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(December, 11th).

************** 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 12月 11日 (木) 13:30~
場所：京都大学 防災研究所 本館E-232D

Date and Time：2025-12-11(Thursday), 13:30~
Place：Uji Campus Main Building E232D

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Speaker 1（発表者）:　福田公平（Kohei FUKUDA）
Title（題目）:
Ambient Noise Detection through Array Processing with a Dense Seismic Network

Abstract（要旨）:
Seismic waves (especially surface waves) are generated by various phenomena. Analysis of wave sources, magnitude, and frequency of these events is important for disaster prevention, improving the accuracy of observed seismic waveforms, and understanding ecosystems. AELUMA method can detect the epicenter of such events of ambient noise by using array analysis for surface waves. It is reported that AELUMA detected stormquake, landslide, and tsunami event of submarine volcano.
In this study, we attempt to improve the AELUMA method for surface wave analysis. This presentation discusses the challenges identified and their causes when applying the AELUMA method to F-net records of earthquakes near the Tokara Islands. It also introduces strategies for improving the methodology.

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Speaker 2（発表者）:　平子　豪（Go HIRAKO）
Title（題目）:
How much does the density of GNSS stations affect the uncertainty for estimating the slip rate of inland active faults?

Abstract（要旨）:
Research on estimating fault strain accumulation rates—which is indispensable for evaluating the potential of inland earthquakes—has traditionally employed methods such as block fault models (e.g., Hashimoto, 2000). However, these models have various limitations; for instance, because block models divide a region along major faults, they struggle to accurately model faults with small or complex geometries. Recently, Johnson et al. (2024) proposed an inversion method to estimate fault slip deficit rates using surface strain rates derived from GNSS data in the vicinity of faults, excluding the area directly above them. This method offers the advantage of simultaneously estimating slip deficit rates for multiple faults with small, complex geometries that previous models failed to capture.
In this study, building upon this approach, we attempt to estimate the slip velocities of inland active faults via inversion. We construct Delaunay triangles using GNSS stations as vertices and utilize the relative displacement velocities between pairs of observation points connected by the triangle edges as input data. With the ultimate goal of estimating slip velocities for active faults across the Japanese archipelago using data from both GEONET and SoftBank GNSS stations, this presentation discusses the extent to which differences in observation station density influence the uncertainties of the estimated fault slip velocities.