基于图像分割的盐丘识别算法研究

Research on Salt Dome Identification Algorithm Based on Image Segmentation

提出一种基于图像分割的盐丘识别方法实现自动化、高精度地识别盐丘。该方法在原始U-Net网络基础上进行迁移学习,加载预训练模型的SENet用作编码器的主干网络,对地震图像中的盐丘特征进行增强,突出图像中的重要特征,抑制不重要的特征。针对盐丘分割任务特点,引入Lovasz-Softmax损失函数进行标准化实验,提升对盐丘边界的分割效果。在TGS盐体识别挑战赛提供数据集上的实验结果表明:该方法最终在测试集上取得了97.5%的准确率和87.26%的交并比,与UNet、USKNet相比交并比分别提升了12.59个百分点和1.6个百分点。

Salt dome identification is of great significance for oil and gas exploration.Many important resources are located near the salt dome,but manual identification is time⁃consuming,labor⁃intensive and subjective.To solve this problem,this paper proposes a salt dome identification method based on image segmentation to realize automatic and high⁃precision identification of salt dome.The method is based on the original U⁃Net network for migration learning.The SENet loaded with the pre training model is used as the backbone network of the encoder.The salt dome features in the seismic image are enhanced,the important features in the image are highlighted,and the unimportant features are suppressed.In addition,according to the characteristics of salt dome segmentation task,Lovasz Softmax loss function is introduced for standardization experiment to improve the segmentation effect of salt dome boundary.The experimental results on the data set provided by the TGS salt dome identification challenge show that the method has finally achieved 97.5%Accuracy and 87.26%IoU on the test set.Compared with UNet and USKNet,the IoU has increased by 12.59 percentage points and 1.6 percentage points,respectively,reflecting the effectiveness and universality of this method.