Abstract：The application of machine learning algorithms in the field of geophysics has been expanded and deepened.In fault recognition on seismic data,the main approach is training a shallow convolutional neural network to achieve fault recognition using actual or synthetical fault samples.Actual fault samples require manual marking,which is very time-consuming.Synthetic fault samples are easy to obtain,but the effect of the trained network model is inadequate when applied to actual seismic data.For this reason,this paper combines a deep residual network with transfer learning to fault recognition.First train synthetical fault samples by constructing a deep residual network with better performance,then use a small number of actual fault samples for transfer learning.This way the generalization ability of the network can be enhanced, and the recognition results can be optimized.After transfer learning,the network can more effectively improve the recognition accuracy of actual faults than ever before.Actual seismic data have proved the feasibility and effectiveness of the method.
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