Simultaneous dictionary learning and denoising for seismic data
Simultaneous dictionary learning and denoising for seismic data
复制标题
地震数据的同步字典学习和去噪
DOI：
10.1190/geo2013-0382.1
复制
发表时间：
2014
期刊：
影响因子：
3.3
通讯作者：
Ma Jianwei
中科院分区：
文献类型：
--
作者：
Beckouche Simon;Ma Jianwei
我们评估了字典学习（DL）的地震数据去噪方法。数据被分成更小的块，并学习了块大小原子的字典。DL方法提供了一个更灵活的框架，根据地震数据本身自适应地构建稀疏数据表示。从数据中学习的表示不依赖于对数据形态的猜测，如标准小波或曲波变换。该方法可以同时或分两个不同的步骤学习字典和去噪地震数据。对实测数据的实验研究表明，与小波、曲波、各向异性全变分和非局部全变分相比，该方法在信噪比和弱特征保留方面具有良好的去噪性能。
We evaluated a dictionary learning (DL) method for seismic-data denoising. The data were divided into smaller patches, and a dictionary of patch-size atoms was learned. The DL method offers a more flexible framework to adaptively construct sparse data representation according to the seismic data themselves. The representation being learned from the data, did not rely on a guess of the data morphology like standard wavelet or curvelet transforms. The method could learn a dictionary and denoise seismic data, whether simultaneously or in two distinctive steps. Empirical study on field data showed promising denoising performance of the presented method in terms of signal-to-noise ratio and weak-feature preservation, in comparison with wavelets, curvelets, anisotropic total variation, and nonlocal total variation.