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Fault and horizon automatic interpretation by CNN: a case study of coalfield
Journal of Geophysics and Engineering ( IF 1.4 ) Pub Date : 2020-12-04 , DOI: 10.1093/jge/gxaa060
Yinling Guo 1, 2 , Suping Peng 1 , Wenfeng Du 1 , Dong Li 1, 2
Affiliation  

A convolutional neural network (CNN) is a powerful tool used for seismic interpretation. It does not require manual intervention and can automatically detect geological structures using the pattern features of the original seismic data. In this study, we presented the development history of seismic interpretation and the application of CNN in seismic exploration. We proposed a set of CNN prediction methods and processes for coalfield seismic interpretation and realised automatic interpretation of faults and horizons based on the relationship between faults and horizons. We defined a CNN model training method based on structural geological modelling, which allowed rapid and accurate establishment of fault and horizon labels by using structural modelling. We used two examples to verify the accuracy of the algorithm, one to test for synthetic 3D seismic data and one to test for real coalfield seismic data. The results showed that CNNs can effectively predict both faults and horizons at the same time and has high accuracy. Thus, CNNs are potentially novel interpretation tools for coalfield seismic interpretation.

中文翻译:

CNN的断层和水平自动解释:以煤田为例

卷积神经网络(CNN)是用于地震解释的强大工具。它不需要人工干预,并且可以使用原始地震数据的图案特征自动检测地质结构。在这项研究中,我们介绍了地震解释的发展历史以及CNN在地震勘探中的应用。我们提出了一套用于煤田地震解释的CNN预测方法和过程,并根据断层与地层之间的关系实现了断层和地层的自动解释。我们定义了基于结构地质建模的CNN模型训练方法,该方法可通过使用结构建模快速而准确地建立断层和层位标签。我们使用了两个示例来验证算法的准确性,一种用于测试合成3D地震数据,另一种用于测试真实煤田地震数据。结果表明,CNN可以同时有效地预测断层和水平,并且具有很高的准确性。因此,CNN是用于煤田地震解释的潜在新颖解释工具。
更新日期:2020-12-30
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