15:00 〜 15:15
[SSS09-16] 脈動P波の震源カタログ
Although observations of microseisms can date back to the early 1900s [e.g., Wiechert 1904], the excitation mechanisms are still in progress. The source distribution is a key for understanding the excitation mechanism.
When we observed teleseismic P-wave microseisms at a distant station, the source can be approximated by a centroid single force practically (e.g., Nishida and Takagi, 2016). The centroid location of P-wave microseisms is crucial for understanding the origins of microseisms. Backprojection method is feasible for locating the centroids of body-wave microseisms. The technique utilizes the information of both slowness and the curvature of the wavefront. It can also utilize information on multi-phases. However, the computational cost is still expensive for making a global catalog for over ten years. On the other hand, the computational cost of beamforming is inexpensive. However, the method cannot distinguish P from PP waves because it utilizes information only on slowness. In this study, we propose a new technique that can extract information, both slowness and the curvature of the wavefront, as a natural extension of beamforming.
In this method, we first infer the initial guess of the slowness vector at the center of the seismic array based on the conventional beamforming. We estimate the beam power according to an assumed curvature for the fixed slowness in the second step. By maximizing the beam power, we infer the initial guess of the curvature at the center. We update the values (slowness vector and the curvature at the center) iteratively based on the perturbation theory. The curvature gives us information on the epicentral distance between the center of the array and the source, although the accuracy is low in general. According to the distance information, we classify the detected slowness into the corresponding phase (e.g., P, or PP). Based on the classified phase, we infer the source location from the slowness value. The curvature information is also useful for reducing the false detection of the beamforming. To improve the accuracy of the centroid locations, we estimated corrections of the measured slowness vector for a global P-wave model (LLNL-G3Dv3: Simmons et al. 2012) using three-dimensional ray tracing (PMTI, Simmons, et al. 2011). To validate the technique, we applied this method for earthquakes with moment magnitudes larger than xx listed in the global CMT catalog (Ekström et al., 2012). The results with the corrections for the three-dimensional structure are consistent with the catalog.
We applied this method for vertical components of velocitymeters at about 800 Hi-net stations in Japan, operated by the National Research Institute for Earth Science and Disaster Prevention (NIED) from 2004 to 2018. The inferred centroid locations show seasonal variations, which are consistent with past studies. We will show a new preliminary catalog of source locations of global P-wave microseisms.
When we observed teleseismic P-wave microseisms at a distant station, the source can be approximated by a centroid single force practically (e.g., Nishida and Takagi, 2016). The centroid location of P-wave microseisms is crucial for understanding the origins of microseisms. Backprojection method is feasible for locating the centroids of body-wave microseisms. The technique utilizes the information of both slowness and the curvature of the wavefront. It can also utilize information on multi-phases. However, the computational cost is still expensive for making a global catalog for over ten years. On the other hand, the computational cost of beamforming is inexpensive. However, the method cannot distinguish P from PP waves because it utilizes information only on slowness. In this study, we propose a new technique that can extract information, both slowness and the curvature of the wavefront, as a natural extension of beamforming.
In this method, we first infer the initial guess of the slowness vector at the center of the seismic array based on the conventional beamforming. We estimate the beam power according to an assumed curvature for the fixed slowness in the second step. By maximizing the beam power, we infer the initial guess of the curvature at the center. We update the values (slowness vector and the curvature at the center) iteratively based on the perturbation theory. The curvature gives us information on the epicentral distance between the center of the array and the source, although the accuracy is low in general. According to the distance information, we classify the detected slowness into the corresponding phase (e.g., P, or PP). Based on the classified phase, we infer the source location from the slowness value. The curvature information is also useful for reducing the false detection of the beamforming. To improve the accuracy of the centroid locations, we estimated corrections of the measured slowness vector for a global P-wave model (LLNL-G3Dv3: Simmons et al. 2012) using three-dimensional ray tracing (PMTI, Simmons, et al. 2011). To validate the technique, we applied this method for earthquakes with moment magnitudes larger than xx listed in the global CMT catalog (Ekström et al., 2012). The results with the corrections for the three-dimensional structure are consistent with the catalog.
We applied this method for vertical components of velocitymeters at about 800 Hi-net stations in Japan, operated by the National Research Institute for Earth Science and Disaster Prevention (NIED) from 2004 to 2018. The inferred centroid locations show seasonal variations, which are consistent with past studies. We will show a new preliminary catalog of source locations of global P-wave microseisms.