Session Outline
| Solid Earth Sciences (S) | ||||
|---|---|---|---|---|
| Session Sub-category | Complex & General (CG) | |||
| Session ID | S-CG71 | |||
| 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.
Because this session aims to promote the integration of information science, including machine learning, with solid earth sciences, the participants' areas of expertise and technical terminology vary widely; therefore, the session language will be designated as "J." Verbal communication (oral presentation, Q&A etc.) can be given in either English or Japanese, while text in slides/posters must include English (Japanese text may be accompanied).
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| Session Format | Orals and Posters session | |||