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

[S-CG50] Driving Solid Earth Science through Machine Learning

Sun. May 26, 2024 5:15 PM - 6:45 PM Poster Hall (Exhibition Hall 6, Makuhari Messe)

convener:Hisahiko Kubo(National Research Institute for Earth Science and Disaster Resilience), Yuki Kodera(Meteorological Research Institute, Japan Meteorological Agency), Makoto Naoi(Hokkaido University), Keisuke Yano(The Institute of Statistical Mathematics)

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 - 6:45 PM

[SCG50-P01] Multi-class anomaly detection from seismic video

*Hiroki Azuma¹, Hiroyuki Fujiwara^2,1 (1.University of Tsukuba, 2.NIED)

5:15 PM - 6:45 PM

[SCG50-P02] Surrogate modeling of hydrothermal systems utilizing the framework of continual learning

*Kazuya Ishitsuka¹ (1.Kyoto University)

5:15 PM - 6:45 PM

[SCG50-P03] Uncertainty quantification in seismic forward and inversion problems using physics-informed generative adversarial neural networks

*Yi Ding¹, Su Chen¹, Xiaojun Li^1,2, Suyang Wang¹ (1.Beijing University of Technology, 2.Institute of Geophysics, China Earthquake Administration)

5:15 PM - 6:45 PM

[SCG50-P04] Empirical knowledge-informed deep learning approach for ground motion prediction equations

*Tomohisa Okazaki¹, Asako Iwaki², Nobuyuki Morikawa², Hiroyuki Fujiwara², Naonori Ueda¹ (1.RIKEN Center for Advanced Intelligence Project, 2.National Research Institute for Earth Science and Disaster Resilience)

5:15 PM - 6:45 PM

[SCG50-P05] Attempt to detect tsunami magnetic field using machine learning

*Chiaki Mita¹, Takuto Minami¹, Hiroaki Toh², Hiroko Sugioka¹ (1.Graduate school of Science, Kobe University, 2.Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University)

5:15 PM - 6:45 PM

[SCG50-P06] Automated detection and hypocenter determination in tectonic tremors using convolutional neural network

*Amane Sugii¹, Yoshihiro Hiramatsu¹, Takahiko Uchide², Kazutoshi Imanishi² (1.Kanazawa University, 2.National Institute of Advanced Industrial Science and Technology)

5:15 PM - 6:45 PM

[SCG50-P07] Spectral clustering-based association analysis of tremor detected stations: application to S-net

*Kodai Sagae¹, Masayuki Kano², Takahiko Uchide¹ (1.Geological Survey of Japan, AIST, 2.Department of Geophysics, Graduate School of Science, Tohoku University)

5:15 PM - 6:45 PM

[SCG50-P08] Validation of Interpretability Enhancement in Volcanic Earthquake Classification Using Transformer Encoders

*Yugo Suzuki¹, Ahyi KIM¹, Masami Yamasaki¹, Yohei Yukutake² (1.Yokohama City University, 2.Earthquake Research Institute, University of Tokyo)

5:15 PM - 6:45 PM

[SCG50-P09] Application of seismic detection techniques based on deep learning: Toward practical use of inland low-frequency earthquakes

*Shiori Suzuki¹, Takuto Maeda², Tomoya Takano² (1.Faculty of Science and Technology, Hirosaki University, 2.Graduate School of Science and Technology, Hirosaki University)

5:15 PM - 6:45 PM

[SCG50-P10] Toward a High-Performance Volcanic Earthquake Phase Detection: R2AU-Net Transfer Learning and Hyperparameter Analysis

*Yuji Nakamura¹, Yukino Yazaki¹, Yohei Yukutake², Yuki Abe³, Ahyi KIM¹ (1.Yokohama City University, 2.Earthquake Research Institute, the University of Tokyo, 3.Hot Springs Research Institute of Kanagawa Prefecture)

5:15 PM - 6:45 PM

[SCG50-P11] Prediction of Mt. Aso eruption by multi-species large-scale monitoring data analysis

*Minoru Luke Ideno¹, Takeshi Tsuji¹ (1.University of Tokyo)

5:15 PM - 6:45 PM

[SCG50-P12] Application of Convolutional Neural Networks for Seismic Velocity Model Building

*YU FAN¹, Ehsan Jamali Hondori², Jin-Oh Park¹ (1.Atmosphere and Ocean Research Institute, The University of Tokyo, 2.Geoscience Enterprise Inc.)

5:15 PM - 6:45 PM

[SCG50-P13] On the application of a suite of computer vision algorithms for natural fracture detection in borehole images

Daniil Kuprin², *Nikita Dubinya^1,2 (1.Moscow Institute of Physics and Technology, 2.Schmidt Institute of Physics of the Earth)

5:15 PM - 6:45 PM

[SCG50-P14] Preseismic anomaly detection of atmospheric radon concentration using Random Forest analysis

*Mayu Tsuchiya¹, Hiroyuki Nagahama¹, Jun Muto¹, Mitsuhiro Hirano¹, Yumi Yasuoka² (1.Department of Earth Sciences, Tohoku University, 2.Kobe Pharmaceutical University)

5:15 PM - 6:45 PM

[SCG50-P15] Detection of atmospheric radon concentration anomalies related to earthquakes using a statistical time series model

*Akito Miura¹, Yumi Yasuoka², Jun Muto¹, Hiroyuki Nagahama¹ (1.Department of Earth Science, Tohoku University, 2.Radiation Control Room, Faculty of Pharmaceutical Sciences, Kobe Pharmaceutical University)

Session information

[S-CG50] Driving Solid Earth Science through Machine Learning

[SCG50-P01] Multi-class anomaly detection from seismic video

[SCG50-P02] Surrogate modeling of hydrothermal systems utilizing the framework of continual learning

[SCG50-P03] Uncertainty quantification in seismic forward and inversion problems using physics-informed generative adversarial neural networks

[SCG50-P04] Empirical knowledge-informed deep learning approach for ground motion prediction equations

[SCG50-P05] Attempt to detect tsunami magnetic field using machine learning

[SCG50-P06] Automated detection and hypocenter determination in tectonic tremors using convolutional neural network

[SCG50-P07] Spectral clustering-based association analysis of tremor detected stations: application to S-net

[SCG50-P08] Validation of Interpretability Enhancement in Volcanic Earthquake Classification Using Transformer Encoders

[SCG50-P09] Application of seismic detection techniques based on deep learning: Toward practical use of inland low-frequency earthquakes

[SCG50-P10] Toward a High-Performance Volcanic Earthquake Phase Detection: R2AU-Net Transfer Learning and Hyperparameter Analysis

[SCG50-P11] Prediction of Mt. Aso eruption by multi-species large-scale monitoring data analysis

[SCG50-P12] Application of Convolutional Neural Networks for Seismic Velocity Model Building

[SCG50-P13] On the application of a suite of computer vision algorithms for natural fracture detection in borehole images

[SCG50-P14] Preseismic anomaly detection of atmospheric radon concentration using Random Forest analysis

[SCG50-P15] Detection of atmospheric radon concentration anomalies related to earthquakes using a statistical time series model

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