基于U-Net的盐体识别方法

卷积神经网络在计算机视觉领域取得重大突破,利用其强大的图像处理能力,将地下沉积盐体的识别问题转化为图像语义分割问题,应用深度卷积神经网络实现盐体地震图像的像素级语义分割.本文在U-Net基础上,增加网络深度并同时引入批归一化和Dropout处理,使得神经网络模型具有更高的可信度和更强的泛化能力.通过实验发现,在卷积层之后引入批归一化处理,并在池化层和叠加层之后引入Dropout可以稳定提升模型对盐体图像的分割性能.

Salt-body Classification Method Based on U-Net

Convolutional neural network has made great breakthroughs in the field of computer vision. With its powerful image processing ability,we transform classification of underground sedimentary salt-body into image semantics segmentation problem. Deep convolution neural network is applied to implement pixel-level semantics segmentation of salt seismic images. We increase depth of the network and adds batch normalization and Dropout processing based on U-Net, which makes the neural network model with higher reliability and stronger generalization ability. With experiments, it was found that adding batch normalization layer after convolution layer, and adding Dropout after the pooling layer and concatenate layer improve steadily segmentation performance of the model for salt-body seismic image.