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Compartmentalization of hydrocarbon reservoirs, change in fluid contacts, effects of high permeable fractures around faults, and hydrocarbon traps created by seal faults make fault detection and extraction as a necessity in the analysis of seismic data. Faulting disrupts the smoothness trend of geological layers (reflections in seismic sections) and displaces layers along its plane. Thus, a fault could be considered as an abnormal phenomenon that globally deviates normal behavior of layers around its plane. In the present study, faults are considered as sparse global anomalies in a seismic section that can be extracted using Gaussian process regression. The Gaussian process regression is a nonparametric probabilistic model based on Bayesian statistics that can be used to model spatial properties as a regression problem. The Gaussian process usually is used to extract and describe normal interactions from the data set using smooth functions. The main idea of this study is to detect the global anomaly using Gaussian process regression. For this purpose, we considered geological layers as smooth normal events in seismic sections. Therefore, the location of the fault plane is where the Gaussian process gets an error during describing the layers. Abnormalities such as faults cause the Gaussian process to suffer an error near the anomaly. We used these errors and analyzed them to detect probable locations of fault edge. Finally, we used a consistent connection algorithm to separate most probable fault points and to connect them to an edge using morphological reconstruction algorithm. The proposed algorithm was evaluated based on the receiver operating characteristics analysis. Several synthetic seismic sections with different levels of signal to noise ratios were used to evaluate the algorithm in the presence of random noise. The results showed that all points predicted by a diagnostic test fell into the area above the diagonal of the receiver operating characteristics space, which represents a good diagnostic classification. • Faulting disrupts the smoothness trend of geological layers. • Global anomaly in a data set is an abnormal phenomenon that globally deviates normal behavior of data set. • Fault and salt dome boundary could be considered as an abnormal phenomenon in seismic data. • The Gaussian process usually is used to extract and describe normal interactions from the data. • Fault locations is where the Gaussian process gets an error during describing the layers as normal events. [ABSTRACT FROM AUTHOR]

Tube wave suppression and the separation of elastic up-going and down-going waves are the most important steps in vertical seismic profile (VSP) data processing. Different methods have been developed to suppress tube waves and separate wave fields. In this work, we use a pyramidal dual-tree directional filter bank as a multi-directional and multi-scale method. We then design two different adaptive algorithms to suppress tube waves and aliased tube waves, and to separate elastic up-going and down-going waves. In the first proposed algorithm, subscales containing tube waves and aliased tube waves were detected adaptively through energy calculation in the 2D Fourier domain, through which the filtered VSP were achieved by zeroing these subscales. When applied to field VSP data with strong tube waves, the proposed filter can attenuate both the tube wave and aliased tube wave successfully, with results outperforming those of f-k filtering, singular value decomposition and median filtering. Similarly, following tube wave and aliased tube wave suppression in the second designed algorithm, subscales with elastic up-going waves were detected fully. By reconstructing these subscales and subtracting them from the original VSP data, the wave fields were separated successfully. The performance of both algorithms in the presence of random noise was investigated and none of the algorithms were found to be highly sensitive. [ABSTRACT FROM AUTHOR]

This study employs the Lamb wave method to detect damage in Fiber-metal laminates (FMLs). The method is based on quasi-isotropic behavior approximation and beamforming techniques. Delay and sum and minimum variance distorsionless response beamformers are applied to a uniform linear phased array. The simulation in finite element software is conducted to evaluate the performance of the presented procedure. The two types of damage studied are the following: (1) Delamination between fiber-epoxy and metal layers and (2) crack on the metal layer. The present study has the following important contributions: (1) Health monitoring of multi-damaged FMLs using Lamb waves and beamforming technique, (2) detection of damage type, (3) detection of damage size by 1D phased array, and (4) identification of damages that occurred very close to the laminate edges or close to each other. [ABSTRACT FROM AUTHOR]

Faults are natural tectonic events as planar features along which rock units are moved. The patterns of these planes on seismic sections include linear or curvilinear features that are called edge in image processing, where amplitudes sharply change. In seismic data, these edge features made by fault traces are usually detected by seismic attributes, which need complex mathematical calculation such as a dip-steered cube. In this study, we introduce faults as global anomalies which disturb the normal interaction of seismic reflectors. Here, the normal interaction means the absence of intensive changes in reflectors trend in seismic sections. Fault detection as a global anomaly is done by the Gaussian process regression model (GPR), which is a nonparametric probabilistic model based on Bayesian statistics supporting noisy (Gaussian noise) and sparse features in data. In data mining, anomaly detection identifies items or events that do not match an expected pattern in a dataset. Global anomalies are sparse and affect a wide range of normal trends of data which are also held by fault features in seismic data. In this study, the GPR-based anomaly detection algorithm was implemented on the 3D seismic data of the Gulf of Mexico containing normal growth faults and a salt dome to detect salt boundary and fault traces in seismic data. In this respect, the reflections from rock units and fault features were taken into account as normal interactions and global anomalies, respectively, because faults disrupted the normal trend of reflectors in seismic sections. To detect fault locations, the location of a voxel in voxel grid where it is a part of fault in seismic data, after smoothing the seismic data, a Gaussian process (GP) model was trained on seismic data, attempting to describe the seismic amplitude data as a multivariate Gaussian model. However, GP regression fails to describe the seismic data at fault locations. Thus, the failure of the GP in the regression step was analyzed to separate the probable fault points, highlighted by calculating the variance of the GPR results. Finally, the detected probable fault points were improved and separated from background results by implementing a consistent reconstruction morphological algorithm. The results were validated using a similar structural index method, mean square error, and power signal to noise ratio indices in comparison with interpreted faults, implying the superiority of the proposed method in comparison with seismic attributes. The faults detected by the proposed method have the most structural similarity to faults interpreted. This similarity has improved by 22% compared to used attributes. • Fault detection using unsupervised Gaussian process regression model (GPR). • Fault identification as a global anomaly in 3D seismic data. • Detected fault by GPR procedure has the most structural similarity with the interpreted ones. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer's disease.