[1]杜睿山,宋健辉,孟令东.基于注意力机制的岩石铸体薄片轻量化分割[J].计算机技术与发展,2023,33(10):128-134.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 020]
　DU Rui-shan,SONG Jian-hui,MENG Ling-dong.Lightweight Segmentation of Rock Casting Sheet Based on Attention Mechanism[J].,2023,33(10):128-134.[doi:10. 3969 / j. issn. 1673-629X. 2023. 10. 020]
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基于注意力机制的岩石铸体薄片轻量化分割()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年10期
页码:
128-134
栏目:
人工智能
出版日期:
2023-10-10

文章信息/Info

Title:
Lightweight Segmentation of Rock Casting Sheet Based on Attention Mechanism
文章编号:
1673-629X(2023)10-0128-07
作者:
杜睿山1; 2 ; 宋健辉1 ; 孟令东2
1. 东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318;
2. 油气藏及地下储库完整性评价黑龙江省重点实验室,黑龙江 大庆 163318
Author(s):
DU Rui-shan1; 2 ; SONG Jian-hui1 ; MENG Ling-dong2
1. School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China;
2. Key Laboratory of Oil and Gas Reservoir and Underground Gas Storage Integrity Evaluation,Daqing 163318,China
关键词:
深度学习; 语义分割; 岩石铸体薄片; 轻量化网络; 注意力机制
Keywords:
deep learning; semantic segmentation; rock casting sheet; lightweight network; attention mechanism
分类号:
TP391. 1
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 10. 020
摘要:
储集层岩石铸体薄片的微观结构特征对研究储层的储渗能力、流体分布、采收率的大小、水驱油效率等均具重要作用。 岩石铸体薄片的分割是研究岩石微观结构特征的前提,目前传统方法是通过染色剂对孔隙区域染色,然后用阈值或连通域等方法进行分割,这种方法准确率低且成本昂贵。 基于深度学习的语义分割网络在不同的分割场景下都取得了很大进展。 该文采用 DeepLabV3+网络作为模型框架,首先,针对语义分割网络参数量多且在恢复空间细节方面表现欠佳等问题,引入了轻量化特征提取网络,优化原模型 Xception 特征提取网络的参数量;其次,优化残差结构,减少参数计算量,降低模型训练耗时;最后,为了弥补参数优化带来的精度损失,在模型的高层特征图提取部分引入注意力机制 CBAM模块,以提高模型准确率。 在岩石铸体薄片数据集上,此方法与原模型相比准确率提高了 3. 7 百分点,识别帧率提高了106 百分点。
Abstract:
The microstructure characteristics of rock casting sheet of reservoir rocks play an important role in studying the reservoir permeability,fluid distribution, oil recovery?
and water displacement efficiency. The segmentation of rock casting sheet is the premise ofstudying the characteristics of rock microstructure. At present, the traditional method is to dye the pore area with dye, and then usethreshold or connected domain methods to segment. This method has low accuracy and high cost. The semantic segmentation networkbased on deep learning has made great progress in different segmentation scenarios. We use DeepLabV3 + network as the modelframework. Firstly,aiming at the problems of large number of parameters in the semantic segmentation network and poor performance inrestoring spatial details,a lightweight feature extraction network is introduced to optimize the parameters of the original model’ s Xceptionfeature extraction network. Secondly,the residual structure is optimized to reduce the amount of parameter calculation and model trainingtime. Finally,in order to compensate for the accuracy loss caused by parameter optimization,the attention mechanism CBAM module isintroduced in the high-level feature map extraction part of the model to improve the accuracy of the model. Compared with the originalmodel,the accuracy of the proposed method is improved by 3. 7 percentage points and the recognition frame rate is improved by 106 percentage points on the thin section data set of rock casting sheets.

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更新日期/Last Update: 2023-10-10