石油地球物理勘探2024，Vol.59Issue(4)：724-735,12.DOI:10.13810/j.cnki.issn.1000-7210.2024.04.011
基于一种注意力机制U-Net的地震数据去噪方法
Seismic data de-noising method based on an attention mechanism U-Net
摘要
Abstract
Due to various factors such as instruments,equipment,and environment during field acquisition,there often exist various types of noise in seismic data,including surface waves,ghost waves,random noise,etc.,affecting the reliability and accuracy of seismic data processing and interpretation.Recently,methods based on artificial intelligence have become a research hotspot in seismic data denoising,as they have high com-puting efficiency and good numerical effects.U-Net is a classic convolutional neural network structure com-monly used in image segmentation tasks.Attention mechanism(AM)is a technique that allows models to fo-cus more on specific regions or features during the learning process.This paper constructs a U-Net with atten-tion function by adding an AM module to the U-Net network and applies it to seismic data denoising.To ad-dress the boundary effects generated during the denoising process,the expansion filling method is used to seg-ment data.This method has strong universality and can be used for other network models.By comparing the de-noising effect of AU-Net and U-Net,it has been proved that AU-Net network has better denoising effect than that of the U-Net,which can better preserve weak signals.Meanwhile,AU-Net denoising method is more adap-table by transfer learning.关键词
地震勘探/深度学习/U型网络/地震数据去噪/神经网络Key words
seismic exploration/deep learning/U-Net/seismic data denoising/neural network分类
天文与地球科学引用本文复制引用
曹静杰,高康富,许银坡,王乃建,张纯,朱跃飞..基于一种注意力机制U-Net的地震数据去噪方法[J].石油地球物理勘探,2024,59(4):724-735,12.基金项目
本项研究受国家自然科学基金项目"面向城市地质的三维地震勘探压缩感知采集设计与数据重建研究"(41974166)、河北省自然科学基金项目"基于深度学习和模型驱动的地震数据重建方法研究"(D2021403010)、"黏弹介质逆时偏移成像研究"(D2021403040)、河北省自然资源厅项目"基于光纤传感的地下空间智能监测方法与应用"、河北地质大学科技创新团队项目"地震信号处理与应用团队"(KJCXTD202106)联合资助. （41974166）
