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- Title
Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes.
- Authors
Zhong, Yiyuan; Tan, Yen Joe
- Abstract
The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor. Plain Language Summary: Earthquake activity at volcanic regions is often monitored to indicate volcanic activity. Identifying the time when the energy radiated from an earthquake source arrives at a seismometer is essential for locating the earthquake, which can be difficult for volcanic earthquakes because of high noise levels, high event rates, and obscured onsets. Previous studies have demonstrated that deep learning can excel in picking the arrival times of regular earthquakes. However, it is unclear how sensitive these detectors are to earthquakes in volcanic regions. Here, we first compile a data set of earthquakes from various volcanoes globally. We then show that existing deep‐learning‐based detectors can miss a large fraction of these earthquakes, especially those without an abrupt change in signal amplitude. We then provide two new models which can better detect volcanic earthquakes than existing models. Our model/workflow can be applied to improve monitoring of volcanic earthquakes globally. Key Points: We compile a data set of seismic waveforms from various volcanic regions globallyWe show that existing deep‐learning phase pickers' performances deteriorate with decreasing earthquake frequency contentOur retrained models perform better and are more generalizable for monitoring volcano seismicity, especially long‐period earthquakes
- Subjects
SLOW earthquakes; VOLCANIC activity prediction; EARTHQUAKES; SUBDUCTION zones; DEEP learning; RADIATION; SEISMOMETERS; VOLCANOES
- Publication
Geophysical Research Letters, 2024, Vol 51, Issue 12, p1
- ISSN
0094-8276
- Publication type
Article
- DOI
10.1029/2024GL108438