Fault recognition using 3D convolutional neural network with global information extraction
Accurate fault identification is crucial to oil and gas exploration and development.Traditional fault identification technology based on coherence volume attribute has poor effects in complex structural zones.Conventional convolutional neural network based on image segmentation is also difficult to make up for the feature information lost in down sampling.Therefore,building a global information extraction attention mechanism can not only introduce information extraction in the concatenation part of the U-Net full convolutional network structure,compensates for the lack of information in the downsampling process and enhances the network's learning ability.It can also enhance the bottom level feature information and improve interpretation accuracy by using information scaling at the bottom level of the network.Moreover,this attention block does not add additional parameter information and has a low memory requirement.The experimental results show that the test accuracy of the neural network model with attention mechanism reaches 96％,and the loss function converges to 7％.The description of the main fault of the actual seismic data is better than the conventional U-Net.The attention mechanism of global information extraction provides a new idea for 3D fault intelligent recognition based on convolutional neural network.
Fault identificationU-Net Convolutional neural networkAttention mechanismFeature fusionGlobal information extraction
万方数据
维普
