3-D inversion of magnetic data based on the L1–L2 norm regularization
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- Material Type
- 記事
- Author/Editor
- Mitsuru Utsugi
- Publication, Distribution, etc.
- Publication Date
- 2019-07-03
- Publication Date (W3CDTF)
- 2019-07-03
- Periodical title
- EPS : Earth, Planets and Space
- No. or year of volume/issue
- 71(73)
- Volume
- 71(73)
- ISSN (Periodical Title)
- 1880-5981
- ISSN-L (Periodical Title)
- 1343-8832
- Text Language Code
- eng
- DOI
- 10.1186/s40623-019-1052-4
- Persistent ID (NDL)
- info:ndljp/pid/11377498
- Collection
- Collection (Materials For Handicapped People:1)
- Collection (particular)
- 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
- Acquisition Basis
- オンライン資料収集制度
- Date Accepted (W3CDTF)
- 2019-10-28T20:45:15+09:00
- Date Captured (W3CDTF)
- 2019-10-28
- Format (IMT)
- application/pdf
- Access Restrictions
- 国立国会図書館内限定公開
- Service for the Digitized Contents Transmission Service
- 図書館・個人送信対象外
- Availability of remote photoduplication service
- 可
- Periodical Title (URI)
- Periodical Title (Persistent ID (NDL))
- info:ndljp/pid/11245705
- Data Provider (Database)
- 国立国会図書館 : 国立国会図書館デジタルコレクション
- Summary, etc.
- Magnetic inversion is one of the popular methods to obtain information about the subsurface structure. However, many of the conventional methods have a serious problem, that is, the linear equations to be solved become ill-posed, under-determined, and thus, the uniqueness of the solution is not guaranteed. As a result, several different models fit the observed magnetic data with the same accuracy. To reduce the non-uniqueness of the model, conventional studies introduced regularization method based on the quadratic solution norm. However, these regularization methods impose a certain level of smoothness, and as the result, the resultant model is likely to be blurred. To obtain a focused magnetic model, I introduce L1 norm regularization. As is widely known, L1 norm regularization promotes sparseness of the model. So, it is expected that, the resulting model is constructed only with the features truly required to reconstruct data and, as a result, a simple and focused model is obtained. However, by using L1 norm regularization solely, an excessively concentrated model is obtained due to the nature of the L1 norm regularization and a lack of linear independence of the magnetic equations. To overcome this problem, I use a combination of L1 and L2 norm regularization. To choose a feasible regularization parameter, I introduce a regularization parameter selection method based on the L-curve criterion with fixing the mixing ratio of L1 and L2 norm regularization. This inversion method is applied to a real magnetic anomaly data observed on Hokkaido Island, northern Japan and reveals the subsurface magnetic structure on this area.
- DOI
- 10.1186/s40623-019-1052-4
- Access Restrictions
- インターネット公開
- Rights (production)
- © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Related Material (URI)
- Is Referenced By
- Magnetic inversion to recover the subsurface block structures based on<i>L</i>1 norm and total variation regularizationMagnetization structure of Nishinoshima volcano, Ogasawara island arc, obtained from magnetic surveys using an unmanned aerial vehicleAeromagnetic survey in Kusatsu-Shirane volcano, central Japan, by using an unmanned helicopter
- References
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- References (URI)
- Data Provider (Database)
- 国立情報学研究所 : CiNii Research
- Original Data Provider (Database)
- 学術機関リポジトリデータベース雑誌記事索引データベースCrossrefCiNii Articles科学研究費助成事業データベースCrossrefCrossrefCrossref
- Bibliographic ID (NDL)
- 11377498
- NAID
- 120006653084