[1] |
Cao L L, Li H T, Han Y S , et al. 2016. Application of convolutional neural networks in classification of high resolution remote sensing imagery[J]. Science of Surveying & Mapping (in Chinese), 41(9):170-175.
|
[2] |
Cao Y S, Chen J H, Wang X Q , et al. 2015. Automatic rejection algorithm of abnormal seismic traces via singular value decomposition[J]. Progress in Geophysics (in Chinese), 30(5):2120-2124.
|
[3] |
Liu C Z, Wang C L, Wang X . 2009. Automatic recognition and evaluation method of abnormal seismic trace among seismic data objects[J]. Petroleum Geology and Recovery Efficiency (in Chinese), 16(4):56-57, 67.
|
[4] |
Hilterman F J . 1975. Amplitudes of seismic waves—A quick look[J]. Geophysics, 40(5):745-762.
doi: 10.1190/1.1440565
|
[5] |
Hinton G E, Salakhutdinov R R . 2006. Reducing the dimensionality of data with neural networks[J]. science, 313(5786):504-507.
doi: 10.1126/science.1127647
pmid: 16873662
|
[6] |
LeCun Y, Bengio Y, Hinton G . 2015. Deep learning[J]. Nature, 521(7553):436-444.
doi: 10.1038/nature14539
|
[7] |
LeCun Y, Bottou L, Bengio Y , et al. 1998. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86(11):2278-2324.
doi: 10.1109/5.726791
|
[8] |
Li S F, Gao F Q , 2017. Handwritten numeral recognition based on convolution neural network[J]. Journal of Zhejiang Institute Sci-Tech University (in Chinese), 37(3):438-443.
|
[9] |
McCormack M D . 1990. Seismic trace editing and first break picking using neural networks[M]. Society of Exploration Geophysicists, 321-324.
|
[10] |
Nielsen M A. 2015. Neural networks and deep learning [J]. URL: .
|
[11] |
Su S L, Cai X L, Zeng Q Q , et al. 2009. Automatic rejection and quality control of abnormal seismic traces under multi-constraints[J]. Progress in Exploration Geophysics (in Chinese), 32(5):334-341.
|
[12] |
Rumelhar D E, Hinton G E, Williams D . 1986. Learning representations by back-propagating errors[J]. Nature, 323(6088):533-538.
|
[13] |
Yi X Y, Zhou J Y , Summary of seismic attribute optimization methods[J]. Oil Geophysical Prospecting (in Chinese), 2005,40(4):482-489.
|
[14] |
Zhang F C, Liu H Q, Niu X M , et al. 2014. High resolution seismic inversion by convolutional neural network[J]. Oil Geophysical Prospecting (in Chinese), 49(6):1165-1169.
|
[15] |
Zhang X G, Li Y D . 1992. Automatic editing of noisy seismic data using an artificial neural network approach[J]. Chinese Journal of Geophysics (in Chinese), 35(5):637-643.
|
[16] |
Zhu G S, Liu R L, Wang T G . 1994. Application of neural network to reservoir lateral prediction and trace editing[J]. Geophysical Prospecting for Petrol (in Chinese), ( 1):1-9.
|
[17] |
曹林林, 李海涛, 韩颜顺 , 等. 2016. 卷积神经网络在高分遥感影像分类中的应用[J]. 测绘科学, 41(9):170-175.
doi: 10.16251/j.cnki.1009-2307.2016.09.033
|
[18] |
曹永生, 陈金焕, 王小青 , 等. 2015. 基于奇异值分解的废道自动识别算法[J]. 地球物理学进展, 30(5):2120-2124.
|
[19] |
刘成斋, 王成礼, 王鑫 , 等. 2009. 地震数据体中非正常地震道的自动识别与评价方法[J]. 油气地质与采收率, 16(4):56-57, 67.
doi: 10.3969/j.issn.1009-9603.2009.04.017
|
[20] |
苏世龙, 蔡希玲, 曾庆芹 , 等. 2009. 多重约束下异常地震道自动剔除与质量监控[J]. 勘探地球物理进展, 32(5):334-341.
|
[21] |
印兴耀, 周静毅 . 地震属性优化方法综述[J]. 石油地球物理勘探, 2005,40(4):482-489.
doi: 10.3321/j.issn:1000-7210.2005.04.027
|
[22] |
张繁昌, 刘汉卿, 钮学民 , 等. 2014. 褶积神经网络高分辨率地震反演[J]. 石油地球物理勘探, 49(6):1165-1169.
|
[23] |
张学工, 李衍达 . 1992. 用人工神经网络实现地震记录中的废道自动切除[J]. 地球物理学报, 35(5):637-643.
|
[24] |
朱广生, 刘瑞林, 王庭阁 . 1994. 神经网络在油气层横向预测和地震道编辑中的应用[J]. 石油物探, ( 1):1-9.
|