Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Deep Learning For Seismic Data Enhancement And Representation
Download Deep Learning For Seismic Data Enhancement And Representation full books in PDF, epub, and Kindle. Read online Deep Learning For Seismic Data Enhancement And Representation ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Deep Learning for Seismic Data Enhancement and Representation by : Jiefu Chen
Download or read book Deep Learning for Seismic Data Enhancement and Representation written by Jiefu Chen and published by Springer. This book was released on 2024-12-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.
Book Synopsis Seismic Data Interpretation using Digital Image Processing by : Abdullatif A. Al-Shuhail
Download or read book Seismic Data Interpretation using Digital Image Processing written by Abdullatif A. Al-Shuhail and published by John Wiley & Sons. This book was released on 2017-06-05 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridging the gap between modern image processing practices by the scientific community at large and the world of geology and reflection seismology This book covers the basics of seismic exploration, with a focus on image processing techniques as applied to seismic data. Discussions of theories, concepts, and algorithms are followed by synthetic and real data examples to provide the reader with a practical understanding of the image processing technique and to enable the reader to apply these techniques to seismic data. The book will also help readers interested in devising new algorithms, software and hardware for interpreting seismic data. Key Features: Provides an easy to understand overview of popular seismic processing and interpretation techniques from the point of view of a digital signal processor. Presents image processing concepts that may be readily applied directly to seismic data. Includes ready-to-run MATLAB algorithms for most of the techniques presented. The book includes essential research and teaching material for digital signal and image processing individuals interested in learning seismic data interpretation from the point of view of digital signal processing. It is an ideal resource for students, professors and working professionals who are interested in learning about the application of digital signal processing theory and algorithms to seismic data.
Book Synopsis Seismic Interpretation: The Physical Aspects by : Nigel A. Anstey
Download or read book Seismic Interpretation: The Physical Aspects written by Nigel A. Anstey and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 641 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this course we shall assume that all participants are familiar with the essentials of seismic prospecting. Thus A the rudiments of the field work -- spreads, sources, arrays B and digital recording -- are assumed known. So also are the C rudiments of processing -- such processes as gain recovery, D filtering, deconvolution, velocity analysis, and display. E Just as important, we shall assume that all participants F have some feeling for the realities of seismic work -- in the l(B) field, under real conditions. Elementary signal theory and the basic techniques of interpretation are also assumed known. However, for certainty, the following pre-course notes include sections reviewing basic signal theory, geophysical aspects of interpretation, and geological aspects of interpretation. These reviews are not intended to be comprehensive. Their function is solely to cover, with the minimum possible discussion, the essential features which will be assumed to be known in the course. None of the course time will be spent on the material of these pre-course notes. Participants are advised that they will not derive full benefit from the course if this background is not known. Most course participants will be already familiar with this material, and will need to do little more than read it through. If, before the course, any participant requires further discussion of signal theory in the same non-rigorous style, he will find it in other writings of the present author, particularly: "Wiggles", Journal of the CSEG, December 1965, pp.l3-43.
Book Synopsis Deep Learning for Scientific Data Representation and Generation by : Jun Han
Download or read book Deep Learning for Scientific Data Representation and Generation written by Jun Han and published by . This book was released on 2022 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Interpreting Subsurface Seismic Data by : Rebecca Bell
Download or read book Interpreting Subsurface Seismic Data written by Rebecca Bell and published by Elsevier. This book was released on 2022-05-27 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interpreting Subsurface Seismic Data presents recent advances in methodologies for seismic imaging and interpretation across multiple applications in geophysics including exploration, marine geology, and hazards. It provides foundational information for context, as well as focussing on recent advances and future challenges. It offers detailed methodologies for interpreting the increasingly vast quantity of data extracted from seismic volumes. Organized into three parts covering foundational context, case studies, and future considerations, Interpreting Subsurface Seismic Data offers a holistic view of seismic data interpretation to ensure understanding while also applying cutting-edge technologies. This view makes the book valuable to researchers and students in a variety of geoscience disciplines, including geophysics, hydrocarbon exploration, applied geology, and hazards. - Presents advanced seismic detection workflows utilized cutting-edge technologies - Integrates geophysics and geology for a variety of applications, using detailed examples - Provides an overview of recent advances in methodologies related to seismic imaging and interpretation
