Aiming at the disadvantages of time-consuming manual annotation of seismic facies and the susceptibility of traditional semi-supervised methods to interference from unreliable pseudo-labels,a semi-supervised seismic facies identification method based on reliability estimation was proposed.A reliability estimation network was utilized to filter unreliable regions in pseudo-labels of seismic facies to avoid cognitive bias caused by erroneous supervision signals and extend multi-type auxiliary decoders based on the mean teacher model for consistency regularization to improve generalization and robustness of the model.Experi-mental results on the Netherlands F3 dataset demonstrate that using a few labeled samples,MIOU can reach 90.33％,effectively improves the identification performance of seismic facies that are easily confused by classification.
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