Download or read book Deep Learning for Automatic Geophysical Interpretation with Uncertainty Quantification written by Nam Phuong Pham and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geophysical interpretation such as picking faults and geobodies, analyzing well logs, and picking arrivals is a tedious, manual, and time-consuming process. Deep learning is a data-driven technique that has been getting more attention recently in different fields, such as medical imaging and computer vision. With large volumes of available data of different types and advancements in computing technology, geophysics is a promising field for applying deep learning. Applying deep learning to geophysical interpretation can make the process faster and the workflow less subjective. Decision-making based on interpretation is uncertain. Therefore, uncertainties in geophysical interpretation are very important. To utilize the deep learning models effectively, uncertainties from data and models' parameters need to be quantified. In this dissertation, I address the problem by including uncertainties in several deep learning-based interpretation algorithms, and show the feasibility of applying them to various geophysical interpretation problems on different types of data. First, I develop a generative adversarial network to produce data that have style from a particular region in the field data. Different styles allow to generate different data to train various convolutional neural networks for automatic fault picking in 2D seismic images. I use a bootstrapping method to generate prediction scenarios and quantify the uncertainties from training data. Second, I introduce an end-to-end network for picking channel geobodies in 3D seismic volumes, which includes uncertainties from data and the model's parameters. This workflow is fast and easy to quantify uncertainties, not only from data, but also from the parameters of a neural network. I then apply a similar workflow to quantify the uncertainties from the model's parameters in picking channel facies and faults simultaneously in 3D seismic volumes. I also analyze the relationship between quantified uncertainties and geologic features in the seismic volumes. Apart from applying the workflow to the segmentation problem, I design a recurrent style network for predicting missing sonic logs from gamma-ray, density, and neutron porosity logs. This is a regression problem with two outputs of compressional and shear sonic logs. The workflow generates mean prediction and quantile values for upper and lower bounds. In the last chapter, I apply a transformer-based network for picking arrivals of earthquake data. I change from discrete labels of 0 and 1, where ones are picks, to continuous distributions with peaks at picks. This helps to quantify the uncertainties of the picking algorithm along time. Finally, I discuss some limitations and suggest some possible future research topics
Download or read book Automatic Channel Detection Using Deep Learning written by Nam Phuong Pham and published by . This book was released on 2019 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Picking 3D channel geobodies in seismic volumes is an important objective in seismic interpretation for hydrocarbon exploration. Manual detection of channel geobodies is a time-consuming and subjective process. The interpreter can calculate different seismic attributes such as coherence to aid for manual detection of channel geobodies in seismic volumes. However, these attributes still do not directly identify 3D channel geobodies. Machine learning and deep learning are data-driven techniques that have been getting more attention recently in different fields, such as medical imaging and computer vision. With large volumes of available data in different types and a development of powerful computational resources, geophysics is a promising field for applying machine learning and deep learning. Many seismic interpretation steps are analogous to different problems in computer vision that have been solved successfully using deep learning. Channel detection in seismic volumes is analogous to segmentation problems for images. Applying deep learning to seismic interpretations, specifically to automatic channel detection in 3D seismic volumes, can make the process faster and the workflow less subjective. Decision-making based on interpretations is uncertain; so uncertainties in interpretation results are very important. Deep learning with different algorithms can also help interpreters quantify this uncertainty.
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
Download or read book Modeling Uncertainty in the Earth Sciences written by Jef Caers and published by John Wiley & Sons. This book was released on 2011-05-25 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems. The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.
Download or read book The Rock Physics Handbook written by Gary Mavko and published by Cambridge University Press. This book was released on 2020-01-09 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: Brings together widely scattered theoretical and laboratory rock physics relations critical for modelling and interpretation of geophysical data.
Download or read book Machine Learning for Subsurface Characterization written by Siddharth Misra and published by Gulf Professional Publishing. This book was released on 2019-10-12 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. - Learn from 13 practical case studies using field, laboratory, and simulation data - Become knowledgeable with data science and analytics terminology relevant to subsurface characterization - Learn frameworks, concepts, and methods important for the engineer's and geoscientist's toolbox needed to support
Download or read book Advances in Subsurface Data Analytics written by Shuvajit Bhattacharya and published by Elsevier. This book was released on 2022-05-18 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also compare the results from an algorithm with others to better equip the readers with different strategies to implement automated workflows for subsurface analysis. Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches will help researchers in academia and professional geoscientists working on the subsurface-related problems (oil and gas, geothermal, carbon sequestration, and seismology) at different scales to understand and appreciate current trends in ML approaches, their applications, advances and limitations, and future potential in geosciences by bringing together several contributions in a single volume. - Covers fundamentals of simple machine learning and deep learning algorithms, and physics-based approaches written by practitioners in academia and industry - Presents detailed case studies of individual machine learning algorithms and optimal strategies in subsurface characterization around the world - Offers an analysis of future trends in machine learning in geosciences
Download or read book Quantifying Uncertainty in Subsurface Systems written by Céline Scheidt and published by John Wiley & Sons. This book was released on 2018-06-19 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under the Earth's surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge. Volume highlights include: A multi-disciplinary treatment of uncertainty quantification Case studies with actual data that will appeal to methodology developers A Bayesian evidential learning framework that reduces computation and modeling time Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. Read the Editors' Vox: eos.org/editors-vox/quantifying-uncertainty-about-earths-resources
Download or read book Geophysical Inversion written by J. Bee Bednar and published by SIAM. This book was released on 1992-01-01 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of papers on geophysical inversion contains research and survey articles on where the field has been and where it's going, and what is practical and what is not. Topics covered include seismic tomography, migration and inverse scattering.
