Dictionary learning based on dip patch selection training for random noise attenuation

TL;DR: The results indicate the ability of the proposed algorithm in attenuating the random noise and preserving the seismic signal effectively despite the existence of a large amount of random noise, for example, when the input signal‐to‐noise ratio is as low as −14.2 dB.

TL;DR: This work achieves facies identification using a relevance vector machine (RVM) and develops a facies discriminant method based on a multikernel RVM (MKRVM), which has advantageous properties such as strong generalization ability and high accuracy.

TL;DR: A novel approach to attenuate seismic random noise based on deep convolutional neural network (CNN) in an unsupervised learning manner by adopting only the training set constructed from the raw noisy data as the input and design a robust deep CNN that just relies on the noisy input to learn the hidden features.

TL;DR: Zhang et al. as mentioned in this paper developed a deep learning framework based on deep image prior (DIP) and attention networks for 3D seismic data enhancement, where the 3D noisy data are divided into several overlapped patches.

TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.

TL;DR: This work addresses the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image, and uses the K-SVD algorithm to obtain a dictionary that describes the image content effectively.

TL;DR: This work gives examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution, and obtains reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.

TL;DR: This article obtains parallel results in a more general setting, where the dictionary D can arise from two or several bases, frames, or even less structured systems, and sketches three applications: separating linear features from planar ones in 3D data, noncooperative multiuser encoding, and identification of over-complete independent component models.