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A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming
A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming
Undergraduate Student Research Internships Conference
Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning …
A Transformer-Based Classification System For Volcanic Seismic Signals, Cristian Bravo Roman, Cindy Mora Stock, Alexander James Hemming
A Transformer-Based Classification System For Volcanic Seismic Signals, Cristian Bravo Roman, Cindy Mora Stock, Alexander James Hemming
Undergraduate Student Research Internships Conference
Volcanic seismic signals are a key element in volcano monitoring to assess the state of unrest and a possible eruption style and timing. Different sources such as brittle fracture (volcano-tectonic - VT) or fluid movement (long period - LP) generate signals with distinct characteristics in frequency content and shape, but site effects such as attenuation or background noise make their determination difficult to the untrained eye. In cases of unrest or an eminent eruption, the amount of data would require a fast and reliable source of pre-classification to classify and catalogue to aid in the job usually done by a …