Fault and horizon automatic interpretation by CNN: a case study of coalfield

Abstract

Abstract 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.