...
  • 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 >Microseismic Denoising and Reconstruction by Unsupervised Machine Learning
【24h】

Microseismic Denoising and Reconstruction by Unsupervised Machine Learning

机译：无监督机器学习微震去噪与重建

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

Microseismic data reconstruction is a procedure to compensate for acquisition deficiencies and to improve the data quality, which is important for subsequent processing steps such as event location. The performance of most reconstruction methods depends on 1) their parameter settings and 2) degrades greatly in case of strong noise interference. We propose an unsupervised machine learning algorithm to realize the incomplete noisy data reconstruction, using the Indian Buffet Process (IBP) as a prior to learning an appropriate dictionary from the noisy data. An approximation to the full posterior is obtained via Gibbs sampling, yielding an ensemble of dictionary and sparse coefficients. Finally, the signal of interest is reconstructed by the product of the dictionary and sparse coefficients. Tests on synthetic and real microseismic data demonstrate that the proposed method works very well for low signal-to-noise ratio data with missing traces.
机译：微震数据重建是补偿采集缺陷并提高数据质量的过程，这对于随后的处理步骤（例如事件位置）很重要。大多数重建方法的性能取决于1）参数设置，2）在强烈的噪声干扰的情况下大大降低。我们提出了一种无监督的机器学习算法来实现不完整的嘈杂数据重建，在从嘈杂的数据中学习适当的字典之前，使用印度自助式过程（IBP）。通过GIBBS采样获得到完整后部的近似，从而产生字典和稀疏系数的集合。最后，由字典和稀疏系数的乘积重建感兴趣的信号。对合成和实际微震数据的测试表明，该方法对于具有缺失迹线的低信噪比数据，该方法非常适用于低信噪比数据。

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