Development of High-Performance Seismic Phase Picker Using Deep Learning in Hakone Volcanic Area

Abstract

Abstract In volcanic regions, active earthquake swarms often occur associated with volcanic activity, and their rapid detection and measurement are crucial for volcano disaster prevention. Currently, however, these processes are ultimately left to human judgment and require much time and money, making detailed verification in real time impossible. To overcome this issue, we attempted to apply machine learning, which has been studied in many seismological fields in recent years. Several models have already been trained using a large amount of training data (mainly crustal earthquakes). Although there are some cases where these models can be applied without any problems, regional dependence on the learned models has also been reported. Since this study targets earthquakes in a volcanic region, existing learned models may be difficult to apply. Therefore, in this study, we created the above publicly available trained model (model0), a model trained with approximately 220,000 seismic waveform data recorded at Hakone volcano from 1999 to 2020 with initialized weights (model1) using the same architecture, and a model fine-tuned with the aforementioned Hakone data using the weight of model0 as initial values (model2), and evaluated their performance. As a result, the detection rates of model1 and 2 were much higher than model0. However, small amplitudes are often missed when multiple seismic waves are in a time window to determine the phase arrival. Therefore, we created training data with two waveforms in the one-time window, retrained the model using the data, and successfully detected waveforms that would have been missed previously. In addition, it was found that more events were detected by setting the threshold to a low value for detection, increasing the number of detections, and filtering by phase association and hypocenter location.