Tokyo Metropolitan University
Tokyo Metropolitan University
National Astronomical Observatory of Japan
Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA)(ISAS)
National Institute of Technology, Oita College
Shizuoka University
Tokyo Metropolitan University
出版者
宇宙航空研究開発機構(JAXA)
出版者（英）
Japan Aerospace Exploration Agency (JAXA)
雑誌名
宇宙航空研究開発機構研究開発報告: 宇宙科学情報解析論文誌: 第6号
雑誌名(英)
JAXA Research and Development Report: Journal of Space Science Informatics Japan: Volume 6
NASA had obtained the moonquake data for about 7 years. The data is available to study the lunar internal structure and the focal mechanisms of moonquakes. Classification of sources of the deep moonquakes is one of important issues. The conventional method to classify deep moonquake sources is mutual comparison among waveforms. Recent machine learning approach enables us to improve the detection of moonquake, and classification of the sources. In this paper, we investigate the effective features to classify the moonquake sources. As a result, we showed that power spectral density of moonquake, and distance between the moon and the earth are effective features to classify the deep moonquake sources using the machine learning approach.
内容記述
形態: カラー図版あり
内容記述（英）
Physical characteristics: Original contains color illustrations
SVMによる深発月震分類のための有効な特徴量の分析
AA1630049004.pdf
(1.44MB)
