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S (Solid Earth Sciences ) » S-CG Complex & General

[S-CG60] Driving Solid Earth Science through Machine Learning

Mon. May 26, 2025 5:15 PM - 7:15 PM Poster Hall (Exhibition Hall 7&8, Makuhari Messe)

convener:Hisahiko Kubo(National Research Institute for Earth Science and Disaster Resilience), Makoto Naoi(Hokkaido University), Keisuke Yano(The Institute of Statistical Mathematics), Yusuke Tanaka(Geospatial Information Authority of Japan)

Machine learning (ML) has brought innovations and remarkable results in various science fields including solid earth science. This session provides an opportunity to inspire each other for future developments by bringing studies of ML applications in various fields including solid earth science. We invite a wide range of presentations on ML and related research, from the budding to the more advanced.

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5:15 PM - 7:15 PM

[SCG60-P01] Improvement of the model architecture of the neural phase picker optimized for the JMA-unified catalog data

*Makoto Naoi¹ (1.Hokkaido University)

5:15 PM - 7:15 PM

[SCG60-P02] Detecting and locating tectonic tremors in the Nankai subduction zone using deep learning

Yuya Jinde¹, *Amane Sugii¹, Yoshihiro Hiramatsu¹ (1.Kanazawa University)

5:15 PM - 7:15 PM

[SCG60-P03] Automatic detection of T-phase using deep learning

*Hiroki Kawakami¹, Naoki Suda¹ (1.Hiroshima University)

5:15 PM - 7:15 PM

[SCG60-P04] Study on improving the unified earthquake catalog of the Japan Meteorological Agency based on phase classification of normal and low-frequency earthquakes

*Kengo Shimojo¹ (1.Meteorological Research Institute)

5:15 PM - 7:15 PM

[SCG60-P05] Feature Extraction and Classification of Volcanic Earthquake Waveforms Using an Autoencoder

*Ahyi KIM¹, Yugo Suzuki¹, Yohei Yukutake² (1.Yokohama City University, 2.Earthquake Research Institute, The University of Tokyo)

5:15 PM - 7:15 PM

[SCG60-P06] Detection of earthquakes around Kanagawa prefecture based on machine learning using waveform amplitude images of multiple stations

*Ryo Kurihara¹ (1.Hot Springs Research Institute of Kanagawa Prefecture)

5:15 PM - 7:15 PM

[SCG60-P07] Automatic Generation of Earthquake Cycle Simulation and Data Assimilation Codes Using Large Language Models

*Masayuki Kano¹, Kazuro Hirahara^2,3, Nobuki Kame⁴, Tomohisa Okazaki² (1.Graduate school of science, Tohoku University, 2.RIKEN, 3.Kagawa University, 4.ERI, Univesity of Tokyo)

5:15 PM - 7:15 PM

[SCG60-P08] Neural Operators for Earthquake Cycle Simulation and Long-Period Ground Motion Forecast

*Tomohisa Okazaki¹, Ryoichiro Agata², Masayuki Kano³, Daisuke Sato², Masayoshi Someya⁴, Rikuto Fukushima⁵ (1.RIKEN Center for Advanced Intelligence Project, 2.Japan Agency for Marine-Earth Science and Technology, 3.Tohoku University, 4.Earthquake Research Institute, University of Tokyo, 5.Stanford University)

5:15 PM - 7:15 PM

[SCG60-P09] Site-specific seismic waveform generation based on the integration of physics theory and deep learning

*Chenfeng Sheng^1,2, Hiroe Miyake¹ (1.Earthquake Research Institute, The University of Tokyo, 2.Department of Earth and Planetary Science, The University of Tokyo)

5:15 PM - 7:15 PM

[SCG60-P10] Preliminary study on earthquake ground motion time-history generation models using generative AI

*Atsuko Oana¹, Takahiro Matsui¹, Masahito Tsukano, Toru Ishii¹, Misaki Yokoji¹, Kei Furukawa¹, Shunichi Taniguchi² (1.Shimizu Corporation, 2.Lightblue, Co., Ltd.)

5:15 PM - 7:15 PM

[SCG60-P11] Nonlinear friction model of rocks and crust based on rheology model by neural networks

*Kairi Hara¹, Hiroyuki Nagahama¹, Jun Muto¹ (1.Department of Earth Sciences, Tohoku University)

5:15 PM - 7:15 PM

[SCG60-P12] Reservoir Characterization by Machine Learning: Using Core and Logging Data

*Eigo Miyajima¹, Kosuke Egawa¹ (1.Kyushu University)

5:15 PM - 7:15 PM

[SCG60-P13] Development of automated detection methods for intragranular cracks using machine learning

*Ayumi Nakagawa¹, Shinichi Uehara¹ (1.Toho University)

5:15 PM - 7:15 PM

[SCG60-P14] Evaluation of estimation methods for physicochemical information of equilibrium melt by multivariate analysis of high-T and high-P experimental data of clinopyroxene

*Ryosuke Kawai¹, Ikuo Okada², Shunsuke Munechika¹, Tomoyuki Shibata¹ (1.Graduate School of Advanced Science and Engineering, Hiroshima University, 2.Hiruzen Institute for Geology and Chronology Co., Ltd.)

Session information

[S-CG60] Driving Solid Earth Science through Machine Learning

[SCG60-P01] Improvement of the model architecture of the neural phase picker optimized for the JMA-unified catalog data

[SCG60-P02] Detecting and locating tectonic tremors in the Nankai subduction zone using deep learning

[SCG60-P03] Automatic detection of T-phase using deep learning

[SCG60-P04] Study on improving the unified earthquake catalog of the Japan Meteorological Agency based on phase classification of normal and low-frequency earthquakes

[SCG60-P05] Feature Extraction and Classification of Volcanic Earthquake Waveforms Using an Autoencoder

[SCG60-P06] Detection of earthquakes around Kanagawa prefecture based on machine learning using waveform amplitude images of multiple stations

[SCG60-P07] Automatic Generation of Earthquake Cycle Simulation and Data Assimilation Codes Using Large Language Models

[SCG60-P08] Neural Operators for Earthquake Cycle Simulation and Long-Period Ground Motion Forecast

[SCG60-P09] Site-specific seismic waveform generation based on the integration of physics theory and deep learning

[SCG60-P10] Preliminary study on earthquake ground motion time-history generation models using generative AI

[SCG60-P11] Nonlinear friction model of rocks and crust based on rheology model by neural networks

[SCG60-P12] Reservoir Characterization by Machine Learning: Using Core and Logging Data

[SCG60-P13] Development of automated detection methods for intragranular cracks using machine learning

[SCG60-P14] Evaluation of estimation methods for physicochemical information of equilibrium melt by multivariate analysis of high-T and high-P experimental data of clinopyroxene

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