日本地球惑星科学連合2024年大会

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[U-15] 2024年能登半島地震(1:J)

2024年5月28日(火) 17:15 〜 18:45 ポスター会場 (幕張メッセ国際展示場 6ホール)

17:15 〜 18:45

[U15-P36] 強震波形記録を用いて推定された2024年能登半島地震の震源過程

*久保 久彦1鈴木 亘1青井 真1関口 春子2 (1.国立研究開発法人防災科学技術研究所、2.京都大学防災研究所)

キーワード:2024年能登半島地震、震源過程、強震動

The 2024 Noto Peninsula earthquake (MJMA 7.6) struck the Hokuriku region of Japan on January 1, 2024 (JST). The focal mechanism and the spatial distribution of aftershocks indicate that this event was a shallow crustal reverse-fault-type earthquake with a NE-SW strike. In this study, we estimate the source process of this earthquake using strong motion waveforms. It is noted that considering a foreshock approximately 13 seconds before the mainshock as part of the 2024 Noto Peninsula earthquake, the analysis is performed with the foreshock occurrence time as the rupture initiation of the 2024 Noto Peninsula earthquake.
For the source-process analysis, we develop a fault model consisting of three rectangular segments with a SE dipping and different strike angles, referring to the spatial distribution and focal mechanisms of aftershocks and geodetic data. The NE segment (54 km × 24 km with a strike angle of 55° and a dip angle of 50°) is located off NE of the Noto Peninsula, the central segment (42 km × 24 km with a strike angle of 60° and a dip angle of 50°) aligns with the north coast of the Noto Peninsula, and the SW segment (30 km × 24 km with a strike angle of 30° and a dip angle of 50°) is located along the west coast of the Noto Peninsula. The rupture initiation point is set to the foreshock hypocenter determined by Hi-net, which is included in the central segment.
Strong motion data recorded at 30 stations of K-NET, KiK-net, and F-net of NIED (Aoi et al., 2020) are used. The velocity waveforms (converted by integrating the original K-NET and KiK-net accelerations) are band-pass filtered between 0.025 and 0.25 Hz, resampled to 5 Hz, and windowed from 5 s before the S-wave arrival for 100 s.
Green's functions are calculated using the discrete wavenumber method (Bouchon, 1981) and the reflection/transmission matrix method (Kennett and Kerry, 1979) assuming a 1D layered velocity structure model. The underground structure model is obtained for each station from the 3D structure model (Fujiwara et al., 2009). Borehole logging data are also referred to for the KiK-net stations.
The multi-time-window linear waveform inversion method (Olson and Apsel, 1982; Hartzell and Heaton, 1983) is used in this study. For the spatial discretization, the fault plane is divided into 21 subfaults along strike direction and 4 subfaults along dip direction, with a size of 6 km × 6 km each. For the temporal discretization, the moment rate function of each subfault is represented by 20 smoothed-ramp functions (time windows) progressively delayed by 1.4 s and having a duration of 2.8 s each. To stabilize the inversion, the slip angle is limited to vary within ±45 degrees centered at 90° using the non-negative least-squares scheme (Lawson and Hanson, 1974). For the 10 stations near the source fault, we use weights that are two times larger than those for the other stations. In addition, we impose the spatiotemporal smoothing constraint on slips (Sekiguchi et al., 2000). The triggering velocity of first time window is set to 2.4 km/s on the NE segment and 2.8 km/s on the central and SW segments because the preliminary analysis suggested that the main rupture timing on the NE segment would be later than on other segments.
In the estimated source model, three large slip areas are found in the shallow parts of each segment. A rupture occurred 15 s after rupture initiation, close to the occurrence time of the mainshock, in an area shallower than the rupture starting point on the central segment. Then, significant ruptures occurred 30 s after rupture initiation in the large slip areas of the NE and SW segments.
A recent study locating aftershocks based on the OBS observation suggests that a part of the NE segment of the source fault dips toward NW. We will also report the result of the source process analysis with a fault model that takes this information into account.