Rock thin section image data management method, device and equipment
By using machine learning and collaborative identification by two experts, the problem of inaccurate manual annotation in rock thin section image data management has been solved, thereby improving the accuracy and reliability of data management.
CN122416191APending Publication Date: 2026-07-17LANGFANG INTEGRATED NATURAL RESOURCES SURVEY CENTER CHINA GEOLOGICAL SURVEY
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Patent Information
- Application Number
- CN202610553997.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
In the current management of rock thin section image data, inaccurate manual annotation leads to low reliability of identification results, affecting the accuracy of management.
Method used
By identifying key rock features through machine learning and combining them with collaborative identification by two experts, the original identification data is preliminarily and secondarily verified using annotation information with and without annotation labels, thus identifying annotation biases and missing information and managing the data.
Benefits of technology
This improves the accuracy and reliability of rock thin section image data management, ensuring the integrity of annotation information and the objectivity of identification results.
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Smart Images
Figure CN122416191A_ABST
Abstract
The application provides a rock slice image data management method, device and equipment, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring image data and original identification data of a rock slice to be managed, which have first annotation information; identifying key features of the rock through machine learning based on the first annotation information and an identification standard to obtain second annotation information, wherein the second annotation information contains information carrying a first annotation information label; preliminarily checking the original identification data by using the second annotation information carrying the first annotation information label to obtain a checking result and annotation deviation information; performing secondary checking on the original identification data by using the second annotation information not carrying the first annotation information label to obtain annotation missing information; and managing the image data based on the checking result, the annotation deviation information and the annotation missing information in combination with double-expert collaborative identification. The application can improve the accuracy of rock slice image data management.
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