• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Branch Construction-Based CNN Denoiser for Desert Seismic Data
【24h】

A Branch Construction-Based CNN Denoiser for Desert Seismic Data

机译：基于分支施工的CNN DNOISER，用于沙漠地震数据

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Seismic random noise reduction is an indispensable step in seismic data processing. Due to complex geological condition and acquisition environment, random noise in the desert seismic data has spatiotemporally variant noise levels and weak similarity to the signals, which severely obscures the seismic signals and increases the difficulty to extract the reflected seismic signals. This letter focuses on suppressing the desert random noise based on a convolutional neural network (CNN) and proposes a branch construction-based denoising network (BCDNet). The BCDNet contains a denoising main network and a branched network added to the downsampled layer of the main network. With the branched network, the global context feature of the seismic data is obtained early in the network to guide the denoising task of the subsequent main network, which allows a flexible denoising for the desert random noise. Moreover, the downsampled layer is able to enlarge the receptive field of the network without increasing the network depth, thus leading to the better retention of the structural features in the seismic records. The extensive experiments and the field desert data application confirm that our BCDNet not only has a significant denoising capacity to desert seismic data but also is competitive in training time and memory cost.
机译：地震随机降噪是地震数据处理中不可或缺的步骤。由于复杂的地质条件和采集环境，沙漠地震数据中的随机噪声具有时尚变化的噪声水平和与信号的弱相似性，这严重模糊了地震信号并增加了提取反射的地震信号的难度。这封信侧重于基于卷积神经网络（CNN）的抑制沙漠随机噪声，并提出基于分支施工的去噪网络（BCDNet）。 BCDNet包含去噪主网络和分支网络，添加到主网络的下采样层。利用分支网络，在网络的早期获得地震数据的全部上下文特征，以引导后续主网络的去噪任务，这允许对沙漠随机噪声进行灵活的去噪。此外，下采样层能够在不增加网络深度的情况下扩大网络的接收领域，从而导致震动记录中的结构特征更好地保留结构特征。广泛的实验和现场沙漠数据应用证实，我们的BCDNET不仅具有对沙漠地震数据的显着去噪能力，而且在培训时间和记忆成本方面也具有竞争力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号