Research output: Contribution to journal › Article › peer-review
Determining the Arrival Times of Direct P- and S-Waves for Weak Earthquakes Using Machine Learning. / Kamashev, A. M.; Duchkov, A. A.; Yaskevich, S. V.
In: Seismic instruments, Vol. 61, No. 3, 01.10.2025, p. 245-259.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Determining the Arrival Times of Direct P- and S-Waves for Weak Earthquakes Using Machine Learning
AU - Kamashev, A. M.
AU - Duchkov, A. A.
AU - Yaskevich, S. V.
N1 - The study was supported by the Russian Science Foundation (project no. 23-17-00237). Kamashev, A. M. Determining the Arrival Times of Direct P- and S-Waves for Weak Earthquakes Using Machine Learning / A. M. Kamashev, A. A. Duchkov, S. V. Yaskevich // Seismic Instruments. – 2025. – Vol. 61, No. 3. – P. 245-259. – DOI 10.3103/S0747923925700380.
PY - 2025/10/1
Y1 - 2025/10/1
N2 - We suggest an approach to automatic picking of P- and S-wave arrival times when processing data from local seismological-monitoring network. A distinctive feature of this approach is that it does not attempt to train a universal neural network for processing all types of seismological data. Instead, we focus on one specific region at a time, which significantly narrows the requirements for the training dataset size and variability. An important result is the automatic quality-control tool, since it simultaneously ensures the accuracy of the accepted events as well as forms a fairly small dataset of rejected events. This small dataset can be further used for manual processing and additional neural-network training. This approach was tested on real data from two local seismological networks located in different regions. We demonstrate that a small seismological dataset can be used for training the neural network for processing seismological data from a specific region: records from 20–40 local earthquakes. For high-quality data, it is possible to pick the arrival times of P- and S-waves with an error less than 50 ms for 94 and 88% of cases, respectively. For the poor-quality dataset, it was possible to determine the arrival times of P- and S-waves with an error less than 200 ms in 82 and 73% of cases, respectively. The proposed approach makes it possible to accelerate automatic processing by reducing the required size of the training sample; the approach was implemented in stream processing for the considered seismological networks.
AB - We suggest an approach to automatic picking of P- and S-wave arrival times when processing data from local seismological-monitoring network. A distinctive feature of this approach is that it does not attempt to train a universal neural network for processing all types of seismological data. Instead, we focus on one specific region at a time, which significantly narrows the requirements for the training dataset size and variability. An important result is the automatic quality-control tool, since it simultaneously ensures the accuracy of the accepted events as well as forms a fairly small dataset of rejected events. This small dataset can be further used for manual processing and additional neural-network training. This approach was tested on real data from two local seismological networks located in different regions. We demonstrate that a small seismological dataset can be used for training the neural network for processing seismological data from a specific region: records from 20–40 local earthquakes. For high-quality data, it is possible to pick the arrival times of P- and S-waves with an error less than 50 ms for 94 and 88% of cases, respectively. For the poor-quality dataset, it was possible to determine the arrival times of P- and S-waves with an error less than 200 ms in 82 and 73% of cases, respectively. The proposed approach makes it possible to accelerate automatic processing by reducing the required size of the training sample; the approach was implemented in stream processing for the considered seismological networks.
KW - LOCAL EARTHQUAKES
KW - SEISMIC MONITORING
KW - artificial neural networks
KW - WAVE ARRIVAL TIMES
KW - AUTOMATIC PROCESSING
UR - https://www.mendeley.com/catalogue/2fee9f02-5963-337c-8b41-8b797436f8a4/
UR - https://elibrary.ru/item.asp?id=83051560
U2 - 10.3103/s0747923925700380
DO - 10.3103/s0747923925700380
M3 - Article
VL - 61
SP - 245
EP - 259
JO - Seismic instruments
JF - Seismic instruments
SN - 0747-9239
IS - 3
ER -
ID: 71568720