Next Article in Journal
New Insights into Chromogenic Mechanism and the Genesis of Blue Jadeite from Guatemala
Previous Article in Journal
Investigation of the Coloration Mechanisms of Yellow-Green Nephrite from Ruoqiang (Xinjiang), China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Minerals
Volume 15
Issue 9
10.3390/min15090962
minerals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology

by
Lanfang Dong
1,2,3,4,*,
Chenxu Sun
3,
Xiaolu Yu
2,
Xinming Zhang
3,
Menglian Chen
4 and
Mingyang Xu
5
1
State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Petroleum Exploration and Production Research Institute, China Petrochemical Corporation, Beijing 102206, China
2
Sinopec Key Laboratory of Petroleum Accumulation Mechanisms, Petroleum Exploration and Production Research Institute, China Petrochemical Corporation, Wuxi 214126, China
3
Institute of Advanced Technology, University of Science and Technology of China, Hefei 230031, China
4
School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
5
Anhui Rank Artificial Intelligent Technology Co., Ltd., Hefei 230088, China
*
Author to whom correspondence should be addressed.
Minerals 2025, 15(9), 962; https://doi.org/10.3390/min15090962
Submission received: 5 August 2025 / Revised: 1 September 2025 / Accepted: 9 September 2025 / Published: 11 September 2025
(This article belongs to the Section Mineral Exploration Methods and Applications)
Versions Notes

Abstract

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.
Keywords: computer vision; automated petrology; tight sandstone; mineral identification; structural quantification; hybrid deep learning; reservoir characterization computer vision; automated petrology; tight sandstone; mineral identification; structural quantification; hybrid deep learning; reservoir characterization

Share and Cite

MDPI and ACS Style

Dong, L.; Sun, C.; Yu, X.; Zhang, X.; Chen, M.; Xu, M. Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology. Minerals 2025, 15, 962. https://doi.org/10.3390/min15090962

AMA Style

Dong L, Sun C, Yu X, Zhang X, Chen M, Xu M. Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology. Minerals. 2025; 15(9):962. https://doi.org/10.3390/min15090962

Chicago/Turabian Style

Dong, Lanfang, Chenxu Sun, Xiaolu Yu, Xinming Zhang, Menglian Chen, and Mingyang Xu. 2025. "Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology" Minerals 15, no. 9: 962. https://doi.org/10.3390/min15090962

APA Style

Dong, L., Sun, C., Yu, X., Zhang, X., Chen, M., & Xu, M. (2025). Hybrid Architecture for Tight Sandstone: Automated Mineral Identification and Quantitative Petrology. Minerals, 15(9), 962. https://doi.org/10.3390/min15090962

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop