Publications

2025

• Kusui, T., H. Nagao, S. Ito, S. Katoh, and T. Tokuda, A deep learning approach to identifying neural SDE models using the signature kernel, 2025 the 28th International Conference on Information Fusion, accepted.

• Nakao, A., T. Kuwatani, S. Ito, and H. Nagao, Adjoint-based marker-in-cell data assimilation for constraining thermal and flow processes from Lagrangian particle records, J. Geophys. Res. Machine Learning and Computation, Vol. 2, e2024JH000288, doi:10.1029/2024JH000288, 2025.

• Nagao, H. and T. Tokuda, 最先端情報科学による地震研究の深化, 計測と制御, Special Feature: Simulation and AI, Vol. 64, No. 3, pp. 145–150, Article, 2025．

2024

• Nagao, H., S. Ito, and M. Matsumura, Dominant mode extraction based on the four-dimensional variational method The Proceedings of 27th International Conference on Information Fusion, doi:10.23919/FUSION59988.2024.10706506, 2024.

• Nakao, A., T. Kuwatani, S. Ito, and H. Nagao, Adjoint-based data assimilation for reconstruction of thermal convection in a highly viscous fluid from surface velocity and temperature snapshots, Geophys. J. Int., Vol. 236, Issue 1, Pages 379–394, doi:10.1093/gji/ggad417, 2024.

• Tomoki, T. and H. Nagao, Wishart Mixture-based Multiple Clustering for Selecting Seismic Stations for Low-frequency Earthquake Detection, Ouyou toukeigaku，Vol.52 No.2, Pages 99-112, doi:10.5023/jappstat.52.99, 2024.

2023

• Tokuda, T and H. Nagao, Seismic-phase detection using multiple deep learning models for global and local representations of waveforms, Geophys. J. Int., Vol. 235, Issue 2, Pages 1163–1182, doi:10.1093/gji/ggad270, 2023

• Nakai, K., T. Nagata, K. Yamada, Y. Saito, T. Nonomura, M. Kano, S. Ito, and H. Nagao, Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction, Geophys. J. Int., Vol. 234, Issue 3, Pages 1786–1805, doi:10.1093/gji/ggad165, 2023.

• Kaneko, R., H. Nagao, S. Ito, H. Tsuruoka, and K. Obara, Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network, J. Geophys. Res. Solid Earth, Vol. 128, Issue 2, doi:10.1029/2022JB024842, 2023.

• Ito S., M. Kano, and H. Nagao, Adjoint-based uncertainty quantification for inhomogeneous friction on a slow-slipping fault, Geophys. J. Int., Vol. 232, Issue 1, Pages 671–683, doi:10.1093/gji/ggac354, 2023.

2022

• Hirose, K., and Y. Terada, Sparse and Simple Structure Estimation via Prenet Penalization, Psychometrika, doi:10.1007/s11336-022-09868-4, 2022.

2021

• Kurihara, R., A. Kato, S. Kurata, and H. Nagao, Detection of low-frequency earthquakes by the matched filter technique using the product of mutual information and correlation coefficient, Earth Planets Space, Vol. 73, No. 225, 2021.

• Matsumura, T., Y. Kuwayama, K. Ueki, T. Kuwatani, Y. Ando, K. Nagata, S. Ito, and H. Nagao, Bayesian modelling of the equation of state for liquid iron in Earth’s outer core, Journal of Geophysical Research - Solid Earth, Vol. 126, Iss. 12, 2021.

• Morikawa, K. and J. K. Kim, Semiparametric optimal estimation with nonignorable nonresponse data, Annals of Statistics, 49, 2991-3014, 2021.

• Kurihara, R. and K. Obara, Spatiotemporal Characteristics of Relocated Deep Low-Frequency Earthquakes Beneath 52 Volcanic Regions in Japan Over an Analysis Period of 14 Years and 9 Months, Journal of Geophysical Research: Solid Earth, Vol. 126, Iss. 10, 2021.

• Tanaka, K., K. Morikawa et al., G20 Summit and emergency medical services in Osaka, Japan, Acute Medicine & Surgery, 8, e661, 2021.

• Kaneko, R., H. Nagao, S. Ito, K. Obara, and H. Tsuruoka, Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images, Lecture Notes in Computer Science, Vol. 12705, pp. 31-43, 2021.

