今週のうなぎセミナーについてお知らせいたします。

10/24は3時から避難訓練がありますのでその間、ゼミを中断します。
避難訓練終了後、再度ゼミを再開します。よろしくお願いいたします。

Here is information of the Unagi-seminar(October, 23rd).

We have an evacuation drill at 3pm during the seminar.
We will stop the seminar and join the drill.

************** 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 23日 (木) 13:30~
場所：京都大学 防災研究所 本館E-232D

Date and Time：2025-10-23(Thursday), 13:30~
Place：Uji Campus Main Building E232D

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Speaker（発表者）:　Abraham Hernandez
Title（題目）:
Modelling the Shallow Crustal Structure of the Guerrero Seismic Gap from geodetic data: Defining the prior distribution

Abstract（要旨）:
The Guerrero Seismic Gap (GGap) is a segment of ∼ 200 km at the Cocos-North America plate boundary between −99.2◦ and −101.2◦ where no large subduction thrust earthquake has occurred in the SW part of the gap since 1911. Considering the rupture history of large earthquakes in the MSZ, their return times, and the geometric relationship (length versus width of the rupture) of the gap, it is expected that an earthquake with a magnitude of MW ∼ 8.2 could occur. Such an event could generate devastating scenarios for some cities in the state of Guerrero, Acapulco, and Mexico City.
There is physical evidence of subduction of seamounts in the GGap zone, which may play an important role in dissipating energy through medium-sized earthquakes (magnitude MW ≥ 6.5), preventing the nucleation of larger magnitudes. In order to study these structures in detail, studies are needed to describe the configuration of the upper crust and to determine its possible relationship with seismic activity.
Inversion methods in geosciences can address the interpretation of subsurface structure. In particular, gravimetric data inversion seeks to determine a model of the density distribution or depth of bodies of known density that generate anomalies. Statistical inversion provides more robust solutions and prior integration to improve parameter estimation. On this occasion, I will focus on explaining the procedure for arriving at our prior distribution by determining the forward map using various solvers and adjusting the best result to our observed gravity value.