机译:通过改进的残余卷积神经网络深度学习地震随机噪声衰减
Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China|China Univ Petr State Key Lab Petr Resource & Prospecting Beijing 102249 Peoples R China;
Yangtze Univ Key Lab Explorat Technol Oil & Gas Resources Minist Educ Wuhan 430100 Peoples R China|Yangtze Univ Cooperat Innovat Ctr Unconvent Oil & Gas Minist Educ Wuhan 430100 Hubei Peoples R China;
Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Peoples R China;
Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Peoples R China;
Attenuation; Transforms; Training; Task analysis; Noise reduction; Signal to noise ratio; Convolution; Convolutional neural networks (CNNs); deep learning (DL); random noise attenuation; transfer learning;
机译:深度卷积神经网络对地震随机噪声衰减的残差学习
机译:基于地震数据集残留卷积神经网络的随机噪声衰减
机译:频域中卷积神经网络衰减地震膨胀噪声及转移学习
机译:基于复值残差卷积神经网络的f-x域地震随机噪声衰减
机译:植物分割通过深度卷积神经网络与转移学习的豆豆植物的植物分割和表型特性
机译:基于卷积神经网络和Boosting随机森林的基于深度学习的错误严重性分类新技术
机译:具有网络结构优化的深度卷积去噪,随机GPR噪声的高保真衰减
机译:用于细粒度图像分类的卷积神经网络中的传递学习。