Method for determining uranium metallogenic potential area by using multi-source geological data
By constructing a multi-source geoscientific information spatial database and using machine learning algorithms to optimize the prediction model, the problem of predicting deep uranium mineralization potential areas has been solved, improving the efficiency and accuracy of mineral exploration.
CN122155885APending Publication Date: 2026-06-05BEIJING RES INST OF URANIUM GEOLOGY
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BEIJING RES INST OF URANIUM GEOLOGY
- Filing Date
- 2024-12-03
- Publication Date
- 2026-06-05
AI Technical Summary
Technical Problem
In deep geological exploration, existing technologies are insufficient to accurately predict uranium mineralization potential areas, resulting in low mineral exploration efficiency.
Method used
By collecting multi-source geoscientific information, a multi-source geoscientific information spatial database is constructed. Machine learning algorithms are used to build a predictive model for uranium mineralization potential areas, and the model is optimized through training and testing to improve prediction accuracy.
Benefits of technology
It has improved the accuracy of uranium mineralization potential area prediction and mineral exploration efficiency, and achieved accurate prediction of deep concealed mineral deposits.
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Smart Images
Figure CN122155885A_ABST
Abstract
The embodiment of the application relates to the technical field of exploration or detection by using comprehensive means, and particularly relates to a method for determining a uranium mineralization potential area by using multi-source geological data, which comprises the following steps: processing multi-source geological information, and establishing a multi-source geological information spatial database of a to-be-explored area; determining ore-controlling elements of the to-be-explored area according to known uranium mines in the to-be-explored area, and constructing a prediction model of the uranium mineralization potential area according to the ore-controlling elements; constructing a sample set according to the distribution of the known uranium mines in the to-be-explored area and the multi-source geological information spatial database; training and testing the prediction model by using the sample set, and determining the prediction model; and predicting the to-be-explored area by using the prediction model, and determining the distribution of the uranium mineralization potential area of the to-be-explored area. The method can effectively improve the prediction accuracy of the uranium mineralization potential area and improve the ore-prospecting efficiency by comprehensively processing and integrating the multi-source geological information of the to-be-explored area, and constructing a prediction model of the uranium mineralization potential area that can meet the predetermined needs.
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