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(October, 30th).

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

科目：地震学ゼミナールIV B, D / Seminar on Seismology IV B, D（修士・博士）
日時：2025年 10月 30日 (木) 13:30~
場所：京都大学 防災研究所 本館E-232D

Date and Time：2025-10-30(Thursday), 13:30~
Place：Uji Campus Main Building E232D

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Speaker 1（発表者）:　Shaoyan Zhang
Title（題目）:
Experimental studies on periodic pore-pressure induced episodic slip activities

Abstract（要旨）:
Intermittent landslide movement is usually modulated by the seasonal rainfalls and the fluctuation of groundwater level or river level. Such intermittent movements may exhibit similar slip patterns with episodic slow slip events. Periodic fluid pressure might be the common driving mechanism behind some intermittent landslide movement and episodic slow slip events. We conducted ring-shear experimental studies on saturated granular geomaterials. Episodic slip activities were reproduced under periodic normal stress variation, which is equivalent to periodic water pressure according to the principle of effective stress. We analyzed the mechanical behaviors and acoustic emission signals during laboratory episodic slip activities. These results might be valuable for understanding the natural landslide and earthquake phenomena.

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Speaker 2（発表者）:　Bertrand Rouet-Leduc
Title（題目）:
(To be defined)

Abstract（要旨）:
Faults can accommodate stress in a variety of slip modes, from dynamic rupture to slow slip events and aseismic slip. Among these slip modes, slow slip events remain the most elusive and poorly understood.
However, the difficulty in detecting transient slow slip events, either seismically or geodetically, points to a possible observational gap that may explain the rarity of slow deformation detected prior to dynamic earthquakes.
In this presentation, the use of machine learning to improve the detection of slow slip events will be explored, as a tool to fill this observational gap, by using deep learning techniques to derive reliable deformation time series from InSAR interferogram networks.