Bauxite resource prediction analysis method and system
By performing distortion correction and image mask on the remote sensing images of bauxite resources, combining geological exploration data to generate a three-dimensional distribution model, and constructing a mineralized knowledge map, the problem of insufficient complementarity of remote sensing images and geological data is solved, and more accurate bauxite resource prediction and grade prediction are achieved.
Patent Information
- Application Number
- CN202510175183.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-18
- Publication Date
- 2025-03-21
- Estimated Expiration
- 2045-02-18
AI Technical Summary
In the prior art, the resolution and spectral characteristics of remote sensing images are easily affected by factors such as weather and vegetation coverage, resulting in a decrease in the accuracy of bauxite resource identification, and insufficient geological exploration data samples and uneven spatial distribution, making it difficult to fully reflect the actual distribution characteristics of ore bodies.
By collecting surface remote sensing images of the target bauxite, digital elevation data distortion correction and image mask are performed, surface resource distribution characteristics are extracted; a three-dimensional distribution model of ore body is generated based on geological exploration data, and a mineralization knowledge map is constructed, and the ore body grade is finally predicted through deep learning models.
It improves the quality of remote sensing images and data complementarity, enhances the reliability and accuracy of bauxite resource prediction, can more accurately identify the location and scale of bauxite resources, and improves the accuracy of ore grade prediction.