Book Synopsis Trends in Deep Learning Methodologies by : Vincenzo Piuri
Download or read book Trends in Deep Learning Methodologies written by Vincenzo Piuri and published by Academic Press. This book was released on 2020-11-12 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. - Provides insights into the theory, algorithms, implementation and the application of deep learning techniques - Covers a wide range of applications of deep learning across smart healthcare and smart engineering - Investigates the development of new models and how they can be exploited to find appropriate solutions
Book Synopsis Meta-attributes and Artificial Networking by : Kalachand Sain
Download or read book Meta-attributes and Artificial Networking written by Kalachand Sain and published by John Wiley & Sons. This book was released on 2022-08-16 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying machine learning to the interpretation of seismic data Seismic data gathered on the surface can be used to generate numerous seismic attributes that enable better understanding of subsurface geological structures and stratigraphic features. With an ever-increasing volume of seismic data available, machine learning augments faster data processing and interpretation of complex subsurface geology. Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data. Volume highlights include: Historic evolution of seismic attributes Overview of meta-attributes and how to design them Workflows for the computation of meta-attributes from seismic data Case studies demonstrating the application of meta-attributes Sets of exercises with solutions provided Sample data sets available for hands-on exercises The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Book Synopsis Machine Learning and Artificial Intelligence in Geosciences by :
Download or read book Machine Learning and Artificial Intelligence in Geosciences written by and published by Academic Press. This book was released on 2020-09-22 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. - Provides high-level reviews of the latest innovations in geophysics - Written by recognized experts in the field - Presents an essential publication for researchers in all fields of geophysics
Book Synopsis Database and Expert Systems Applications by : Christine Strauss
Download or read book Database and Expert Systems Applications written by Christine Strauss and published by Springer Nature. This book was released on 2022-07-28 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 13426 and 13427, constitutes the thoroughly refereed proceedings of the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna in August 2022. The 43 full papers presented together with 20 short papers in these volumes were carefully reviewed and selected from a total of 120 submissions. The papers are organized around the following topics: Big Data Management and Analytics, Consistency, Integrity, Quality of Data, Constraint Modelling and Processing, Database Federation and Integration, Interoperability, Multi-Databases, Data and Information Semantics, Data Integration, Metadata Management, and Interoperability, Data Structures and much more.
Book Synopsis Deep Learning for Data Analytics by : Himansu Das
Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. - Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. - Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks - Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning
Book Synopsis Supercomputing by : Vladimir Voevodin
Download or read book Supercomputing written by Vladimir Voevodin and published by Springer Nature. This book was released on 2024-01-04 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 14388 and 14389 constitutes the refereed proceedings of the 9th Russian Supercomputing Days International Conference (RuSCDays 2023) held in Moscow, Russia, during September 25-26, 2023. The 44 full papers and 1 short paper presented in these proceedings were carefully reviewed and selected from 104 submissions. The papers have been organized in the following topical sections: supercomputer simulation; distributed computing; and HPC, BigData, AI: algorithms, technologies, evaluation.
Book Synopsis Seismic Diffraction by : Tijmen Jan Moser
Download or read book Seismic Diffraction written by Tijmen Jan Moser and published by SEG Books. This book was released on 2016-06-30 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of diffraction imaging to complement the seismic reflection method is rapidly gaining momentum in the oil and gas industry. As the industry moves toward exploiting smaller and more complex conventional reservoirs and extensive new unconventional resource plays, the application of the seismic diffraction method to image sub-wavelength features such as small-scale faults, fractures and stratigraphic pinchouts is expected to increase dramatically over the next few years. “Seismic Diffraction” covers seismic diffraction theory, modeling, observation, and imaging. Papers and discussion include an overview of seismic diffractions, including classic papers which introduced the potential of diffraction phenomena in seismic processing; papers on the forward modeling of seismic diffractions, with an emphasis on the theoretical principles; papers which describe techniques for diffraction mathematical modeling as well as laboratory experiments for the physical modeling of diffractions; key papers dealing with the observation of seismic diffractions, in near-surface-, reservoir-, as well as crustal studies; and key papers on diffraction imaging.
Book Synopsis Improving Accuracy and Efficiency of Seismic Data Analysis Using Deep Learning by : Harpreet Kaur (Ph. D.)
