[1]肖 红,张瑶瑶*,张福禄.改进的卷积神经网络及在地层识别中的应用[J].计算机技术与发展,2021,31(09):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 028]
　XIAO Hong,ZHANG Yao-yao*,ZHANG Fu-lu.Improved Convolution Neural Network and Its Application of Stratigraphic Identification[J].,2021,31(09):167-172.[doi:10. 3969 / j. issn. 1673-629X. 2021. 09. 028]
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改进的卷积神经网络及在地层识别中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年09期
页码:
167-172
栏目:
应用前沿与综合
出版日期:
2021-09-10

文章信息/Info

Title:
Improved Convolution Neural Network and Its Application of Stratigraphic Identification
文章编号:
1673-629X(2021)09-0167-06
作者:
肖 红1 ; 张瑶瑶1* ; 张福禄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. 东北石油大学 电气信息工程学院,黑龙江 大庆 163318
Author(s):
XIAO Hong1 ; ZHANG Yao-yao1* ; ZHANG Fu-lu2
1. School of Computer and Information Technology,Northeast Petroleum University,Daqing 163318,China;
2. School of Electrical Engineering and Information,Northeast Petroleum University,Daqing 163318,China
关键词:
卷积神经网络; 地层识别; GhostNet; 双向级联网络; 测井曲线; 扩张卷积
Keywords:
convolution neural network; stratigraphic identification; GhostNet; bidirectional cascade network; logging curve;  dilated convolution
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 09. 028
摘要:
针对地层识别效率低、准确率不高的问题,提出一种基于改进卷积神经网络的地层识别新方法。 该方法基于Ghost 模块及扩张卷积,搭建双向级联 GhostNet,可有效减少网络参数,从而降低计算量;同时,该模型具有多尺度特征提取能力,并采用双向损失函数对学习过程进行监督,从浅层聚焦于图像局部信息到深层提取语义信息,对所有层输出进行融合,可有效提高地层识别的准确度。 首先根据地区特性对测井曲线进行组合优选,对数据进行分层、沃尔什滤波以及线性插值等预处理操作,然后将测井曲线形态映射为二值图像,构造样本数据集,应用改进后的网络即可进行地层识别。 实验结果表明,与同类算法比较,提出算法的准确率约有六个百分点的提高,且参数量显著减少。 从而表明该方法在复杂地层识别方面具有较大潜力。
Abstract:
Aiming at the problem of low efficiency and low accuracy of formation recognition,a new method of strati graphic identification based on improved convolutional neural network is proposed. This method is based on the Ghost module and dilated convolution to build a bi-directional cascade GhostNet,which can effectively reduce network parameters and the amount of calculation. At the same time,the model has multi-scale feature extraction capabilities and uses a two -way loss function to supervise the learning process. The shallow layer focuses on the image local information to the deep layer to extract semantic information,and the output of all layers is fused,which can effectively improve the accuracy of strati graphic identification. Firstly,the log curves are combined and optimized according to the regional characteristics,and the preprocessing operations such as stratification, Walsh filtering and linear interpolation are performed on them. Then the log curve shape is mapped into a binary image,the sample data set is constructed,and the improved network can be used for strati graphic identification. The experiment shows that compared with similar algorithms,the accuracy of the proposed algorithm is improved by about six percentage points,and the amount of parameters is significantly reduced. It shows that the proposed method has great potential in the identification of complex strati graphic.

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更新日期/Last Update: 2021-09-10