The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12670710 B1

Date of Patent:

Jun. 30, 2026

Filed:

May. 14, 2023

Method and apparatus for using transfer learning to locate igneous carbonate rock-type rare earth deposits

Applicants:

Guoqiang Xue, Beijing, CN;

Pengfei LV, Beijing, CN;

Weiying Chen, Beijing, CN;

Ya Xu, Beijing, CN;

Xin Wu, Beijing, CN;

Jian Wang, Beijing, CN;

Xianhua LI, Beijing, CN;

Inventors:

Guoqiang Xue, Beijing, CN;

Pengfei Lv, Beijing, CN;

Weiying Chen, Beijing, CN;

Ya Xu, Beijing, CN;

Xin Wu, Beijing, CN;

Jian Wang, Beijing, CN;

Xianhua Li, Beijing, CN;

Assignee:

INSTITUTE OF GEOLOGY AND GEOPHYSICS, CHINESE ACADEMY OF SCIENCES, Beijing, CN;

Attorney:

Primary Examiner:

Gandhi Thirugnanam

Int. Cl.

CPC ...

G06V 20/10 (2022.01); G06V 10/26 (2022.01); G06V 10/70 (2022.01); G06V 20/60 (2022.01);

U.S. Cl.

CPC ...

G06V 20/10 (2022.01); G06V 10/26 (2022.01); G06V 10/70 (2022.01); G06V 20/60 (2022.01);

Abstract

The present application proposes a method and apparatus for transfer learning-based localization of igneous carbonate-hosted rare earth mineralization. The technology relates to electromagnetic exploration and includes obtaining exploration data for electric, magnetic, seismic, and gravity methods in the target area. Transfer learning-based localization is applied to the electric, magnetic, seismic, and gravity exploration data to determine the cross-sectional map corresponding to the anomalous position of igneous carbonate-hosted rare earth mineralization in the target area. Feature decomposition dimensionality reduction, feature enhancement, and weighted fusion processing are applied to the cross-sectional map, followed by segmentation of the igneous carbonate-hosted rare earth mineralization geological body in the fused image to obtain the spatial distribution of the detection target. The integrated analysis of multi-source geophysical data using images improves the accuracy of identification and localization of igneous carbonate-hosted rare earth mineralization.

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