基于ResUNet和Dense CRF模型的地震裂缝识别方法

doi:10.3969/j.issn.0255-8297.2021.03.003

Abstract

Abstract: Aiming at the problems of time-consuming, low efficiency, and high subjective influence in artificial interpretation of seismic data, a crack identification method based on ResUNet and dense conditional random field (Dense CRF) model is proposed. First, the method uses the ResUNet model to extract the features of different resolution levels from the cracks in the seismic amplitude data volume to achieve seismic crack identification, then it uses the Dense CRF model to further optimize the recognition results, so as to achieve accurate recognition of seismic cracks. The proposed method is compared with the traditional UNet and ResUNet methods based on the synthetic seismic amplitude data volume and the seismic amplitude volume data of the F3 work area. Experimental results show that the proposed method performs higher accuracy, finer size and better continuity in crack identification.

Key words: 3D seismic data set, crack identification, deep learning, ResUNet neural network model, dense conditional random field (Dense CRF) model

CLC Number:

TP391

DU Chengze, DUAN Youxiang, SUN Qifeng. Seismic Fault Identification Method Based on ResUNet and Dense CRF Model[J]. Journal of Applied Sciences, 2021, 39(3): 367-366.

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Seismic Fault Identification Method Based on ResUNet and Dense CRF Model

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