Global geologic mapping for elucidation of origin and evolution of asteroid Vesta

• Project/Area Number: 17K05644
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Solid earth and planetary physics
• Research Institution: Japan Aerospace EXploration Agency (2017, 2020-2022); National Institute for Environmental Studies (2018-2019)
• Principal Investigator: Ishihara Yoshiaki 国立研究開発法人宇宙航空研究開発機構, 国際宇宙探査センター, 主任研究開発員 (80400232)
• Co-Investigator(Kenkyū-buntansha): 晴山 慎 聖マリアンナ医科大学, 医学部, 助教 (60327286)
• Project Period (FY): 2017-04-01 – 2023-03-31
• Project Status: Completed (Fiscal Year 2022)
• Budget Amount: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000); Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000); Fiscal Year 2018: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2017: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
• Keywords: 小天体 / 地質図 / 機械学習 / ベスタ / クラスタリング / 小惑星 / 地質 / 機械分類 / 惑星起源・進化

Outline of Final Research Achievements

By applying integrated analysis using machine learning methods to various exploration data obtained by the Dawn spacecraft, which was conducted by the National Aeronautics and Space Administration (NASA), a global reflectance spectral feature classification of Vesta based on the latest observation data was created. Using a resulting classification map together with topographical information and elemental distribution., information on the geological distribution and internal structure of Vesta was obtained. This method demonstrated the effectiveness of the machine learning approach in creating a global geological map based on the visual inspection and interpretation of data by researchers, which has been impossible with conventional interpretation by researchers.

Academic Significance and Societal Importance of the Research Achievements

近年めざましく発展している機械学習手法を用いて米国Dawn探査機の観測データにクラスタリング解析を行い、研究者の手作業では実現困難な全球地質図の作成が可能であること、またクラスタリング結果は鉱物特徴の違いを捉えたものであることを示した。同手法はVesta以外の探査データにも適用出来、既存の探査データに適用し他天体での全球地質図の作成やその結果に基づいて詳細解析計画を立案し、さらには将来探査のターゲット領域の決定等に役立てることが可能である。巨額を投じる探査計画のより効率的な推進にも貢献するものである。

Research Products

• [Presentation] Vestian Reflectance Spectral Characteristic Map Based on Unsupervised Classification of Multiband Reflectance Data Obtained by DAWN Framing Camera and Its Relation of Chemical Composition and Topographic Features. (2023)
  • Author(s): Ishihara, Y., Hareyama, M. and Ohtake, M.
  • Organizer: The 54th Lunar and Planetary Science Conference (LPSC)
  • Int'l Joint Research
• [Presentation] 教師無し分類手法を用いた小惑星ベスタの地質分類の試み (2019)
  • Author(s): 石原吉明 ・晴山慎 ・大竹真紀子
  • Organizer: 日本惑星科学会2019年秋季講演会
• [Presentation] Preliminary Unsupervised Classification of 4 Vesta’s Surface using Multiband Reflectance Data Obtained by DAWN Framing Camera. (2018)
  • Author(s): Ishihara, Y., M. Hareyama, M. Ohtake
  • Organizer: AOGS 15th Annual Meeting
  • Int'l Joint Research
• [Presentation] Preliminary Unsupervised Classification of the 4 Vesta’s Surface using Multiband Reflectance Data Obtained by DAWN Framing Camera (2018)
  • Author(s): Ishihara, Y., M. Hariyama, M. Ohtake
  • Organizer: 49th Lunar and Planetary Science Conference
  • Int'l Joint Research