基于CNN-SVM和集成学习的固井质量评价方法OA北大核心CSTPCD
Cementing Quality Evaluation Method Based on CNN-SVM and Integrated Learning
为解决固井质量评价问题,提出一种基于CNN-SVM和集成学习的固井质量评价方法.首先,针对DenseNet模型采取缩减网络层数、增加多尺度卷积层、嵌入卷积注意力模块等改进措施,以提高模型的训练速度和评价准确率;其次,利用InceptionV 1模块和扩张卷积构建一个模型复杂度相对较小且评价准确率相对较高的Inception-DCNN模型;再次,优选3个经典的卷积神经网络模型(ResNet50,MobileNetV3-Small,GhostNet)…查看全部>>
In order to solve the problem of cementing quality evaluation,we proposed a cementing quality evaluation method based on CNN-SVM and integrated learning.Firstly,the method adopted improvement measures such as reducing the number of network layers,adding multi-scale convolutional layers,and embedding convolutional attention modules for the DenseNet model to improve the training speed and evaluation accuracy of the model.Secondly,the InceptionV1 module and dil…查看全部>>
肖红;钱祎鸣
东北石油大学计算机与信息技术学院,黑龙江大庆 163318东北石油大学计算机与信息技术学院,黑龙江大庆 163318
计算机与自动化
固井质量评价扇区水泥胶结测井集成学习卷积神经网络支持向量机
cementing quality evaluationsector cement cement loggingintegrated learningconvolutional neural networksupport vector machine
《吉林大学学报（理学版）》 2024 (4)
960-970,11
黑龙江省自然科学基金(批准号:LH2019F004).
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