• Sugasawa, S., K. Morikawa and K. Takahata, Bayesian Semiparametric Modeling of Response Mechanism for Nonignorable Missing Data, TEST, 2021.

• Morikawa, K., H. Nagao, S. Ito, Y. Terada, S. Sakai, and N. Hirata, Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression, Geophysical Journal International, 226, 1018-1035, 2021.

• Anzaki, R., S. Ito, H. Nagao, M. Mizumaki, M. Okada, and I. Akai, Phase prediction method for pattern formation in time-dependent Ginzburg-Landau dynamics for kinetic Ising model without a priori assumptions of domain patterns, Physical Review B, Vol. 103, Iss. 9, 094408, 2021.

Conferences

2025

• Nagao, H., S. Katoh and T. Kusui, 人工知能に基づく地震波形表現モデル獲得, Conductivity Anomaly Seminar，The Earthquake Research Institute，Invited，2025.3.14

• Kusui, T., H. Nagao, S. Ito, S. Katoh and T. Tokuda, 連続時間生成モデルによる地震波形データに基づくスロー地震の確率微分方程式表現の獲得， jss2025spring，University of Tsukuba Tokyo Campus，Poster，2025.3.8

• Nagao, H.，最先端の情報科学に基づく固体地球観測データ解析技術・モデリング技術の開発研究，Earthquake and Volcano Observation Research Plan to Contribute to Disaster Mitigation (3rd Phase)，Symposium to report the results of 2024，The University of Tokyo Takeda Hall，Poster，2025.3.6

2024

• Katoh, S., 地震観測データからの地震波検測, SIGAIs 2024 – GeoSciAI2024, Online, Oral, 2024.12.20

• Nagao, H., R. Kaneko, S. Ito, H. Tsuruoka and K. Obara, Detection of Deep Low-Frequency Tremors From Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network, AGU24, Washington, D.C., Oral, 2024.12.10

• Tokuda, T. and H. Nagao, Seismic detection based on unsupervised combination of station-wise phase picks by deep learning, AGU24, Washington, D.C., Oral, 2024.12.9

• Mendo-Perez, G., H. Nagao, S. Katoh and M. Shinohara, Use of sequential models to detect seismic event detection and phase identification using Distributed Acoustic Sensing records of seafloor cable in Sanriku, Japan, AGU24, Washington, D.C., Poster, 2024.12.10

• Katoh, S., H. Nagao, M. Imaizumi and Y. Iio, Enhancement of Phase Picking Models Using Deep Learning by Addressing the Label Imbalance Problem, AGU24, Washington, D.C., Poster, 2024.12.9

• Kusui, T., H. Nagao, S. Ito, S. Katoh and T. Tokuda, Acquiring a Stochastic Differential Equation Representation to Characterize Low-Frequency Tremors from Seismic Waveform Data Using Deep Learning, AGU24, Washington, D.C., Poster, 2024.12.10

• Nagao, H., Four-dimensional variational method for data assimilation and its applications to models in solid Earth science, 2024 Japan-Taiwan Joint Workshop on Inverse Problems and Related Topics, Taiwan, Invited, 2024.11.8

• Tokuda, T. and H. Nagao, 複数観測点の波形を用いた地震検出手法：観測点ごとに得られた深層学習による検出結果の統合, The 2024 SSJ Fall Meeting，Niigata，Oral, 2024.10.22

• Katoh, S., H. Nagao, M. Imaizumi and Y. Iio, ラベル不均衡問題への対処による深層学習を用いた走時読み取りモデルの強化, The 2024 SSJ Fall Meeting，Niigata，Oral, 2024.10.22

• Gerardo Manuel Mendo, H. Nagao, S. Katoh, M. Shinohara, Development of deep-learning methods for seismic event detection in seafloor DAS data: creation of training/validation datasets, The 2024 SSJ Fall Meeting，Niigata，Poster, 2024.10.21

• Kusui, T., H. Nagao, S. Ito, S. Katoh, and T. Tokuda，Acquiring a Stochastic Differential Equation Representation to Characterize Tremors from Seismic Waveform Data Using Deep Learning, The 2024 SSJ Fall Meeting，Niigata，Poster, 2024.10.21

