... the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, its performance for mineral exploration targeting is affected to a certain extent. For this reason, neighbourhood component analysis and dictionary learning algorithms...

...Lina Liu; Jianwei Ma ABSTRACT The dictionary learning method has been successfully applied to denoise and interpolate seismic data. However, this method cannot be used to adequately interpret weak seismic events and structural features. By combining dictionary learning and a convolutional neural...

... good results. To overcome the problem, a new strong-noise elimination method called inception-temporal convolutional network-shift-invariant sparse coding (IncepTCN-SISC) is developed based on deep learning and dictionary learning. First, a novel deep neural network model called IncepTCN is created...

...Yuhan Sui; Xiaojing Wang; Jianwei Ma ABSTRACT Seismic denoising is an essential step for seismic data processing. Conventionally, dictionary learning (DL) methods for seismic denoising always assume the representation coefficients to be sparse and the dictionary to be normalized or a tight frame...

... method does not work properly because of the unstable inversion when solving the local orthogonalization weight due to the large-amplitude erratic noise. We propose a robust dictionary learning and sparse coding algorithm to retrieve the leaked signals. In this robust method, we substitute the L 2-norm...

...Mohammed Outhmane Faouzi Zizi; Pierre Turquais ABSTRACT For a marine seismic survey, the recorded and processed data size can reach several terabytes. Storing seismic data sets is costly, and transferring them between storage devices can be challenging. Dictionary learning (DL) has been shown...

... parameter, whereas the seismic data are highly variable locally. To retrieve the leaked signals adaptively, we have adopted a new dictionary-learning (DL) method. Because of the patch-based nature of the DL method, it can adapt to the local features of seismic data. We train a dictionary of atoms...

... train an adaptive dictionary and divide the atoms of the dictionary into two subdictionaries to reconstruct these two components. We have devised an adaptive dictionary learning method for acquisition footprint suppression in the time slice of 3D poststack migration seismic data. To obtain an adaptive...

... 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...

... regularization and tensor dictionary learning. Inspired by the elastic net, we first develop the elastic half norm regularization as a sparsity constraint, and we establish a robust high-dimensional interpolation model with this technique. Then, considering the multidimensional structure and spatial correlation...

...Murad Almadani; Umair bin Waheed; Mudassir Masood; Yangkang Chen ABSTRACT Seismic data inevitably suffer from random noise and missing traces in field acquisition. This limits the use of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained...

...-value decomposition (another dictionary learning method) sparse decomposition. Experimental results illustrate that SISC provides the best performance. Robustness test results indicate that SISC can increase the signal-to-noise ratio of noisy signal from 0 to more than 15 dB. Case studies of synthetic...

...Julián L. Gómez; Danilo R. Velis ABSTRACT Dictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows for the removal of random noise from...

... method for 3C microseismic data based on joint sparse representation. The three components are represented by different dictionary atoms; the dictionary can be fixed or adaptive depending on the dictionary learning method that is used. Our method adds an extra time consistency constraint...

...Bin She; Yaojun Wang; Zhining Liu; Hanpeng Cai; Wei Liu; Guangmin Hu Abstract We have addressed the seismic impedance inversion problem, which is often ill posed because of inaccurate and insufficient data. The approach taken is based on dictionary learning and sparse representation. By shifting...

...) and sparse coefficients. Dictionary learning has a critical role in obtaining a state-of-the-art sparse representation. A good dictionary should capture the representative features of the data. The whole signal can be used as training patches to learn a dictionary. However, this approach suffers from high...

... earthquakes that are not similar to other earthquakes. In recent years, machine‐learning techniques for earthquake detection have been emerging as a new active research direction. In this article, we develop a novel earthquake detection method based on dictionary learning. Our detection method first generates...

...Chao Zhang; Mirko van der Baan; Ting Chen ABSTRACT Waveform enhancement methods generally explore lateral coherency in arrivals, often assuming a linear moveout across an array, as exhibited by plane waves. We illustrate how unsupervised dictionary learning combined with orthogonal matching pursuit...