Multi-attribute recognition method for low-order faults based on LOFUnet deep convolutional neural network
Low-order faults control traps and hydrocarbon enrichment,which are significant for oil and gas exploration and development.However,its identification and description are complicated and inefficient,which seriously restricts such res-ervoirs'exploration and development process.With the development of artificial intelligence,deep learning provides a new way to identify low-order faults.LOFUnet network is an improvement based on UNet,which can obtain more features of low-order fault information in the sample.In this paper,a new fault body is obtained through the fusion of variance attribute,dip attribute,and amplitude attribute,and the LOFUnet network is constructed to identify low-order faults.The network in this paper can obtain more low-order fault features at the encoder end,solve the problem of gradient disappearance,improve the model's convergence speed,enhance the model's stability,and improve the accuracy and efficiency of low-order fault detec-tion.The forward simulation and actual seismic data are used to test the UNet and LOFUnet models,respectively.The results show that the multi-attribute recognition method of low-order faults based on the LOFUnet depth convolution neural net-work can extract more information and improve the accuracy of low-order fault recognition.
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