Title: Imaging the magmatic systems under submarine volcanoes with satellite altimetry

 

Abstract:

The magmatic systems under most submarine volcanoes are poorly known and have not been imaged by geophysical techniques, due to their inaccessibility and the time and cost needed for research cruises. In this talk, I investigate the detectability of magma reservoirs in the vertical gravity gradient (VGG) anomalies calculated from satellite altimetry data. We will first look at a suite of synthetic seamount models to identify the expected gravity signature of magmatic systems. One of the largest explosive eruptions instrumentally recorded occurred at Hunga volcano (Tonga) on 15 January 2022. I will show how we can use marine gravity data derived from satellite altimetry combined with multibeam bathymetry to model the architecture and dynamics of the magmatic system before and after the January 2022 Hunga eruption. Finally, we will see how global satellite altimetry data with increasing resolution offer a promising reconnaissance tool to probe the subsurface for magma at other submarine volcanoes, including those in the Izu-Bonin-Mariana arc.

下記のとおり地震研究所談話会を開催いたします。

対面での開催を再開しておりますので、地震研究所へお越しいただければ幸いです。

なお、お知らせするZoom URLの二次配布はご遠慮ください。また、著作権の問題が

ありますので、配信される映像・音声の録画、録音を固く禁じます。

 　　　　　　　　　　　　　　　記

　　　　　　　　　日　　時：　令和7年5月23日(金)　午後1時30分～

　　　　　　　　　場　　所：　地震研究所1号館2階　セミナー室

　　　　　　　　　　　　　　　Zoom Webinarにて同時配信　

1. 13:30-13:45

演題：地震・地殻変動観測に基づく 北アナトリア断層の活動評価【2024年度所長裁量経費成果報告】

著者：○青木陽介・加藤愛太郎・張 基・悪原 岳・竹尾明子・武村俊介・中川茂樹・西田 究、高橋成美（防災科研）、山本揚二朗（JAMSTEC）

2. 13:45-14:00

演題：理工連携による火山ダイナミクスモデル実験【2024年度所長裁量経費成果報告】

著者：○市原美恵、亀田正治(東京農工大学)、Nataliya Galina・村松　弾・柘植鮎太・桑野 修

3. 14:00-14:15

演題：南太平洋島嶼国における火山災害軽減ネットワーク構築【2024年度所長裁量経費成果報告】

著者：○市原美恵・金子隆之・行竹洋平・篠原雅尚・森田雅明・大湊隆雄・三宅弘恵・村松　弾・前野　深・柘植鮎太・三反畑修・望月公廣

○発表者

※時間は質問時間を含みます。

※既に継続参加をお申し出いただいている方は、当日zoom URLを自動送信いたします。

※談話会のお知らせが不要な方は下記までご連絡ください。

〒113－0032　東京都文京区弥生1－1－1　

東京大学地震研究所 共同利用担当

E-mail：k-kyodoriyo(at)eri.u-tokyo.ac.jp

※次回の談話会は令和7年6月20日(金)　午後１時30分～です。

Title: Submarine volcanic processes

 

Abstract:
While submarine volcanoes account for over 70% of volcanism on Earth, their inaccessibility means that our understanding of their active processes remains limited. In this talk, I will first discuss the discovery of a type of seismo-acoustic event which allows the remote tracking of lava flow during submarine eruptions, as well as the most likely source mechanism of these impulsive seafloor events. Subsequently, I will present results from over a decade of continuous, real-time monitoring of Axial Seamount on the Juan de Fuca ridge in the Northeast Pacific Ocean such as the precursory processes leading up to its 2015 eruption, the magmatic and fault systems’ response to tidal loading, and the volcano’s evolution as it builds towards the next imminent eruption.

Title: Operator Learning in Seismology: AI for infinite dimensions

 

Abstract:

Seismology and geophysics are fields in which phenomena are typically described by partial differential equations, i.e. operators that map between infinite dimensional function spaces. However, deep neural networks are designed to work only on finite dimensional vector spaces. An immediate consequence is that deep neural networks are trained for a particular spatial/temporal discretization, and are unable to generalize to arbitrary mesh configurations. For example, suddenly increasing the spatial resolution by 1000x can break deep neural networks, even if the physics is the same at both scales. A recent machine learning paradigm called Operator Learning has developed a variety of Neural Operator models that naturally learn on function spaces, and provably generalize to irregular sensor geometries and arbitrary mesh resolution. This makes Neural Operators an excellent choice for many data-driven and simulation-based problems. In this talk, I will cover the fundamentals of Operator Learning and its importance to problems in seismology and geophysics. I will show applications to earthquake detection and picking; applications to seismic full waveform imaging; generative modeling of seismic ground motions, and more. I will highlight the advantages of using Neural Operators instead of Neural Networks for each of these problems. I will discuss why I believe Operator Learning will be a valuable part of Seismology in the future.