In order to obtain realistic seismic fault training samples,a seismic fault training sample synthesis method based on cy-cle-consistent adversarial networks is proposed.This method takes randomly generated fault labels and real fault data as inputs,and employs an unsupervised adversarial network to learn the relationship between fault labels and fault data and generate seismic fault samples that match the characteristics of the fault labels,thereby obtaining a labeled fault training sample set.This new method al-leviates the problem of training dataset shortage for deep learning in seismic fault interpretation.A quantitative analysis of the mean frequency and textural difference between the synthetic and real faults was performed,showing a high similarity between the two.The neural network trained with the fault samples generated by this method was applied to real data for testing and compari-son.The results show that these samples are realistic and reliable.Furthermore,this method can generate targeted faults for differ-ent work areas,and be flexibly and effectively applied in intelligent seismic fault detection in work areas.
维普
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