改进PSPNet的电成像测井裂缝自动识别
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长江大学计算机科学学院

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TP391.41;P631

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国家自然科学基金(41674136)


Improved Automatic Crack Identification for Electrical Imaging Logging Using PSPNet
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School of Computer Science,Yangtze University

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    摘要:

    针对裂缝特征提取困难导致裂缝分割精度低、网络参数量计算量大的问题,本文提出一种改进的PSPNet网络用于自动识别电成像测井图像中的裂缝。首先将PSPNet中的骨干网络替换为优化的MobileNetV3网络,这样可以显著减少网络参数量和计算量;其次,引入了渐进特征金字塔(Asymptotic Feature Pyramid Network,AFPN),用于增加多尺度信息的交互,增强对细小裂缝的识别能力;接着,引入多深度头转置注意力(Multi-Depthwise Conv head Transposed Attention,MDTA)进行全局特征的提取,提升关键信息的提取能力;最后,采用Focal Loss和Dice Loss组合相加作为损失函数,以解决数据集类别占比不平衡的问题。实验结果表明,改进的PSPNet网络对电成像测井裂缝具有较好的分割效果。与PSPNet网络相比,mIoU提升了3.17%,mPA提升了6.38%。此外,本文算法的参数量、计算量、权重分别比原模型减少94.3%、95.7%和93.8%。同时,开发了基于CIFLog的裂缝识别系统,该系统能够满足对电成像测井的实际需要。

    Abstract:

    In this paper, an improved PSPNet network is proposed to automatically identify fractures in electrical imaging logging images, which is difficult to extract fracture features and leads to low segmentation accuracy and large calculation of network parameters. Firstly, the backbone network in PSPNet is replaced with the optimized MobileNetV3 network, which can significantly reduce the number of network parameters and the amount of computation. Secondly, the Asymptotic Feature Pyramid Network (AFPN) is introduced to increase the interaction of multi-scale information and enhance the recognition ability of small cracks. Then, Multi-Depthwise Conv head Transposed Attention (MDTA) was introduced to extract global features and improve the extraction ability of key information. Finally, the combination of Focal Loss and Dice Loss was used as a loss function to solve the problem of unbalanced proportion of data sets. The experimental results show that the improved PSPNet network has a good segmentation effect on the fracture in the electrical imaging logging. Compared with the PSPNet network, mIoU improved by 3.17% and mPA improved by 6.38%. In addition, the number of parameters, calculation amount and weight of the proposed algorithm are reduced by 94.3%, 95.7% and 93.8% respectively compared with the original model. At the same time, the crack identification system based on CIFLog is developed, which can meet the practical needs of the electrical imaging logging.

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申科,肖小玲. 改进PSPNet的电成像测井裂缝自动识别[J]. 科学技术与工程, , ():

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  • 收稿日期:2023-12-13
  • 最后修改日期:2024-06-18
  • 录用日期:2024-07-09
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