Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology. |
| Συγγραφείς: |
Dong, Lanfang, Sun, Chenxu, Yu, Xiaolu, Zhang, Xinming, Chen, Menglian, Xu, Mingyang |
| Πηγή: |
Minerals (2075-163X); Sep2025, Vol. 15 Issue 9, p962, 26p |
| Θεματικοί όροι: |
Sandstone, Deep learning, Petrophysics, Computer vision, Mineral analysis, Petrology |
| Περίληψη: |
This study proposes an integrated computer vision system for automated petrological analysis of tight sandstone micro-structures. The system combines Zero-Shot Segmentation SAM (Segment Anything Model), Mask R-CNN (Region-Based Convolutional Neural Networks) instance segmentation, and an improved MetaFormer architecture with Cascaded Group Attention (CGA) attention mechanism, together with a parameter analysis module to form a hybrid deep learning system. This enables end-to-end mineral identification and multi-scale structural quantification of granulometric properties, grain contact relationships, and pore networks. The system is validated on proprietary tight sandstone datasets, SMISD (Sandstone Microscopic Image Segmentation Dataset)/SMIRD (Sandstone Microscopic Image Recognition Dataset). It achieves 92.1% mIoU segmentation accuracy and 90.7% mineral recognition accuracy while reducing processing time from more than 30 min to less than 2 min per sample. The system provides standardized reservoir characterization through automated generation of quantitative reports (Excel), analytical images (JPG), and structured data (JSON), demonstrating production-ready efficiency for tight sandstone evaluation. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |