Suppressing Drilling Noise From Seismic Data Based on Multiscale Generator Network With Adaptive Feature Extraction

Yifan Ma;Xiaotao Wen;Yang Lei;Wu Wen;Hongping Ren
{"title":"Suppressing Drilling Noise From Seismic Data Based on Multiscale Generator Network With Adaptive Feature Extraction","authors":"Yifan Ma;Xiaotao Wen;Yang Lei;Wu Wen;Hongping Ren","doi":"10.1109/LGRS.2024.3496482","DOIUrl":null,"url":null,"abstract":"In oilfield exploration and development, drilling noise creates significant interference, severely reducing the signal-to-noise ratio (SNR) of seismic data. Due to the complex characteristics of noise and signal, suppressing noise while recovering effective signals poses a challenge for denoising models. To address this, we propose a multiscale generator network with adaptive feature extraction for drilling noise suppression. The constructed network primarily consists of a dual-layer encoder-decoder structure. In the encoder, we designed an adaptive feature extraction module (AFEM) and a depthwise separable encoding module. The former utilizes deformable convolutions for adaptive extraction of effective features in seismic data, while the latter employs depthwise separable convolutions and an inverse bottleneck design to reduce computational complexity while maintaining effective feature extraction. The decoding module in the decoder uses two convolutional layers to reconstruct the seismic data with minimal computational cost. To prevent network degradation, residual connections are employed in both the encoding and decoding modules. The dual-layer structure extracts semantic information at different scales, preserving richer effective signal features and maximizing drilling noise suppression. Experimental results on both synthetic and field data demonstrate that the proposed method achieves higher-quality denoising results compared to denoising convolutional neural network (CNNS) (DnCNN), Unet, and MLGNet, while retaining the maximum amount of effective data.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10755013/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In oilfield exploration and development, drilling noise creates significant interference, severely reducing the signal-to-noise ratio (SNR) of seismic data. Due to the complex characteristics of noise and signal, suppressing noise while recovering effective signals poses a challenge for denoising models. To address this, we propose a multiscale generator network with adaptive feature extraction for drilling noise suppression. The constructed network primarily consists of a dual-layer encoder-decoder structure. In the encoder, we designed an adaptive feature extraction module (AFEM) and a depthwise separable encoding module. The former utilizes deformable convolutions for adaptive extraction of effective features in seismic data, while the latter employs depthwise separable convolutions and an inverse bottleneck design to reduce computational complexity while maintaining effective feature extraction. The decoding module in the decoder uses two convolutional layers to reconstruct the seismic data with minimal computational cost. To prevent network degradation, residual connections are employed in both the encoding and decoding modules. The dual-layer structure extracts semantic information at different scales, preserving richer effective signal features and maximizing drilling noise suppression. Experimental results on both synthetic and field data demonstrate that the proposed method achieves higher-quality denoising results compared to denoising convolutional neural network (CNNS) (DnCNN), Unet, and MLGNet, while retaining the maximum amount of effective data.
基于自适应特征提取的多尺度生成器网络抑制地震数据中的钻井噪声
在油田勘探开发中,钻井噪声对地震资料的干扰较大,严重降低了地震资料的信噪比。由于噪声和信号的复杂特性,在恢复有效信号的同时抑制噪声对去噪模型提出了挑战。为了解决这个问题,我们提出了一种具有自适应特征提取的多尺度发电机网络,用于钻井噪声抑制。所构建的网络主要由双层编码器-解码器结构组成。在编码器中,设计了自适应特征提取模块(AFEM)和深度可分编码模块。前者利用可变形卷积自适应提取地震数据中的有效特征,后者采用深度可分离卷积和逆瓶颈设计,在保持有效特征提取的同时降低计算复杂度。解码器中的解码模块使用两个卷积层以最小的计算成本重建地震数据。为了防止网络退化,在编码和解码模块中都使用了剩余连接。双层结构提取不同尺度的语义信息,保留更丰富的有效信号特征,最大限度地抑制钻井噪声。综合数据和现场数据的实验结果表明,该方法在保留最大有效数据量的前提下,比卷积神经网络(CNNS) (DnCNN)、Unet和MLGNet去噪获得了更高质量的去噪结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信