Download or read book Improving Accuracy and Efficiency of Seismic Data Analysis Using Deep Learning written by Harpreet Kaur (Ph. D.) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ultimate goal of seismic data analysis is to retrieve high-resolution information about the subsurface structures. It comprises different steps such as data processing, model building, wave propagation, and imaging, etc. Increasing the resolution and fidelity of the different seismic data analysis tasks eventually leads to an improved understanding of fine-scale structural features. Conventional implementation of these techniques is computationally intensive and expensive, especially with large data sets. Recent advances in neural networks have provided an ability to produce a reasonable result to computationally intensive and time-consuming problems. Deep neural networks are capable of extracting complex nonlinear relationships among variables and have shown efficacy as compared to conventional statistical methods in different areas. A major bottleneck for seismic data analysis is the tradeoff between resolution and efficiency. I address some of these challenges by implementing neural network based frameworks. First, I implement a neural network based workflow for stable and efficient wave extrapolation. Conventionally, it is implemented by finite differences (FD), which have a low computational cost but for larger time-steps may suffer from dispersion artifacts and instabilities. On the other hand, recursive integral time extrapolation (RITE) methods, especially the low-rank extrapolation, which are mixed-domain space-wavenumber operators are designed to make time extrapolation stable and dispersion free in heterogeneous media for large time steps, even beyond the Nyquist limit. They have high spectral accuracy; however, they are expensive as compared to finite-difference extrapolation. The proposed framework overcomes the numerical dispersion of finite-difference wave extrapolation for larger time steps and provides stable and efficient wave extrapolation results equivalent to low-rank wave extrapolation at a significantly reduced cost. Second, I address wave-mode separation and wave-vector decomposition problem to separate a full elastic wavefield into different wavefields corresponding to their respective wave mode. Conventionally, wave mode separation in heterogeneous anisotropic media is done by solving the Christoffel equation in all phase directions for a given set of stiffness-tensor coefficients at each spatial location of the medium, which is a computationally expensive process. I circumvent the need to solve the Christoffel equation at each spatial location by implementing a deep neural network based framework. The proposed approach has high accuracy and efficiency for decoupling the elastic waves, which has been demonstrated using different models of increasing complexity. Third, I propose a hyper-parameter optimization (HPO) workflow for a deep learning framework to simulate boundary conditions for acoustic and elastic wave propagation. The conventional low-order implementation of ABCs and PMLs is challenging for strong anisotropic media. In the tilted transverse isotropic (TTI) case, instabilities may appear in layers with PMLs owing to exponentially increasing modes, which eventually degrades the reverse time migration output. The proposed approach is stable and simulates the effect of higher-order absorbing boundary conditions in strongly anisotropic media, especially TTI media, thus having a great potential for application in reverse time migration. Fourth, I implement a coherent noise attenuation framework, especially for ground-roll noise attenuation using deep learning. Accounting for non-stationary properties of seismic data and associated ground-roll noise, I create training labels using local-time frequency transform (LTF) and regularized non-stationary regression (RNR). The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Lastly, I address the limitation of the iterative methods with conventional implementation for true amplitude imaging. I implement a workflow to correct migration amplitudes by estimating the inverse Hessian operator weights using a neural network based framework. To incorporate non-stationarity in the framework, I condition the input migrated image with different conditioners like the velocity model and source illumination. To correct for the remnant artifacts in the deep neural network (DNN) output, I perform iterative least-squares migration using neural network output as an initial model. The network output is close to the true model and therefore, with fewer iterations, a true-amplitude image with the improved resolution is obtained. The proposed method is robust in areas with poor illumination and can easily be generalized to more-complex cases such as viscoacoustic, elastic, and others. The proposed frameworks are numerically stable with high accuracy and efficiency and are, therefore, desirable for different seismic data analysis tasks. I use synthetic and field data examples of varying complexities in both 2D and 3D to test the practical application and accuracy of the proposed approaches
Book Synopsis Deep Learning through Sparse and Low-Rank Modeling by : Zhangyang Wang
Download or read book Deep Learning through Sparse and Low-Rank Modeling written by Zhangyang Wang and published by Academic Press. This book was released on 2019-04-26 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics. Combines classical sparse and low-rank models and algorithms with the latest advances in deep learning networks Shows how the structure and algorithms of sparse and low-rank methods improves the performance and interpretability of Deep Learning models Provides tactics on how to build and apply customized deep learning models for various applications
Book Synopsis Metaheuristics in Water, Geotechnical and Transport Engineering by : Xin-She Yang
Download or read book Metaheuristics in Water, Geotechnical and Transport Engineering written by Xin-She Yang and published by Newnes. This book was released on 2012-09 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence. Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation Develops new hybrid and advanced methods suitable for civil engineering problems at all levels Appropriate for researchers and advanced students to help to develop their work
Book Synopsis Deep Learning for the Earth Sciences by : Gustau Camps-Valls
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Book Synopsis Machine Learning for Data Science Handbook by : Lior Rokach
Download or read book Machine Learning for Data Science Handbook written by Lior Rokach and published by Springer Nature. This book was released on 2023-08-17 with total page 975 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
