Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yubo Han, Ye Liu
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引用次数: 0

Abstract

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

通过集成深度学习的自适应 GLFIF 进行先进的岩相薄片分割
在地质研究中,砂岩薄片的精确分割对于详细的地下材料分析至关重要。传统方法往往无法准确捕捉这些样本的复杂性。本研究提出了一种创新的分割方法,该方法将自适应全局和局部模糊图像拟合(GLFIF)算法与大津阈值法相结合,显著提高了分割精度和效率。我们的方法结合了深度学习和传统图像处理技术。由深度学习驱动的自适应 GLFIF 算法可自动调整参数,从而减少人工干预并提高精度。与学习固定参数的传统方法不同,我们的模型会动态调整分割过程,以获得精确的结果。双阶段分割策略能有效隔离小特征并处理复杂的边界,从而确保高质量的结果。实验结果表明,与传统方法相比,我们的方法将分割准确率提高了 11.2%(从 82.6% 提高到 93.8%),将 Jaccard 指数提高了 15.4%(从 76.8% 提高到 92.2%),将 Dice 系数提高了 9%(从 86.9% 提高到 95.9%)。这项技术弥补了传统图像分析与深度学习之间的差距,将精确分割与先进算法的自动化和计算能力相结合。我们的分割算法代表了自动化岩相薄片分析的重大进步。传统的图像处理方法,如阈值化和水平集,在处理小物体和复杂边界方面表现出色,但需要大量人工干预,无法实现完全自动化。最新的深度学习方法,尤其是语义分割法,可实现端到端的自动化,但在处理小目标和复杂边界时却显得力不从心。我们的方法有效地结合了这两种方法的优势,为地质图像分析提供了全面高效的解决方案,确保了高精度和全自动化。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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