Fundamentals of Computational Geoscience

Fundamentals of Computational Geoscience PDF Author: Chongbin Zhao
Publisher: Springer Science & Business Media
ISBN: 3540897437
Category : Science
Languages : en
Pages : 248

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Book Description
Geoscience is a fundamental natural science discipline dealing with the origin, evolutionary history and behaviour of the planet Earth. As a result of its complicated and complex nature, the Earth system not only provides the necessary materials and environment for mankind to live, but also brings many types of natural disasters, such as earthquakes, volcanic eruptions, tsunamis, ?oods and tornadoes, to mention just a few. With the ever-increasing demand for improving our living standards, it has been recognized that the existing natural resources will be exhausted in the near future and that our living environments are, in fact, deteriorating. To maintain the sustainable development of our living standards and the further improvement of our living environments, an inevitable and challenging task that geoscientists are now confronting is how accurately to predict not only the occurrences of these natural disasters, but also the locations of large concealed natural resources in the deep Earth. For this reason, geoscientists must study the processes, rules and laws, by which the Earth system operates, instead of simply describing and observing g- science phenomena.

Computational Geosciences with Mathematica

Computational Geosciences with Mathematica PDF Author: William Haneberg
Publisher: Springer Science & Business Media
ISBN: 3642185541
Category : Science
Languages : en
Pages : 386

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Book Description
Computational Geosciences with Mathematica is the only book written by a geologist specifically to show geologists and geoscientists how to use Mathematica to formulate and solve problems. It spans a broad range of geologic and mathematical topics, which are drawn from the author's extensive experience in research, consulting, and teaching. The reference and text leads readers step-by-step through geologic applications such as custom graphics programming, data input and output, linear and differential equations, linear and nonlinear regression, Monte Carlo simulation, time series and image analysis, and the visualization and analysis of geologic surfaces. It is packed with actual Mathematica output and includes boxed Computer Notes with tips and exploration suggestions.

Computational Methods for Geodynamics

Computational Methods for Geodynamics PDF Author: Alik Ismail-Zadeh
Publisher: Cambridge University Press
ISBN: 1139489356
Category : Science
Languages : en
Pages : 333

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Book Description
Written as both a textbook and a handy reference, this text deliberately avoids complex mathematics assuming only basic familiarity with geodynamic theory and calculus. Here, the authors have brought together the key numerical techniques for geodynamic modeling, demonstrations of how to solve problems including lithospheric deformation, mantle convection and the geodynamo. Building from a discussion of the fundamental principles of mathematical and numerical modeling, the text moves into critical examinations of each of the different techniques before concluding with a detailed analysis of specific geodynamic applications. Key differences between methods and their respective limitations are also discussed - showing readers when and how to apply a particular method in order to produce the most accurate results. This is an essential text for advanced courses on numerical and computational modeling in geodynamics and geophysics, and an invaluable resource for researchers looking to master cutting-edge techniques. Links to supplementary computer codes are available online.

3D Geoscience Modeling

3D Geoscience Modeling PDF Author: Simon Houlding
Publisher: Springer Science & Business Media
ISBN: 3642790127
Category : Science
Languages : en
Pages : 399

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Book Description
This book is a result of a career spent developing and applying computer techniques for the geosciences. The need for a geoscience modeling reference became apparent during participation in several workshops and conferences on the subject in the last three years. For organizing these, and for the lively discussions that ensued and inevitably contributed to the contents, I thank Keith Turner, Brian Kelk, George Pflug and Johnathan Raper. The total number of colleagues who contributed in various ways over the preceding years to the concepts and techniques presented is beyond count. The book is dedicated to all of them. Compilation of the book would have been impossible without assistance from a number of colleagues who contributed directly. In particular, Ed Rychkun, Joe Ringwald, Dave Elliott, Tom Fisher and Richard Saccany reviewed parts of the text and contributed valuable comment. Mohan Srivastava reviewed and contributed to some of the geostatistical presentations. Mark Stoakes, Peter Dettlaff and Simon Wigzell assisted with computer processing of the many application examples. Anar Khanji and Randal Crombe assisted in preparation of the text and computer images. Klaus Lamers assisted with printing. The US Geological Survey, the British Columbia Ministry of Environment, Dave Elliott and others provided data for the application examples. My sincere thanks to all of them.

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences PDF Author: Gustau Camps-Valls
Publisher: John Wiley & Sons
ISBN: 1119646162
Category : Technology & Engineering
Languages : en
Pages : 436

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Book Description
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.

