both the type and the scale of the analytics that they can perform. The broader impacts of this project are difficult to overstate. Comprehensive and complete earthquake catalogs are foundational to multiple branches of earthquake science, notably the physics of earthquake nucleation, hazard analysis and risk reduction. The hazard and risk models derived from them can be used by governments and private industry to plan for and mitigate economic and human losses, e.g. by mandating resilient construction and infrastructure, and by accurately assessing insured risk. The project’s comprehensive educational and outreach activities have already been piloted on a small scale and include detailed plans to reach out to under-served communities at the K-12 and college levels. 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.