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

Yifan Ma;Xiaotao Wen;Yang Lei;Wu Wen;Hongping Ren
{"title":"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","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":"{\"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}","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
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Suppressing Drilling Noise From Seismic Data Based on Multiscale Generator Network With Adaptive Feature Extraction
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Deeper and Broader Multimodal Fusion: Cascaded Forest-of-Experts for Land Cover Classification Impact of Targeted Sounding Observations From FY-4B GIIRS on Two Super Typhoon Forecasts in 2024 Structural Representation-Guided GAN for Remote Sensing Image Cloud Removal Multispectral Airborne LiDAR Point Cloud Classification With Maximum Entropy Hierarchical Pooling A Satellite Selection Algorithm for GNSS-R InSAR Elevation Deformation Retrieval
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1