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Open AccessArticle
Towards Near-Real-Time Seismic Phase Recognition, Event Detection, and Location with Deep Neural Networks in Volcanic Area of Campi Flegrei
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione Osservatorio Vesuviano, 80124 Napoli, Italy
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Appl. Sci. 2026, 16(1), 458; https://doi.org/10.3390/app16010458 (registering DOI)
Submission received: 20 November 2025
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Revised: 28 December 2025
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Accepted: 29 December 2025
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Published: 1 January 2026
Featured Application
The potential application of this work is to assist experts working in volcano monitoring centres, both by speeding up event detection times and by identifying weak events as potential precursors to volcanic activity, especially in densely populated areas. This can help to provide essential information for risk assessment and prevention.
Abstract
The real-time phase picking, detection, and location of seismic events is a crucial challenge for monitoring in densely populated volcanic areas. In such contexts, low-magnitude events may escape traditional detection methods due to high levels of anthropogenic noise, which often masks weak seismic signals. This study presents the implementation of a near-real-time automatic event detector with a seismic phase recognizer, pick associator, and localiser. The system is based on PhaseNet, a well-established deep neural network recognized for its effectiveness in seismology. The main innovation introduced in this work lies in the direct application of this method to real-time data streams. This integration allows for the enhanced identification and cataloguing of low-magnitude seismic events that would otherwise remain unobserved. The adoption of the system in a real-time operational context not only increases monitoring sensitivity and responsiveness but also contributes to a more detailed and comprehensive understanding of seismic activity in critical volcanic areas, providing essential data for risk assessment and prevention.
Share and Cite
MDPI and ACS Style
Cantiello, P.; Esposito, R.; Di Filippoand, A.; Peluso, R.
Towards Near-Real-Time Seismic Phase Recognition, Event Detection, and Location with Deep Neural Networks in Volcanic Area of Campi Flegrei. Appl. Sci. 2026, 16, 458.
https://doi.org/10.3390/app16010458
AMA Style
Cantiello P, Esposito R, Di Filippoand A, Peluso R.
Towards Near-Real-Time Seismic Phase Recognition, Event Detection, and Location with Deep Neural Networks in Volcanic Area of Campi Flegrei. Applied Sciences. 2026; 16(1):458.
https://doi.org/10.3390/app16010458
Chicago/Turabian Style
Cantiello, Pasquale, Roberta Esposito, Alessandro Di Filippoand, and Rosario Peluso.
2026. "Towards Near-Real-Time Seismic Phase Recognition, Event Detection, and Location with Deep Neural Networks in Volcanic Area of Campi Flegrei" Applied Sciences 16, no. 1: 458.
https://doi.org/10.3390/app16010458
APA Style
Cantiello, P., Esposito, R., Di Filippoand, A., & Peluso, R.
(2026). Towards Near-Real-Time Seismic Phase Recognition, Event Detection, and Location with Deep Neural Networks in Volcanic Area of Campi Flegrei. Applied Sciences, 16(1), 458.
https://doi.org/10.3390/app16010458
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