Solid Earth Sciences (S)
Session Sub-category Complex & General(CG)
Session ID S-CG50
Title Driving Solid Earth Science through Machine Learning
Short Title Machine Learning in Solid Earth Sciences
Main Convener Name Hisahiko Kubo
Affiliation National Research Institute for Earth Science and Disaster Resilience
Co-Convener 1 Name Yuki Kodera
Affiliation Meteorological Research Institute, Japan Meteorological Agency
Co-Convener 2 Name Makoto Naoi
Affiliation Hokkaido University
Co-Convener 3 Name Keisuke Yano
Affiliation The Institute of Statistical Mathematics
Session Language 
J
Scope 
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.
Presentation Format Oral and Poster presentation
Invited Authors Kan Hatakeyama (Tokyo Institute of Technology)
Hirotaka Hachiya (Wakayama University)
Time Presentation No Title Presenter
Oral Presentation May 27 AM1
09:00 - 09:15 SCG50-01 Retraining of Neuro picker based on JMA unified seismic catalog Makoto Naoi
09:15 - 09:30 SCG50-02 Multiscale Fault Estimation in California and Oklahoma Yasunori Sawaki
09:30 - 09:45 SCG50-03 Physics-driven deep learning method for seismic wave modeling Yi Ding
09:45 - 10:15 SCG50-04 A deep learning-based approach for forecasting ground motion and precipitation Hirotaka Hachiya
Oral Presentation May 27 AM2
10:45 - 11:15 SCG50-05 Recent Trials to Train Large Language Models for Scientific Data and Reasoning Kan Hatakeyama
11:15 - 11:30 SCG50-06 Preliminary investigation of the application of large language models to geotechnical problems Wu Stephen
11:30 - 11:45 SCG50-07 Detection of slow slips from seismic wave records using random forest Kazuki Ohtake
11:45 - 12:00 SCG50-08 Attention-based Machine Learning Model for Magnitude Estimation JI ZHANG
Presentation No Title Presenter
Poster Presentation May 26 PM3
SCG50-P01 Multi-class anomaly detection from seismic video Hiroki Azuma
SCG50-P02 Surrogate modeling of hydrothermal systems utilizing the framework of continual learning Kazuya Ishitsuka
SCG50-P03 Uncertainty quantification in seismic forward and inversion problems using physics-informed generative adversarial neural networks Yi Ding
SCG50-P04 Empirical knowledge-informed deep learning approach for ground motion prediction equations Tomohisa Okazaki
SCG50-P05 Attempt to detect tsunami magnetic field using machine learning Chiaki Mita
SCG50-P06 Automated detection and hypocenter determination in tectonic tremors using convolutional neural network Amane Sugii
SCG50-P07 Spectral clustering-based association analysis of tremor detected stations: application to S-net Kodai Sagae
SCG50-P08 Validation of Interpretability Enhancement in Volcanic Earthquake Classification Using Transformer Encoders Yugo Suzuki
SCG50-P09 Application of seismic detection techniques based on deep learning: Toward practical use of inland low-frequency earthquakes Shiori Suzuki
SCG50-P10 Toward a High-Performance Volcanic Earthquake Phase Detection: R2AU-Net Transfer Learning and Hyperparameter Analysis Yuji Nakamura
SCG50-P11 Prediction of Mt. Aso eruption by multi-species large-scale monitoring data analysis Minoru Luke Ideno
SCG50-P12 Application of Convolutional Neural Networks for Seismic Velocity Model Building FAN YU
SCG50-P13 On the application of a suite of computer vision algorithms for natural fracture detection in borehole images Nikita Dubinya
SCG50-P14 Preseismic anomaly detection of atmospheric radon concentration using Random Forest analysis Mayu Tsuchiya
SCG50-P15 Detection of atmospheric radon concentration anomalies related to earthquakes using a statistical time series model Akito Miura