ダイジシン ノ ダンソウ シュウヘン ニ オケル オウリョクバ ノ クウカン ヘンカ

京都大学0048新制・課程博士博士(理学)甲第12114号理博第3008号新制||理||1448(附属図書館)23950UT51-2006-J109京都大学大学院理学研究科地球惑星科学専攻(主査)助教授 飯尾 能久, 教授 平原 和朗, 教授 橋本 学学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDA

Reappraisal of volcanic seismicity at the Kirishima volcano using machine learning

Abstract Volcanic earthquakes provide essential information for evaluating volcanic activity. Because volcanic earthquakes are often characterized by swarm-like features, conventional methods using manual picking require considerable time to construct seismic catalogs. In this study, using a machine learning framework and a trained model from a volcanic earthquake catalog, we obtained a detailed picture of volcanic earthquakes during the past 12 years at the Kirishima volcano, southwestern Japan. We detected ~ 6.2 times as many earthquakes as a conventional seismic catalog and obtained a high-resolution hypocenter distribution through waveform correlation analysis. Earthquake clusters were estimated below the craters, where magmatic or phreatic eruptions occurred in recent years. Increases in seismic activities, b values, and the number low-frequency earthquakes were detected before the eruptions. The process can be conducted in real time, and monitoring volcanic earthquakes through machine learning methods contributes to understanding the changes in volcanic activity and improving eruption predictions. Graphical Abstrac

MOESM1 of Why do aftershocks occur? Relationship between mainshock rupture and aftershock sequence based on highly resolved hypocenter and focal mechanism distributions

Additional file 1. Procedure of event selection for principal component analysis (PCA). This file describes our procedure for event selection

MOESM2 of Why do aftershocks occur? Relationship between mainshock rupture and aftershock sequence based on highly resolved hypocenter and focal mechanism distributions

Additional file 2. Validation of the thickness of aftershock distribution by using the observed differential arrival times. This file shows the waveform records to validate the thickness of aftershock distribution

Development of a high-performance seismic phase picker using deep learning in the Hakone volcanic area

Abstract In volcanic regions, active earthquake swarms often occur in association with volcanic activity, and their rapid detection and analysis are crucial for volcano disaster prevention. Currently, these processes are ultimately left to human judgment and require significant time and money, making detailed real-time verification impossible. To overcome this issue, we attempted to apply machine learning, which has been successfully applied to various seismological fields to date. For seismic phase pick, several models have already been trained using a large amount of training data (mainly crustal earthquakes). Although there are some cases in which these models can be applied without any problems, regional dependence on pre-trained models has been reported. Since this study targets earthquakes in a volcanic region, applying existing pre-trained models may be difficult. Therefore, in this study, we compared three models; the publicly available trained model (model 0), a model which was trained with approximately 220,000 P- and S-wave onset reading data recorded at the Hakone volcano from 1999 to 2020 with initialized parameters (model 1) using the same architecture, and a model fine-tuned with the aforementioned Hakone data using the parameters of model 0 as initial values (model 2), and evaluated their phase identification performance for the Hakone data. As a result, the seismic phase detection rates of models 1 and 2 were much higher than those of model 0. However, small-amplitude signals are often missed when multiple seismic events occur within a detection time window. Therefore, we created training data with two earthquakes in the same time window, retrained the model using the data, and successfully detected events that previously would have been missed. In addition, it was found that more events were detected by setting the threshold to a low probability value for detection, increasing the number of seismic phase detections, and filtering by phase association and hypocenter location. Graphical Abstrac

MOESM1 of Infrasonic wave accompanying a crack opening during the 2015 Hakone eruption

Additional file 1. Photographs near the vents (Owakudani) taken using a time-lapse camera before and during the rapid tilt change

MOESM2 of Infrasonic wave accompanying a crack opening during the 2015 Hakone eruption

Additional file 2. Calibration test for the microphone at the OWD station