Solid Earth Sciences (S)
Session Sub-category Complex & General(CG)
Session ID S-CG60
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 Makoto Naoi
Affiliation Hokkaido University
Co-Convener 2 Name Keisuke Yano
Affiliation The Institute of Statistical Mathematics
Co-Convener 3 Name Yusuke Tanaka
Affiliation Geospatial Information Authority of Japan
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 Takayuki shinohara (AIST)
Yusuke Matsui (The University of Tokyo)
Time Presentation No Title Presenter
Oral Presentation May 26 PM1
13:45 - 14:00 SCG60-01 Pioneering the Use of Large Language Models in Earthquake Research Hisahiko Kubo
14:00 - 14:15 SCG60-02 Developing anomaly detection method for InSAR time series with the aim of unknown slow-slip event detection Ryunosuke Sakurai
14:15 - 14:30 SCG60-03 Development of anomaly detection using tensor decomposition: An approach based on time series variation data. Harigai Shunto
14:30 - 14:45 SCG60-04 Trend Extraction from Time Series Data Using the State Space Model and Its Application to Anomaly Detection Mayu Tsuchiya
14:45 - 15:15 SCG60-05 Advancing Remote Sensing and Point Cloud Processing through Machine Learning Takayuki shinohara
Oral Presentation May 26 PM2
15:30 - 16:00 SCG60-06 Fast and large-scale similar waveform search Yusuke Matsui
16:00 - 16:15 SCG60-07 Epicenter estimation of tectonic tremors in the nankai subduction zone from a single station using deep learning Amane Sugii
16:15 - 16:30 SCG60-08 B-Value Estimator Using Recurrent Neural Network Naofumi Aso
16:30 - 16:45 SCG60-09 Methodological development of fault slip detection from GNSS using deep learning and application to western Shikoku Ryo Nakagawa
16:45 - 17:00 SCG60-10 Attempt to extract three-dimensional displacement fields due to fault slip by deep learning technique using InSAR and GNSS data Yutaro Okada
Presentation No Title Presenter
Poster Presentation May 26 PM3
SCG60-P01 Improvement of the model architecture of the neural phase picker optimized for the JMA-unified catalog data Makoto Naoi
SCG60-P02 Detecting and locating tectonic tremors in the Nankai subduction zone using deep learning Amane Sugii
SCG60-P03 Automatic detection of T-phase using deep learning Hiroki Kawakami
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 Shimojo
SCG60-P05 Feature Extraction and Classification of Volcanic Earthquake Waveforms Using an Autoencoder Ahyi KIM
SCG60-P06 Detection of earthquakes around Kanagawa prefecture based on machine learning using waveform amplitude images of multiple stations Ryo Kurihara
SCG60-P07 Automatic Generation of Earthquake Cycle Simulation and Data Assimilation Codes Using Large Language Models Masayuki Kano
SCG60-P08 Neural Operators for Earthquake Cycle Simulation and Long-Period Ground Motion Forecast Tomohisa Okazaki
SCG60-P09 Site-specific seismic waveform generation based on the integration of physics theory and deep learning Chenfeng Sheng
SCG60-P10 Preliminary study on earthquake ground motion time-history generation models using generative AI Atsuko Oana
SCG60-P11 Nonlinear friction model of rocks and crust based on rheology model by neural networks Kairi Hara
SCG60-P12 Reservoir Characterization by Machine Learning: Using Core and Logging Data Eigo Miyajima
SCG60-P13 Development of automated detection methods for intragranular cracks using machine learning Ayumi Nakagawa
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 Kawai