• Kusui, T, H. Nagao, S. Ito,S. Katoh and T. Tokuda, Acquiring a Stochastic Differential Equation Representation to Characterize Low-Frequency Tremors from Seismic Waveform Data Using Deep Learning, International Joint Workshop on Slow-to-Fast Earthquakes 2024, B-CON Plaza, Beppu, Oita, Japan, Poster, 2024.9.27

• Nagao, H., 地震ビッグデータ解析の最前線, symposium"あたらしい統計科学", Kanazawa, Invited, 2024.9.23

• Tokuda, T.，H. Nagao，混合ウィシャートモデルに基づくマルチプル・クラスタリングによる低周波地震検出のための観測点選択，JFSSA2024，Tokyo University of Science, Invited, 2024.9.4

• Tokuda, T., Two-stage approach for transfer learning of earthquake detection model using multiple clustering-based classification, JFSSA2024, Tokyo University of Science, Oral, 2024.9.4

• 楠井 俊朗，長尾 大道，伊藤 伸一，徳田 智磯，加藤 慎也, シグネチャ法を用いたスロー地震の特徴抽出, JFSSA2024, Tokyo University of Science, Oral，2024.9.3

• Nagao, H., 地震学と統計学の接点, 統計サマーセミナー2024, Yuzawa, Invited, 2024.7.30

• Kusui, T., シグネチャ法を用いたスロー地震の特徴抽出, 統計サマーセミナー2024, Yuzawa, Oral, 2024.7.31

• Nagao, H., S. Ito, and M. Matsumura, Dominant mode extraction based on the four-dimensional variational method 27th International Conference on Information Fusion, Venice, Italy, Oral, 2024.7.9

• Katoh, S., H. Nagao and M. Imaizumi, Towards Addressing Challenges in Seismic Wave Arrival-Time Picking Models Using Deep Learning, JSAI2024, Hamamatsu, Oral, 2024.5.30

• Katoh, S., H. Nagao, Y. Iio, H. Katao, M. Sawada, and K. Tomisaka, 深層学習を用いた複数トレースを入力とする深部低周波地震検知手法の開発, JpGU2024, Chiba, Oral, 2024.5.27

• Morikawa, K., H. Nagao, K. Takahara, N. Hirata, 地震波と地震カタログの統合による本震直後の早期余震活動推定, JpGU2024, Chiba, Oral, 2024.5.27

• Tokuda, T. and H. Nagao, 少量データに適用可能な地震波検出モデルの転移学習：マルチプル・クラスタリングを用いた二段階アプローチ, JpGU2024, Chiba, Poster, 2024.5.27

• Gerardo Manuel Mendo Perez and H. Nagao, Creating slow earthquake template catalogs with You Only Search Once algorithm, JpGU2024, Chiba, Poster, 2024.5.27

• Yanagita, M., T. Tokuda, S. Katoh, and H. Nagao, 最適輸送理論に基づく地震波形データからのイベント検出とその分類, JpGU2024, Chiba, Poster, 2024.5.27

• Kusui, T., H. Nagao, and S. Ito, 深層学習を用いた地震波形データからの低周波微動を特徴づける確率微分方程式表現の獲得, JpGU2024, Chiba, Poster, 2024.5.27

2023

• Tokuda, T., and H. Nagao, Seismic-phase detection using multiple deep learning models for global and local representations of waveforms, AGU Fall Meeting 2023, San Fransisco, Poster, 2023.12.13

• Mendo-Perez, G., Semi-automated template matching-based procedure to detect slow earthquakes in western Nankai Trough, Japan, AGU Fall Meeting 2023, San Fransisco, Poster, 2023.12.13

• Katoh, S., Earthquake cataloging by deep learning using Japan's high-dense observation seismic network, AGU Fall Meeting 2023, San Fransisco, Poster, 2023.12.13

• Tokuda, T., and H. Nagao, Seismic-phase detection using multiple deep learning models for global and local representations of waveforms Asia Oceania Geosciences Society, The 20th Annual Meeting (AOGS 2023), Singapore, 2023.8.3

• Morikawa, K., H. Nagao, and N. Hirata, Forecasting aftershocks immediately after the large main shock with epidemic-type aftershock detection function Asia Oceania Geosciences Society, The 20th Annual Meeting (AOGS 2023), Singapore, 2023.8.3

