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  • ERAU Industrial Math Project: Characterizing Seismic Events through Advanced Data Analytics

    PI Mihhail Berezovski

    CO-I Lawrence Acchione

    CO-I Nathaniel Arzola Lilly

    CO-I Dhairya Chokshi

    CO-I Jessica Haselwood

    CO-I Paige Rasmussen

    CO-I Nikolaus Rentzke

    The Signal Processing and Applied Mathematics research group at the Nevada National Security Site, is seeking to collaborate with students at Embry-Riddle Aeronautical University to develop advanced data analytics methods for characterizing seismic events in the southwest United States as near-field earthquakes, far-field earthquakes, or non-natural seismic events, using publicly available seismic data.

    One way of monitoring whether other countries are engaging in explosives-driven testing of weapons is to measure seismicity from the events, but this is only interesting if we can correctly characterize a seismic event as an:

    • earthquake,
    • industrial explosives event (mining, construction, etc.),
    • or an explosives test.

    The first step is to simply be able to distinguish between natural and non-natural seismic events. Since the Nevada National Security Site engages in explosives-driven tests - and is in an area of active earthquake activity - it is a natural test bed for developing data analytics approaches to classifying signals as natural or non-natural. Being a "test bed" means that, in addition to the measured signals, we have some ground truth information, meaning we know when earthquakes and explosives tests actually happen. 

    Larger-scale experiments, like the North Korean nuclear tests, can be seen at much larger distances, so even in Nevada we can measure seismic signals from nuclear tests. Figure shows a seismic measurement in Nevada from North Korea's September 2017 nuclear test.

    Primary Goal: Develop analytics methods for differentiating near- field, small earthquakes in Nevada and southern California, from far- field, large earthquakes around the world, from non-natural seismic events. 

    One of the primary challenges in analyzing seismic data is that the data is collected 24 hours per day, 7 days a week, but actual seismic anomalies - either natural or not - occur relatively rarely.
    Thus a first step in the analytics is to develop a technique for combing through the data to find anything that looks like an anomaly. The second task will be to classify the anomalies.


    Tags: Undergraduate Research Data Analytics Data Science machine learning Seismic Industrial Mathematics

    Categories: Undergraduate

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