今週のうなぎセミナーについてお知らせいたします。

Here is information of the Unagi-seminar(May, 1).

************** Seminar on Seismology IV A, C /地震学ゼミナールIV A, C (Unagi Seminar) **************

科目：地震学ゼミナールIV A, C / Seminar on Seismology IV A, C（修士・博士）
日時：2025年 5月 1日 (木) 13:30~
場所：京都大学 防災研究所 本館E-232D

Date and Time：2025-05-01, 13:30~
Place：Uji Campus Main Building E232D

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Speaker 1（発表者）:　Kai Koyama

Title（題目）:
Global investigation of foreshock acceleration prior to large earthquakes

Abstract（要旨）:
The acceleration of foreshocks has gained significant attention in the context of earthquake forecasting. Bouchon et al. (2013) claimed that many large earthquakes occurred at plate boundaries were preceded by accelerating foreshock activities. Nishikawa & Ide (2018) also reported an accelerating foreshock activity immediately before the M 6.9 interplate earthquake in the Japan Trench subduction zone in 2008. However, Bouchon et al. (2013) have been criticized for inadequately accounting for the effects of earthquake clustering (Felzer et al., 2015). Furthermore, Nishikawa & Ide (2018) analyzed only a few large earthquakes, and it is unclear whether similar acceleration phenomena can be observed globally.
We investigated foreshock activity before large earthquakes globally using the epidemic-type aftershock-sequence (ETAS) model (Ogata, 1988), a standard statistical model of seismicity. Based on previous numerical simulations and rock experiments (e.g., McLaskey, 2019), we incorporated a new term representing the accelerating foreshock activity into the ETAS model. This term, similar to the inverse Omori law, describes a power-law acceleration of the seismicity rate leading to a mainshock (= L/(Teq - t + d)q), where t is time, Teq is the mainshock origin time, and L, d, and q are new model parameters. We applied the model to seismicity (M 4.5 or larger and within 100 km of a large earthquake) preceding 288 M 6.5 or larger earthquakes from 2000 to 2024 in the ANSS earthquake catalog.
Our results show that approximately 2% of the large earthquakes exhibited significant foreshock acceleration. For example, the M 6.9 earthquake analyzed by Nishikawa & Ide (2018) and a M 7.3 interplate earthquake in the Vanuatu subduction zone in 2008 showed notable foreshock acceleration. We also performed a similar analysis using the Japan Meteorological Agency catalog and the QTM Seismicity Catalog (a template matching catalog for southern California), and obtained very similar result.

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Speaker 2（発表者）:　RUAN Yihuan

Title（題目）:
Estimating Crustal Structure Using Receiver Function and Ambient Noise Analysis: Insight from MCMC Joint Inversion

Abstract（要旨）:
Deep low-frequency tremor was first detected in the Nankai subduction zone in southwest Japan. It has since been found that a broad family of observations associated with slow earthquakes is distributed across subduction systems worldwide. To better understand their generation mechanisms and roles during the seismic cycle, one effective approach is to investigate the Earth's internal structure and anisotropic properties in the relevant regions.
Receiver function (RF) analysis and shear wave splitting techniques can estimate the fast polarization direction (FPD) and splitting time, both of which are key parameters for inferring anisotropic characteristics. However, the inherent limitations of RF lead to a trade-off between seismic velocity contrasts and the thicknesses of velocity discontinuities. Moreover, when applying RF analysis to higher-frequency signals, reverberations from shallow structures can significantly affect the interpreted discontinuity depth, velocity structure, and anisotropic results.
The dense distribution of Hi-net stations enables the application of ambient noise surface wave tomography—a seismic interferometry technique that retrieves seismic wave propagation between station pairs through cross-correlation of continuous records. We utilized two years of continuous data for the ambient noise analysis to minimize seasonal effects. Due to the characteristics of the Hi-net sensors, the resulting tomography is more sensitive to shallow velocity structures and has limited resolution at greater depths.
By combining two datasets that provide complementary constraints, a joint inversion is conducted using a Markov Chain Monte Carlo (MCMC) algorithm.

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