• Kaneko, R., H. Nagao, S. Ito, H. Tsuruoka, and K. Obara, Detection of deep low-frequency tremors from continuous paper records at a station in southwest Japan about 50 years ago based on convolutional neural network The International Union of Geodesy and Geophysics, The 28th General Assembly (IUGG 2023), Berlin, Germany, 2023.7.18

• Tokuda, T., and H. Nagao, Seismic-phase detection using multiple deep learning models for global and local representations of waveforms The International Union of Geodesy and Geophysics, The 28th General Assembly (IUGG 2023), Berlin, Germany, 2023.7.18

• H. Nagao, 4次元変分法データ同化の理論深化と応用展開, 第50回 ものづくり企業に役立つ応用数理手法の研究会, Online, invited, 2023.6.20

• H. Nagao, 人工知能による地震研究の新展開, JSAI2023, Kumamoto, invited, 2023.6.8

• R. Kaneko, 残差学習に基づく地震波形紙記録からの低周波微動の検出, 第37回 人工知能学会全国大会 熊本城ホール（熊本県熊本市）, 口頭発表, 2023.6.6

• K.Morikawa, Forecasting aftershocks immediately after the large main shock with epidemic-type aftershock detection function, JpGU2023, Chiba, Oral, 2023.5.21

• T. Tokuda, 地震波形の全体・局所領域に対する複数の深層学習モデルを統合した地震検出手法, JpGU2023, Chiba, Oral, 2023.5.21

• K. Nakai, プロセス駆動型地震波動場再構成に向けた物理モデルパラメタ推定のための観測点選択, JpGU2023, Chiba, Oral, 2023.5.21

• Mendo-Perez, G., Analysis of the seismo-acoustic signals associated with the explosive activity of Popocatepetl volcano, Mexico, JpGU2023, Chiba, Oral, 2023.5.21

• Kaneko, R., H. Nagao, S. Ito, H. Tsuruoka, and K. Obara, Detection of deep low-frequency tremors from continuous paper records at a station in southwest Japan about 50 years ago based on convolutional neural network, JpGU2023, Chiba, Poster, 2023.5.21

2022

• H. Nagao, Detection of Deep Low-Frequency Tremors from Continuous Paper Records at a Station in Southwest Japan About 50 Years Ago Based on Convolutional Neural Network for Seismogram Images, AGU Fall Meeting 2022, Oral, 2022.12.17

• T. Tokuda, Robust Seismic Phase Detection Method Modeling Both Global and Local Representations of Waveform, AGU Fall Meeting 2022, Poster, 2022.12.16

• H. Nagao, 地震学と情報地質学の接点, シンポジウム2022「新情報地質学：情報地質学の発展Ⅱ」, Invited, 2022.12.2

• H. Nagao, Data Science Techniques to Extract Information from Image Data, The 75th IIW Annual Assembly and International Conference, GRAND NIKKO TOKYO DAIBA, Poster, 2022.7.18

2021

• H. Nagao, Optimization and uncertainty quantification based on the four-dimensional variational method, International Workshop on the Integration of (Simulation + Data + Learning): Towards Society 5.0 by h3-Open-BDEC, Online, 2021.12.

• 栗原亮，加藤愛太郎，倉田澄人，長尾大道，相互情報量と相関係数の積を用いたマッチドフィルタ法による深部低周波地震の検出，日本地震学会2021年度秋季大会，オンライン，2021.10.

• 金子亮介，長尾大道，伊藤伸一，小原一成，鶴岡弘，地震連続波形画像からの深部低周波微動検出に向けた 畳み込みニューラルネットワークの構築，日本地震学会2021年度秋季大会，オンライン，2021.10.

• 森川耕輔，長尾大道，伊藤伸一，寺田吉壱，酒井慎一，平田直，ガウス過程回帰を用いた本震直後における余震分布の推定，日本地震学会2021年度秋季大会，オンライン，2021.10.

• 森川耕輔，長尾大道，伊藤伸一，寺田吉壱，酒井慎一，平田直，ガウス過程回帰を用いた本震直後における余震分布の推定，2021年度 統計関連学会連合大会，オンライン，2021.9.

