摘要
不同于传统的深度学习反演方法,文中提出一种基于先验约束的深度学习地震波阻抗反演方法:参照地震相类型分割待反演区域,且将区域分割结果作为一种明确的空间约束条件监控网络模型的反演过程;将蕴含丰富低频信息的初始模型作为一种标签以丰富反演结果的低频信息;并使用一种强抗噪性激活函数提高网络模型对噪声数据的适应能力。为降低标签数据的获取难度并保证网络的反演精度,还采取半监督学习方式对网络模型进行训练。将所提方法应用于Marmousi 2模型,测试结果表明反演效果良好且具有较强抗噪性能;随后将该方法成功地应用于M油田实际勘探数据。
We propose a deep learning seismic impedance inversion method based on constraints of prior information.Different from traditional deep learning inversion methods,the inversion area is segmented based on the category of seismic face and segmentation regions are applied as an explicit spatial constraint to constrain the inversion process of the network model.Then the initial model is set as a label to enrich the low-frequency information of the inversion result.Finally,a strong anti-noise activation function is used to improve the adaptability of the network model to noisy data.To reduce the difficulty of acquiring label data and ensure the inversion accuracy of the network,semi-supervised learning is adopted to train the network model.The proposed method is tested on the Marmousi2 model,and the test results indicate that it has a good inversion effect and anti-noise performance.Subsequently,it is successfully applied to the real exploration data of an oilfield.
作者
宋磊
印兴耀
宗兆云
李炳凯
瞿晓阳
郗晓萍
SONG Lei;YIN Xingyao;ZONG Zhaoyun;LI Bingkai;QU Xiaoyang;XI Xiaoping(School of Geosciences,China University of Petroleum(East China),Qingdao,Shandong 266580,China;BGP Geological Research Center,BGP,CNPC,Zhuozhou,Hebei 072751,China)
出处
《石油地球物理勘探》
EI
CSCD
北大核心
2021年第4期716-727,I0008,共13页
Oil Geophysical Prospecting
基金
国家自然科学基金项目“裂缝型储层五维地震解释理论与方法研究”(42030103)资助。
关键词
深度学习
半监督学习
先验约束
抗噪性
波阻抗反演
deep learning
semi-supervised learning
prior constraints
anti-noise
impedance inversion