09:30 〜 09:45
[SCG52-03] Ductile shear zone of the Arima-Takatsuki fault zone as a fluid pathway
キーワード：延性せん断帯、S波反射法解析、レシーバ関数、深層学習
The structure of the ductile shear zone has been proposed in various models (Norris and Toy., 2014, Fossen et al., 2017). However, the detailed structure is not clear. It is important to understand the structure of ductile shear zones and whether fluids exist there because the presence of fluids in ductile shear zones can influence the process of inland earthquake occurrence (Iio et al., 2002).
A previous study (Katoh et al., 2019) revealed a dipping reflector in the lower crust of the north-central Kinki region that has the same strike as the Arima-Takatsuki fault zone (ATFZ) and exists only in along dip direction of the ATFZ by an S-wave reflection analysis using natural earthquakes. In addition, receiver function imagines suggested that this reflector may be a thin layer of low seismic wave velocity. Based on the fact that the deepest part of the reflector corresponds to the focal area of deep low-frequency earthquakes and the impermeability of the lower crust, the previous study concluded that the reflector was a structure indicating the presence of fluid in the ductile shear zone.
However, since the previous study assumed a horizontally layered half-space structure, the dipping reflector imaged is an apparent structure. Therefore, it is necessary to estimate the true position of the reflector to discuss the correspondence between the reflector and the focal area of deep low-frequency earthquakes, and the positional relationship between the reflector and the ATFZ.
In this study, we performed an S-wave reflection analysis considering the dipping correction of the reflector in the lower crust of the central-northern Kinki region. For this analysis, we used waveforms of natural earthquakes from 2009 to 2019 observed by the Manten network deployed in the north-central Kinki region. In the previous study, waveforms from 2009 to 2013 were used for the analysis, but the development of an automatic seismic arrival time picking model using deep learning has enabled us to use data from 2014 onward, thus significantly increasing the amount of data.
In the previous study, all waveforms were used in the analysis regardless of whether they contained reflected waves or not, but in this study, only reflected waves were used for the analysis by picking the arrival time of the reflected wave using deep learning. The ray-tracing for receiver function imaging was performed using the dipping angle of the reflector obtained from the S-wave reflection analysis.
In this presentation, we present the results of S-wave reflection analysis and receiver function analysis, and we discuss the possibility that the reflector is a ductile shear zone of the ATFZ.
A previous study (Katoh et al., 2019) revealed a dipping reflector in the lower crust of the north-central Kinki region that has the same strike as the Arima-Takatsuki fault zone (ATFZ) and exists only in along dip direction of the ATFZ by an S-wave reflection analysis using natural earthquakes. In addition, receiver function imagines suggested that this reflector may be a thin layer of low seismic wave velocity. Based on the fact that the deepest part of the reflector corresponds to the focal area of deep low-frequency earthquakes and the impermeability of the lower crust, the previous study concluded that the reflector was a structure indicating the presence of fluid in the ductile shear zone.
However, since the previous study assumed a horizontally layered half-space structure, the dipping reflector imaged is an apparent structure. Therefore, it is necessary to estimate the true position of the reflector to discuss the correspondence between the reflector and the focal area of deep low-frequency earthquakes, and the positional relationship between the reflector and the ATFZ.
In this study, we performed an S-wave reflection analysis considering the dipping correction of the reflector in the lower crust of the central-northern Kinki region. For this analysis, we used waveforms of natural earthquakes from 2009 to 2019 observed by the Manten network deployed in the north-central Kinki region. In the previous study, waveforms from 2009 to 2013 were used for the analysis, but the development of an automatic seismic arrival time picking model using deep learning has enabled us to use data from 2014 onward, thus significantly increasing the amount of data.
In the previous study, all waveforms were used in the analysis regardless of whether they contained reflected waves or not, but in this study, only reflected waves were used for the analysis by picking the arrival time of the reflected wave using deep learning. The ray-tracing for receiver function imaging was performed using the dipping angle of the reflector obtained from the S-wave reflection analysis.
In this presentation, we present the results of S-wave reflection analysis and receiver function analysis, and we discuss the possibility that the reflector is a ductile shear zone of the ATFZ.