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NTIS 바로가기IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society, v.19, 2022년, pp.1 - 5
Hu, Guang (China University of Geosciences, Hubei Subsurface Multi-Scale Imaging Key Laboratory, Wuhan, China) , Hu, Zhengwang (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China) , Liu, Jiangping (Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, China) , Cheng, Fei (China University of Geosciences, College of Marine Science and Technology, Wuhan, China) , Peng, Daicheng (China University of Geosciences, Hubei Subsurface Multi-Scale Imaging Key Laboratory, Wuhan, China)
Seismic fault detection is indispensable for exploring reservoirs of hydrocarbons, and a considerable amount of research has thus been devoted to it. With the rapid development of deep learning in recent years, researchers have begun using convolutional neural networks (CNNs) to identify seismic fau...
Gersztenkorn, Adam, Sharp, John, Marfurt, Kurt. Delineation of tectonic features offshore Trinidad using 3-D seismic coherence. The leading edge, vol.18, no.9, 1000-1008.
Sigismondi, Mario E., Soldo, Juan C.. Curvature attributes and seismic interpretation: Case studies from Argentina basins. The leading edge, vol.22, no.11, 1122-1126.
Yan, Z., Gu, H., Cai, C.. Automatic fault tracking based on ant colony algorithms. Computers & geosciences, vol.51, 269-281.
Di, Haibin, AlRegib, Ghassan. Semi‐automatic fault/fracture interpretation based on seismic geometry analysis. Geophysical prospecting, vol.67, no.5, 1379-1391.
Huang, Lei, Dong, Xishuang, Clee, T. Edward. A scalable deep learning platform for identifying geologic features from seismic attributes. The leading edge, vol.36, no.3, 249-256.
Pochet, Axelle, Diniz, Pedro H. B., Lopes, Hélio, Gattass, Marcelo. Seismic Fault Detection Using Convolutional Neural Networks Trained on Synthetic Poststacked Amplitude Maps. IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society, vol.16, no.3, 352-356.
Wu, Xinming, Liang, Luming, Shi, Yunzhi, Fomel, Sergey. FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation. Geophysics, vol.84, no.3, IM35-IM45.
Chen, Liang-Chieh, Papandreou, George, Kokkinos, Iasonas, Murphy, Kevin, Yuille, Alan L.. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE transactions on pattern analysis and machine intelligence, vol.40, no.4, 834-848.
Badrinarayanan, Vijay, Kendall, Alex, Cipolla, Roberto. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE transactions on pattern analysis and machine intelligence, vol.39, no.12, 2481-2495.
arXiv:1409.1556 Very deep convolutional networks for large-scale image recognition Simonyan 2014
4th ICLR, San Juan, Puerto Rico Multi-scale context aggregation by dilated convolutions Yu
Guang-Bin Huang, Zuo Bai, Kasun, Liyanaarachchi Lekamalage Chamara, Chi Man Vong. Local Receptive Fields Based Extreme Learning Machine. IEEE computational intelligence magazine, vol.10, no.2, 18-29.
arXiv:1706.05587 Rethinking atrous convolution for semantic image segmentation Chen 2017
arXiv:1711.05225 CheXNet: Radiologist-level pneumonia detection on chest X-Rays with deep learning Rajpurkar 2017
Vasudevan, K., Eaton, D., Cook, F. A.. Adaptation of seismic skeletonization for other geoscience applications. Geophysical journal international, vol.162, no.3, 975-993.
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