基于全局信息提取的三维卷积神经网络断层智能识别

Fault recognition using 3D convolutional neural network with global information extraction

断层的准确识别对油气勘探开发至关重要,基于相干体属性的传统断层识别技术面对复杂构造带效果不佳,基于图像分割的常规卷积神经网络也难以弥补下采样中丢失的特征信息.因此,搭建全局信息提取注意力机制,不仅可以在U-Net全卷积网络结构的拼接部分引入信息提取,弥补下采样过程中信息的缺失、增强网络的学习能力,还可以通过在网络最底层采用信息缩放,增强最底层特征信息、提高解释准确率.而且该注意力模块没有添加额外的参数信息,对内存需求较小.实验结果表明,加入注意力机制的神经网络模型的测试准确率达到96%,损失函数值收敛到7%,对实际地震数据的主干断层刻画优于常规U-Net网络.全局信息提取的注意力机制为基于卷积神经网络的三维断层智能识别提供了新的思路.

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.