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Sensors
Volume 21
Issue 18
10.3390/s21186290
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Article

The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time

by
Andrey Stepnov
1,*,
Vladimir Chernykh
2 and
Alexey Konovalov
1
1
Far East Geological Institute, Far Eastern Branch, Russian Academy of Sciences, 690022 Vladivostok, Russia
2
Khabarovsk Federal Research Center, Far Eastern Branch, Russian Academy of Sciences, 680000 Khabarovsk, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Nunzio Cennamo
Sensors 2021, 21(18), 6290; https://doi.org/10.3390/s21186290
Received: 21 August 2021 / Revised: 11 September 2021 / Accepted: 16 September 2021 / Published: 19 September 2021
(This article belongs to the Topic Artificial Intelligence in Sensors)
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Abstract

When recording seismic ground motion in multiple sites using independent recording stations one needs to recognize the presence of the same parts of seismic waves arriving at these stations. This problem is known in seismology as seismic phase picking. It is challenging to automate the accurate picking of seismic phases to the level of human capabilities. By solving this problem, it would be possible to automate routine processing in real time on any local network. A new machine learning approach was developed to classify seismic phases from local earthquakes. The resulting model is based on spectrograms and utilizes the transformer architecture with a self-attention mechanism and without any convolution blocks. The model is general for various local networks and has only 57 k learning parameters. To assess the generalization property, two new datasets were developed, containing local earthquake data collected from two different regions using a wide variety of seismic instruments. The data were not involved in the training process for any model to estimate the generalization property. The new model exhibits the best classification and computation performance results on its pre-trained weights compared with baseline models from related work. The model code is available online and is ready for day-to-day real-time processing on conventional seismic equipment without graphics processing units. View Full-Text
Keywords: seismogram; spectrogram; transformer; attention; CNN; deep learning; seismic phase; real-time automation; classification; computational efficiency; local seismic network seismogram; spectrogram; transformer; attention; CNN; deep learning; seismic phase; real-time automation; classification; computational efficiency; local seismic network
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MDPI and ACS Style

Stepnov, A.; Chernykh, V.; Konovalov, A. The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time. Sensors 2021, 21, 6290. https://doi.org/10.3390/s21186290

AMA Style

Stepnov A, Chernykh V, Konovalov A. The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time. Sensors. 2021; 21(18):6290. https://doi.org/10.3390/s21186290

Chicago/Turabian Style

Stepnov, Andrey, Vladimir Chernykh, and Alexey Konovalov. 2021. "The Seismo-Performer: A Novel Machine Learning Approach for General and Efficient Seismic Phase Recognition from Local Earthquakes in Real Time" Sensors 21, no. 18: 6290. https://doi.org/10.3390/s21186290

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