Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

Abstract

Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing a common sparse coefficient matrix for the whole related tasks of the data. Basically, in the learning process, each task can help other tasks to learn better and thus a sparser representation is obtained. Furthermore, different from other DL methods that use a limited random number of patches to learn a dictionary, the proposed algorithm can take the whole data information into account with a reasonable time cost and thus can obtain an efficient and effective denoising performance. We have applied the method on synthetic and real 3D data, which demonstrated superior performance in random noise attenuation when compared with state-of-the-art denoising methods such as MSSA, BM4D, and FXY predictive filtering, especially in amplitude and continuity preservation in low signal-to-noise ratio cases and fault zones.

Deep Learning for Geophysics: Current and Future Trends

“…Second, a rank-1 approximation SVD algorithm is used to obtain the updated dictionary and coefficients simultaneously, thereby accelerating convergence and reducing computational memory. K-SVD is applied in geophysics with extensions to improve efficiency (Nazari Siahsar et al, 2017).…”

De‐aliased and de‐noise Cadzow filtering for seismic data reconstruction

“…It can be also used as a preprocessing step to recover missing data due to the presence of obstacles in the field, feathering of towed streamers on the sea and other different reasons (Nazari Siahsar et al . ). Generally speaking, conventional seismic reconstruction techniques are divided into four categories.…”

A Novel Tensor Ring Sparsity Measurement for Image Completion

“…These techniques have also exhibited successful implementations across various domains within the natural sciences. For example, in [5,6], they used image completion methods to achieve reliable, high-quality seismic data, which is crucial before the subsequent processing steps. Moreover, in [7], they used the image completion method to acquire dense earthquake data, which largely promotes the understanding of the structure and dynamics of Earth.…”