A SPD-UNet Model for Seismic Fault Image Identification

doi:10.19678/j.issn.1000-3428.0059327

Abstract

Abstract: Fault is the main factor that controls the formation and distribution of oil and gas fields, so the detection and identification of fault plays an important role in the exploration oil and gas fields.Based on the Attention-UNet model, this paper proposes an improved SPD-UNet model for fault identification in earthquake images.SPD-UNet introduces dilated convolution, which can effectively enhance image feature extraction while expanding the receptive field and preventing resolution loss.At the same time, the dilated convolutions in the pyramid structure are stacked to form the SPD module, which avoids the local information loss of dialted convolutions, and improves the correlation between fault information and image identification accuracy.Experimental results show that SPD-UNet exhibits a higher identification accuracy than SegNet and ResUNet.The fault position and shape identified by SPD-UNet are closer to actual information.

Key words: seismic fault identification, image segmentation, neural network, UNet model, dilated convolution, pyramid structure

摘要： 断层是控制油气田形成和分布的主要因素，断层检测和识别对于油气勘探具有重要作用。基于Attention-UNet神经网络模型，构建一种面向地震断层图像识别的SPD-UNet模型。引入空洞卷积，在保证卷积核感受野大小且不损失原始图像分辨率的情况下，增强SPD-UNet模型的断层图像特征提取能力。将金字塔结构的空洞卷积组合成SPD模块，解决空洞卷积的局部信息丢失问题，提高断层信息关联性及图像识别精度。实验结果表明，SPD-UNet模型对于地震断层图像的识别精度优于SegNet与ResUNet模型，并且识别结果与实际标注的地震断层形状及位置更接近。

关键词: 地震断层识别, 图像分割, 神经网络, UNet模型, 空洞卷积, 金字塔结构

CLC Number:

TP18

XI Yingjie, LI Kewen, XU Yanhui, ZHU Jianbing. A SPD-UNet Model for Seismic Fault Image Identification[J]. Computer Engineering, 2021, 47(12): 249-255.

席英杰, 李克文, 徐延辉, 朱剑兵. 一种用于地震断层图像识别的SPD-UNet模型[J]. 计算机工程, 2021, 47(12): 249-255.

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