Quality factor (Q) and reflectivity are two important subsurface properties in seismic data processing and interpretation. They can be calculated simultaneously from a seismic trace corresponding to an anelastic layered model by a simultaneous inversion method based on the nonstationary convolutional model. However, the conventional simultaneous inversion method calculates the optimum Q and reflectivity based on the minimum of the reflectivity sparsity by sweeping each Q value within a predefined range. As a result, the accuracy and computational efficiency of the conventional method depend heavily on the predefined Q value set. To improve the performance of the conventional simultaneous inversion method, we have developed a dictionary learning-based simultaneous inversion of Q and reflectivity. The parametric dictionary learning method is used to update the initial predefined Q value set automatically. The optimum Q and reflectivity are calculated from the updated Q value set based on minimizing not only the sparsity of the reflectivity but also the data residual. Synthetic data and two field data sets are used to test the effectiveness of our method. The results demonstrate that our method can effectively improve the accuracy of these two parameters compared to the conventional simultaneous inversion method. In addition, the dictionary learning method can improve computational efficiency up to approximately seven times when compared to the conventional method with a large predefined dictionary.

中文翻译：

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使用字典学习同时反演 Q 和反射率

质量因子（Q）和反射率是地震数据处理和解释中的两个重要地下特性。它们可以通过基于非平稳卷积模型的同时反演方法从对应于非弹性分层模型的地震道同时计算。然而，传统的同时反演方法通过在预定范围内扫描每个Q值，基于反射率稀疏度的最小值来计算最佳Q和反射率。因此，传统方法的准确性和计算效率在很大程度上取决于预定义的Q值集。为了提高传统同时反演方法的性能，我们开发了一种基于字典学习的Q和反射率同时反演。参数字典学习方法用于自动更新初始预定义的Q值集。最佳Q 值和反射率由更新后的Q值计算值设置基于不仅最小化反射率的稀疏性而且最小化数据残差。合成数据和两个现场数据集用于测试我们方法的有效性。结果表明，与传统的同时反演方法相比，我们的方法可以有效地提高这两个参数的精度。此外，与具有大型预定义字典的传统方法相比，字典学习方法可以将计算效率提高约七倍。