一种基于三维地震的煤层顶板岩性破碎的识别方法
By combining intrinsic structural coherence properties and multi-attribute analysis models with machine learning methods, the problems of low identification efficiency and high cost caused by false anomalies in 3D seismic technology have been solved, enabling accurate identification and efficient construction of coal seam roof lithological fracture.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- RES INST OF COAL GEOPHYSICAL EXPLORATION
- Filing Date
- 2025-12-17
- Publication Date
- 2026-07-17
AI Technical Summary
Existing 3D seismic technology suffers from false anomalies when identifying coal seam roof lithological fracture, resulting in low identification efficiency and high labor costs. Furthermore, its low resolution makes it difficult to accurately identify the fracture condition of the coal seam roof.
By employing intrinsic structural coherence properties, eigenvalue decomposition and covariance matrix analysis, combined with multi-attribute analysis models and machine learning methods, we can identify lithological fracturing of coal seam roof, thereby improving identification accuracy and reducing false anomalies.
It improved the accuracy of identifying coal seam roof lithological fracture, reduced misjudgments and ineffective construction caused by false anomalies, lowered labor costs, and ensured the safe and efficient operation of coal mine production.
Smart Images
Figure CN121454614B_ABST