JpGU Meeting 2019
Session outline| Solid Earth Sciences (S) | ||
|---|---|---|
| Session Sub-category | Complex & General(CG) | |
| Session ID | S-CG62 | |
| Title | Potentiality of Machine Learning in Solid Earth Sciences | |
| Short Title | Machine Learning in Solid Earth Sciences | |
| Main Convener | Name | Takahiko Uchide |
| Affiliation | Research Institute of Earthquake and Volcano Geology, Geological Survey of Japan, National Institute of Advanced Industrial Science and Technology (AIST) | |
| Co-Convener 1 | Name | Hirokuni Oda |
| Affiliation | Institute of Geology and Geoinformation, Geological Survey of Japan, AIST | |
| Session Language | J | |
| Scope | The recent development of the machine learning techniques including the deep learning is leading innovations in various fields. The applications of these techniques to solid earth sciences are also expected to develop new frontiers by, for example, the classification, the pattern recognition, and the regression of data. On the other hand, there are concerns on the human-interpretability of the deep learning. This session will provide an opportunity to share the application studies to various fields in solid earth science and inspire each other. We also look forward to studies addressing the black box issue of the deep learning. | |
| Presentation Format | Oral and Poster presentation | |
| Invited Authors | Gregory C Beroza (Stanford University) Tsuyoshi Murata (Tokyo Institute of Technology) | |
| Time | Presentation No | Title | Presenter | Abstract |
|---|---|---|---|---|
| Oral Presentation May 26 AM1 | ||||
| 09:00 - 09:20 | SCG62-01 | The Promise of Machine Learning in Solid Earth Geoscience: Examples from Seismology | Gregory C Beroza | |
| 09:20 - 09:35 | SCG62-02 | Automatic Approach to Low-Frequency Earthquakes Detection in Southwest Japan Based on Deep Learning Technique | Yu Junyu | |
| 09:35 - 09:50 | SCG62-03 | Broadband ground motion waveform synthesis utilizing AI-based upsampling technique (2) | Takahiro Maeda | |
| 09:50 - 10:05 | SCG62-04 | Forecast trial of postseismic deformetion after the 2011 Tohoku-Oki earthquake by recurrent neural network | Norifumi Yamaga | |
| 10:05 - 10:25 | SCG62-05 | Deep Learning Approaches for Eruption Prediction of Sakurajima | Tsuyoshi Murata | |
| Presentation No | Title | Presenter | Abstract |
|---|---|---|---|
| Poster Presentation May 26 PM2 | |||
| SCG62-P01 | Earthquake Magnitude Prediction with Seismic Nucleation Phase based on Machine Learning | Kangming Wu | |
| SCG62-P02 | Automatic signal classification for continuous seismic waveform records based on an unsupervised learning algorithm: Application to artificial noises and low-frequency tremors | Yuki Kodera | |
| SCG62-P03 | Detection of Gas Bubble signals recorded at the OBS Stations by Machine-Learning | emmy TY CHANG | |
| SCG62-P04 | Automatic Phase Picking of Seismograms by Deep Neural Network | Takahiko Uchide | |
| SCG62-P05 | Seismic event detection in time-frequency domain with CNN and phase picking with HED | Liping Fan | |
| SCG62-P06 | Broadband ground-motion synthesis using embeddig machine learning | Tomohisa Okazaki | |
| SCG62-P07 | LSTM and CNN Applications to Forecast Earthquake Magnitude Probability Distribution | Qi Liu | |
| SCG62-P08 | FORCsensei: A machine learning framework to estimate optimized first-order reversal curve distributions | Hirokuni Oda | |