Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...     

Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification

Neural Computing and Applications - In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly...     

Hyperspectral imaging-based unsupervised adulterated red chili content transformation for classification: Identification of red chili adulterants

Neural Computing and Applications - Preserving red-chili quality is of utmost importance in which the authorities demand quality techniques to detect, classify, and prevent it from impurities. For...     

A systematic review on emperor penguin optimizer

Neural Computing and Applications - Emperor Penguin Optimizer (EPO) is a recently developed metaheuristic algorithm to solve general optimization problems. The main strength of EPO is twofold....     

Grain boundary embrittlement in Cu-AI-Niβ-phase alloys

The grain boundary embrittlement in Cu-AI-Ni-phase alloys has been investigated. The study included both the bulk alloys and rapidly solidified ribbons. It was observed that the fracture characteristics and the phase transformations in rapidly solidified ribbons were similar to those in the bulk alloys. Various factors responsible for intergranular fracture were considered. It was found that intrinsic or extrinsic precipitates at the grain boundaries are not responsible for the embrittlement. It was further observed that the segregation of impurities does not occur at the grain boundaries and hence is not a factor contributing to the embrittlement. The intrinsic characteristics, however, appear to play an important role. These may include high elastic anisotropy, ordered structure and plastic incompatibility. Severe embrittlement in high-nickel alloys is associated with spinodal decomposition occurring in these alloys. The large grain size exhibited by these alloys is found not to be a significant factor contributing to the embrittlement.     

Spin glass behaviour in the Co2−xZnxTiO4 compound

A.c. susceptibility measurements were carried out on the disordered spinel system Co_2–x Zn _x TiO₄ (0<x<1) between 10 and 80 K. Our measurements show three peaks in the versus T curve for Co₂TiO₄ and CoZnTiO₄ compounds. In both compounds two peaks are very close to each other, indicating that the Néel and semi-spin glass temperatures are very close. A third peak indicates the presence of the semi-spin glass to spin glass transition. In Co_1.5Zn_0.5TiO₄ only one peak is observed which indicates spin glass ordering at 26 K. Furthermore, the A-site canting present in Co₂TiO₄ and CoZnTiO₄ has a collinear and magnetic structure, indicating strong A-B coupling. X-ray analysis indicated that compounds of the system Co₂Zn_xTiO₄ synthesized with cubic symmetry. From transport properties it was found that the activation energy and thermoelectric coefficient decrease with increasing concentration of Zn in the system. The mobility of the system calculated from infrared measurements is typically of the order of 10^–9cm²V^–1s^–1.     

Role of Macrophages and RhoA Pathway in Atherosclerosis

The development, progression, or stabilization of the atherosclerotic plaque depends on the pro-inflammatory and anti-inflammatory macrophages. The influx of the macrophages and the regulation of macrophage phenotype, inflammatory or anti-inflammatory, are controlled by the small GTPase RhoA and its downstream effectors. Therefore, macrophages and the components of the RhoA pathway are attractive targets for anti-atherosclerotic therapies, which would inhibit macrophage influx and inflammatory phenotype, maintain an anti-inflammatory environment, and promote tissue remodeling and repair. Here, we discuss the recent findings on the role of macrophages and RhoA pathway in the atherosclerotic plaque formation and resolution and the novel therapeutic approaches.     

Preparation and Characterization of Inorganic Organic Hybrid Material Based on TEOS/MAPTMS for Biomedical Applications

Silicon - In this study, inorganic-organic hybrid material consisting of tetraethyl orthosilicate (TEOS) and 3-methacryloxypropyl trimethoxysilane (MAPTMS) were prepared with sol-gel process and...