Download or read book Faults Fluid Flow and Petroleum Traps written by Rasoul B. Sorkhabi and published by . This book was released on 2005 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Primer on Machine Learning in Subsurface Geosciences written by Shuvajit Bhattacharya and published by Springer Nature. This book was released on 2021-05-03 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with a timely review and discussion of the success, promise, and perils of machine learning in geosciences. It explores the fundamentals of data science and machine learning, and how their advances have disrupted the traditional workflows used in the industry and academia, including geology, geophysics, petrophysics, geomechanics, and geochemistry. It then presents the real-world applications and explains that, while this disruption has affected the top-level executives, geoscientists as well as field operators in the industry and academia, machine learning will ultimately benefit these users. The book is written by a practitioner of machine learning and statistics, keeping geoscientists in mind. It highlights the need to go beyond concepts covered in STAT 101 courses and embrace new computational tools to solve complex problems in geosciences. It also offers practitioners, researchers, and academics insights into how to identify, develop, deploy, and recommend fit-for-purpose machine learning models to solve real-world problems in subsurface geosciences.
Download or read book Interactive Data Processing and 3D Visualization of the Solid Earth written by Daniel Patel and published by Springer Nature. This book was released on 2022-02-21 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents works detailing the application of processing and visualization techniques for analyzing the Earth’s subsurface. The topic of the book is interactive data processing and interactive 3D visualization techniques used on subsurface data. Interactive processing of data together with interactive visualization is a powerful combination which has in the recent years become possible due to hardware and algorithm advances in. The combination enables the user to perform interactive exploration and filtering of datasets while simultaneously visualizing the results so that insights can be made immediately. This makes it possible to quickly form hypotheses and draw conclusions. Case studies from the geosciences are not as often presented in the scientific visualization and computer graphics community as e.g., studies on medical, biological or chemical data. This book will give researchers in the field of visualization and computer graphics valuable insight into the open visualization challenges in the geosciences, and how certain problems are currently solved using domain specific processing and visualization techniques. Conversely, readers from the geosciences will gain valuable insight into relevant visualization and interactive processing techniques. Subsurface data has interesting characteristics such as its solid nature, large range of scales and high degree of uncertainty, which makes it challenging to visualize with standard methods. It is also noteworthy that parallel fields of research have taken place in geosciences and in computer graphics, with different terminology when it comes to representing geometry, describing terrains, interpolating data and (example-based) synthesis of data. The domains covered in this book are geology, digital terrains, seismic data, reservoir visualization and CO2 storage. The technologies covered are 3D visualization, visualization of large datasets, 3D modelling, machine learning, virtual reality, seismic interpretation and multidisciplinary collaboration. People within any of these domains and technologies are potential readers of the book.
Download or read book Machine Learning in Heliophysics written by Thomas Berger and published by Frontiers Media SA. This book was released on 2021-11-24 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice
Download or read book Deep Learning Algorithms and Applications written by Witold Pedrycz and published by Springer. This book was released on 2019-11-04 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.
Download or read book Theoretical and Computational Methods in Mineral Physics written by Renata M. Wentzcovitch and published by Walter de Gruyter GmbH & Co KG. This book was released on 2018-12-17 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume 71 of Reviews in Mineralogy and Geochemistry represents an extensive review of the material presented by the invited speakers at a short course on Theoretical and Computational Methods in Mineral Physics held prior (December 10-12, 2009) to the Annual fall meeting of the American Geophysical Union in San Francisco, California. The meeting was held at the Doubletree Hotel & Executive Meeting Center in Berkeley, California. Contents: Density functional theory of electronic structure: a short course for mineralogists and geophysicists The Minnesota density functionals and their applications to problems in mineralogy and geochemistry Density-functional perturbation theory for quasi-harmonic calculations Thermodynamic properties and phase relations in mantle minerals investigated by first principles quasiharmonic theory First principles quasiharmonic thermoelasticity of mantle minerals An overview of quantum Monte Carlo methods Quantum Monte Carlo studies of transition metal oxides Accurate and efficient calculations on strongly correlated minerals with the LDA+U method: review and perspectives Spin-state crossover of iron in lower-mantle minerals: results of DFT+U investigations Simulating diffusion Modeling dislocations and plasticity of deep earth materials Theoretical methods for calculating the lattice thermal conductivity of minerals Evolutionary crystal structure prediction as a method for the discovery of minerals and materials Multi-Mbar phase transitions in minerals Computer simulations on phase transitions in ice Iron at Earth’s core conditions from first principles calculations First-principles molecular dynamics simulations of silicate melts: structural and dynamical properties Lattice dynamics from force-fields as a technique for mineral physics An efficient cluster expansion method for binary solid solutions: application to the halite-silvite, NaCl-KCl, system Large scale simulations Thermodynamics of the Earth’s mantle
Download or read book Clouds and Climate written by A. Pier Siebesma and published by Cambridge University Press. This book was released on 2020-08-20 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive overview of research on clouds and their role in our present and future climate, for advanced students and researchers.