• 栗原亮，加藤愛太郎，倉田澄人，長尾大道，相互情報量と相関係数の積を用いたマッチドフィルタ法による深部低周波地震の検出，2021年度 統計関連学会連合大会，オンライン，2021.9.

• 森川耕輔，Jae Kwang Kim，標本調査における包含確率の情報を用いたセミパラメトリック漸近有効推定量の提案，2021年度 統計関連学会連合大会，オンライン，2021.9.

• 金子亮介，長尾大道，伊藤伸一，小原一成，鶴岡弘，畳み込みニューラルネットワークを用いた地震波形画像からの深部低周波微動の検出，2021年度 統計関連学会連合大会，オンライン，2021.9.

• Morikawa, K., H. Nagao, S. Ito, Y. Terada, S. Sakai, and N. Hirata, Forecasting temporal variation of aftershocks immediately after a main shock using Gaussian process regression, Asia Oceania Geosciences Society, Online, 2021.8.

• Anzaki, R., S. Ito, H. Nagao M. Mizumaki, M. Okada, and I. Akai, Pattern formation via the time-dependent Ginzburg-Landau equation in spin systems, Asia Oceania Geosciences Society, Online, 2021.8.

• Kaneko, R., H. Nagao, S. Ito, K. Obara, and H. Tsuruoka, Convolutional neural network to detect deep low-frequency tremors from seismic waveform images, Asia Oceania Geosciences Society, Online, 2021.8.

• Beppu, K. and K. Morikawa, On the Verifiable Identification Condition in NMAR Missing Data Analysis, 10th World Congress in Probability and Statistics, Korea, Online, 2021.7.

• Beppu, K. and K. Morikawa, On the Verifiable Identification Condition in NMAR Missing Data Analysis, EcoSta 2021, Online, 2021.6.

• Morikawa, K., H. Nagao, S. Ito, Y. Terada, S. Sakai, and N. Hirata, Prediction of Aftershocks With Gaussian Process Regression: Application to the 2004 Chuetsu Earthquake, JpGU2021, 2021.6.

• Kaneko, R., H. Nagao, S. Ito, K. Obara, and H. Tsuruoka, Convolutional neural network to detect deep low-frequency tremors from seismic waveform images, PAKDD2021 Workshop on Machine Learning for Measurement Informatics (MLMEIN), Online, 2021.5.

• Morikawa, K., H. Nagao, S. Ito, Y. Terada, S. Sakai, and N. Hirata，Forecasting Temporal Variation of Aftershocks Immediately After a Main Shock Using Gaussian Process Regression，地震研究所談話会，Online，2021.3.

• Anzaki, R., S. Ito, H. Nagao, M. Mizumaki, M. Okada, and I. Akai，双極子間相互作用を含む時間依存Ginzburg-Landau方程式によるパターン形成における相分類の提案と解析的な相予測，第68回応用物理学会 春季学術講演会，Online，2021.3.

• Anzaki, R., S. Ito, H. Nagao, M. Mizumaki, M. Okada, and I. Akai，時間依存Ginzburg-Landau方程式による2次元スピン系でのパターン形成に関する理論的研究，日本物理学会 第76回年次大会，Online，2021.3.

• Nakajima, K., T. Furumura, H. Tsuruoka, H. Matsuba, H. Nagao, and T. Hanawa, Integration of 3D earthquake simulation & real-time data assimilation on h3-Open-BDEC, 2021 SIAM Conference on Computational Science and Engineering, Online, 2021.3.

Awards

2024

• Dr. Shinya Katoh received "the First Prize" from GeoSciAI2024 Competition (Earthquake Field).

• Dr. Tomoki and Prof. Nagao received the "JSAS Best Paper Award" from Japanese Society of Applied Statistics.

2023

• Prof. Kazushige Obara received "the Imperial Prize and Japan Academy Prize" from the Japan Academy．

• Prof. Masaaki Imaizumi received the "Young Scientist Award of Commendation for Science and Technology 2023" from the Minister of Education, Culture, Sports, Science and Technology．

2022

• Prof. Kazushige Obara received the "Medal with Purple Ribbon" from the Cabinet Office．

2021

• Ryosuke Kaneko received the "Excellent Reporting Award" from Japanese Joint Statistical Meeting 2021．