基于ResUNet和Dense CRF模型的地震裂缝识别方法

doi:10.3969/j.issn.0255-8297.2021.03.003

应用科学学报 ›› 2021, Vol. 39 ›› Issue (3): 367-366.doi: 10.3969/j.issn.0255-8297.2021.03.003

• CCF NCCA 2020专栏 • 上一篇

基于ResUNet和Dense CRF模型的地震裂缝识别方法

杜承泽, 段友祥, 孙歧峰

中国石油大学(华东) 计算机科学与技术学院, 山东青岛 266580

收稿日期:2020-08-30 发布日期:2021-06-08
通信作者: 杜承泽，研究方向为人工智能、地震裂缝识别等。E-mail:s18070012@s.upc.edu.cn E-mail:s18070012@s.upc.edu.cn
基金资助:
中石油重大科技项目（No.ZD2019-183-006）；中央高校基本科研业务费专项基金（No.20CX05017A）资助

Seismic Fault Identification Method Based on ResUNet and Dense CRF Model

DU Chengze, DUAN Youxiang, SUN Qifeng

College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, Shandong, China

Received:2020-08-30 Published:2021-06-08

摘要/Abstract

摘要： 针对人工解释地震资料耗时长、效率低、受主观因素影响较大的不足，提出了一种基于ResUNet和全连接条件随机场（dense conditional random field，Dense CRF）模型的裂缝识别方法。该方法首先使用ResUNet模型提取地震振幅数据体中裂缝的不同分辨率的特征，实现地震裂缝识别；然后利用Dense CRF模型进一步优化识别结果，从而实现地震裂缝的精准识别。将该方法与传统UNet、ResUNet模型在合成地震振幅数据体和F3工区地震数据体进行了实验比较，结果表明运用所提方法识别的裂缝更准确、裂缝尺寸更细、连续性更好。

关键词: 三维地震数据集, 裂缝识别, 深度学习, ResUNet神经网络模型, Dense CRF模型

Abstract: Aiming at the problems of time-consuming, low efficiency, and high subjective influence in artificial interpretation of seismic data, a crack identification method based on ResUNet and dense conditional random field (Dense CRF) model is proposed. First, the method uses the ResUNet model to extract the features of different resolution levels from the cracks in the seismic amplitude data volume to achieve seismic crack identification, then it uses the Dense CRF model to further optimize the recognition results, so as to achieve accurate recognition of seismic cracks. The proposed method is compared with the traditional UNet and ResUNet methods based on the synthetic seismic amplitude data volume and the seismic amplitude volume data of the F3 work area. Experimental results show that the proposed method performs higher accuracy, finer size and better continuity in crack identification.

Key words: 3D seismic data set, crack identification, deep learning, ResUNet neural network model, dense conditional random field (Dense CRF) model

中图分类号:

TP391

杜承泽, 段友祥, 孙歧峰. 基于ResUNet和Dense CRF模型的地震裂缝识别方法[J]. 应用科学学报, 2021, 39(3): 367-366.

DU Chengze, DUAN Youxiang, SUN Qifeng. Seismic Fault Identification Method Based on ResUNet and Dense CRF Model[J]. Journal of Applied Sciences, 2021, 39(3): 367-366.

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基于ResUNet和Dense CRF模型的地震裂缝识别方法

Seismic Fault Identification Method Based on ResUNet and Dense CRF Model

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