Mathematical Geosciences

Mathematical Geosciences PDF Author: Joseph L. Awange
Publisher: Springer
ISBN: 3319673718
Category : Science
Languages : en
Pages : 615

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Book Description
This book showcases powerful new hybrid methods that combine numerical and symbolic algorithms. Hybrid algorithm research is currently one of the most promising directions in the context of geosciences mathematics and computer mathematics in general. One important topic addressed here with a broad range of applications is the solution of multivariate polynomial systems by means of resultants and Groebner bases. But that’s barely the beginning, as the authors proceed to discuss genetic algorithms, integer programming, symbolic regression, parallel computing, and many other topics. The book is strictly goal-oriented, focusing on the solution of fundamental problems in the geosciences, such as positioning and point cloud problems. As such, at no point does it discuss purely theoretical mathematics. "The book delivers hybrid symbolic-numeric solutions, which are a large and growing area at the boundary of mathematics and computer science." Dr. Daniel Li chtbau

Basics of Computational Geophysics

Basics of Computational Geophysics PDF Author: Pijush Samui
Publisher: Elsevier
ISBN: 0128209011
Category : Science
Languages : en
Pages : 437

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Book Description
Basics of Computational Geophysics provides a one-stop, collective resource for practitioners on the different techniques and models in geoscience, their practical applications, and case studies. The reference provides the modeling theory in an easy-to-read format that is verified with onsite models for specific regions and scenarios, including the use of big data and artificial intelligence. This book offers a platform whereby readers will learn theory, practical applications, and the comparison of real-world problems surrounding geomechanics, modeling and optimizations. - Covers various advanced computational techniques for solving different problems in geophysics, including the use of Big Data and artificial intelligence - Includes case studies that provide examples surrounding practical applications - Provides an assessment of the capabilities of commercial software

Computing Risk for Oil Prospects: Principles and Programs

Computing Risk for Oil Prospects: Principles and Programs PDF Author: J.W. Harbaugh
Publisher: Elsevier
ISBN: 008052950X
Category : Science
Languages : en
Pages : 467

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Book Description
The petroleum industry is enduring difficult financial times because of the continuing depressed price of crude oil on the world market. This has caused major corporate restructuring and reductions in staff throughout the industry. Because oil exploration must now be done with fewer people under more difficult economic constraints, it is essential that the most effective and efficient procedures be used. Computing Risk for Oil Prospects describes how prospect risk assessment — predicting the distribution of financial gains or losses that may result from the drilling of an exploration well — can be done using objective procedures implemented on personal computers. The procedures include analyses of historical data, interpretation of geological and geophysical data, and financial calculations to yield a spectrum of the possible consequences of decisions. All aspects of petroleum risk assessment are covered, from evaluating regional resources, through delineating an individual prospect, to calculation of the financial consequences of alternative decisions and their possible results. The bottom lines are given both in terms of the probable volumes of oil that may be discovered and the expected monetary returns. Statistical procedures are linked with computer mapping and interpretation algorithms, which feed their results directly into routines for financial analysis. The programs in the included library of computer programs are tailored to fit seamlessly together, and are designed for ease and simplicity of operation. The two diskettes supplied are IBM compatible. Full information on loading is given in Appendix A - Software Installation. Risk I diskette contains data files and executables and Risk 2 diskette contains only executables. The authors contend that the explorationist who develops a prospect should be involved in every facet of its analysis, including risk and financial assessments. This book provides the tools necessary for these tasks.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences PDF Author:
Publisher: Academic Press
ISBN: 0128216840
Category : Science
Languages : en
Pages : 318

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Book Description
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

Mathematical Modeling of Earth's Dynamical Systems

Mathematical Modeling of Earth's Dynamical Systems PDF Author: Rudy Slingerland
Publisher: Princeton University Press
ISBN: 1400839114
Category : Science
Languages : en
Pages : 246

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Book Description
A concise guide to representing complex Earth systems using simple dynamic models Mathematical Modeling of Earth's Dynamical Systems gives earth scientists the essential skills for translating chemical and physical systems into mathematical and computational models that provide enhanced insight into Earth's processes. Using a step-by-step method, the book identifies the important geological variables of physical-chemical geoscience problems and describes the mechanisms that control these variables. This book is directed toward upper-level undergraduate students, graduate students, researchers, and professionals who want to learn how to abstract complex systems into sets of dynamic equations. It shows students how to recognize domains of interest and key factors, and how to explain assumptions in formal terms. The book reveals what data best tests ideas of how nature works, and cautions against inadequate transport laws, unconstrained coefficients, and unfalsifiable models. Various examples of processes and systems, and ample illustrations, are provided. Students using this text should be familiar with the principles of physics, chemistry, and geology, and have taken a year of differential and integral calculus. Mathematical Modeling of Earth's Dynamical Systems helps earth scientists develop a philosophical framework and strong foundations for conceptualizing complex geologic systems. Step-by-step lessons for representing complex Earth systems as dynamical models Explains geologic processes in terms of fundamental laws of physics and chemistry Numerical solutions to differential equations through the finite difference technique A philosophical approach to quantitative problem-solving Various examples of processes and systems, including the evolution of sandy coastlines, the global carbon cycle, and much more Professors: A supplementary Instructor's Manual is available for this book. It is restricted to teachers using the text in courses. For information on how to obtain a copy, refer to: http://press.princeton.edu/class_use/solutions.html