A semi-supervised horizon tracking method with convolutional neural network
Horizon tracking is a key step in seismic data interpretation.It is typically performed manually by in-terpreters in a human-computer interaction manner,which results in low efficiency.Convolutional neural net-work(CNN)can establish a nonlinear mapping relationship between seismic data and training labels to achieve horizon tracking.However,since it is difficult to obtain manually interpreted results,models trained merely with a few labels tend to have relatively poor generalization capability.Therefore,a semi supervised horizon tracking method based on a convolutional neural network is proposed to transform horizon tracking into image segmentation between horizons and faults.First,the unlabeled data is trained by the autoencoder.Then a small amount of labeled data is used for supervised learning after part of the parameters are transferred to the super-vised learning network.Finally,the seismic data of the whole working area is predicted,and the edge of the segmentation result is extracted as the horizon tracking result.The test results of both synthetic data and the real data show that compared with the supervised learning horizon tracking method,the proposed method pre-sents less error segmentation and smaller errors between the horizon extracted from the segmentation edge and the artificial horizon interpretation results,and thus has better generalization capability.
horizon trackingseismic data interpretationconvolutional neural networksemi-supervised learningimage segmentation
万方数据
维普
