A new paper is added to the collection of reproducible documents: Double sparsity dictionary for seismic noise attenuation

A key step in sparsifying signals is the choice of a sparsity-promoting dictionary. There are two basic approaches to design such a dictionary: the analytic approach and the learning-based approach. While the analytic approach enjoys the advantage of high efficiency, it lacks adaptivity to various data patterns. On the other hand, the learning-based approach can adaptively sparsify different datasets but has a heavier computational complexity and involves no prior-constraint pattern information for particular data. We propose a double sparsity dictionary (DSD) for seismic data in order to combine the benefits of both approaches. We provide two models to learn the DSD: the synthesis model and the analysis model. The synthesis model learns DSD in the data domain, and the analysis model learns DSD in the model domain. We give an example of the analysis model and propose to use the seislet transform and data-driven tight frame (DDTF) as the base transform and adaptive dictionary respectively in the DSD framework. DDTF obtains an extra structure regularization by learning dictionaries, while the seislet transform obtains a compensation for the transformation error caused by slope dependency. The proposed DSD aims to provide a sparser representation than the individual transform and dictionary and therefore can help achieve better performance in denoising applications. Although for the purpose of compression, the proposed DSD is less sparse than the seislet transform, it outperforms both seislet and DDTF in distinguishing signal and noise. Two simulated synthetic examples and three field data examples confirm a better denoising performance of the proposed approach.

A new paper is added to the collection of reproducible documents: Seismic data interpolation using nonlinear shaping regularization

Seismic data interpolation plays an indispensable role in common seismic data processing workflows. Iterative shrinkage thresholding (IST) and projection onto convex sets (POCS) can both be considered as a specific form of nonlinear shaping regularization. Compared with linear form of shaping regularization, the nonlinear version can be more adaptive because the shaping operator is not limited to be linear. With a linear combination operator, we introduce a faster version of nonlinear shaping regularization. The new shaping operator utilizes the information of previous model to better constrain the current model. Both synthetic and field data examples demonstrate that the nonlinear shaping regularization can be effectively used to interpolate irregular seismic data and the proposed faster version of shaping regularization can indeed get obvious faster convergence.

A new paper is added to the collection of reproducible documents: Ground-roll noise attenuation using a simple and effective approach based on local bandlimited orthogonalization

Bandpass filtering is a common way to estimate ground-roll noise on land seismic data, because of the relatively low frequency content of ground-roll. However, there is usually a frequency overlap between ground-roll and the desired seismic reflections that prevents bandpass filtering alone from effectively removing ground-roll without also harming the desired reflections. We apply a bandpass filter with a relatively high upper bound to provide an initial, imperfect separation of ground-roll and reflection signal. We then apply a technique called ‘local orthogonalization’ to improve the separation. The procedure is easily implemented, since it involves only bandpass filtering and a regularized division of the initial signal and noise estimates. We demonstrate the effectiveness of the method on an open-source set of field data.