machine learning for subsurface characterization

Machine Learning For Subsurface Characterization
Author: Siddharth Misra
Publisher: Gulf Professional Publishing
Release Date: 2019-10-12
Pages: 440
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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

Machine Learning For The Subsurface Characterization At Core  Well  And Reservoir Scales
Author: Hao Li
Publisher:
Release Date: 2020
Pages: 228
ISBN:
Available Language: English, Spanish, And French
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Multifrequency Electromagnetic Data Interpretation For Subsurface Characterization
Author: Siddharth Misra
Publisher: Elsevier
Release Date: 2021-02-15
Pages: 275
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Multifrequency Electromagnetic Data Interpretation for Subsurface Characterization focuses on the development and application of electromagnetic measurement methodologies and their interpretation techniques for subsurface characterization. The book guides readers on how to characterize and understand materials using electromagnetic measurements, including dielectric permittivity, resistivity and conductivity measurements. This reference will be useful for subsurface engineers, petrophysicists, subsurface data analysts, geophysicists, hydrogeologists, and geoscientists who want to know how to develop tools and techniques of electromagnetic measurements and interpretation for subsurface characterization. Includes case studies to add additional color to the presented content Provides codes for the mechanistic modeling of multi-frequency conductivity and relative permittivity of porous geomaterials Presents detailed descriptions of multifrequency electromagnetic data interpretation models and inversion algorithm

Machine Learning For Spatial Environmental Data
Author: Mikhail Kanevski
Publisher: EPFL Press
Release Date: 2009-06-09
Pages: 377
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Accompanying CD-RM contains Machine learning office software, MLO guide (pdf) and examples of data.

GeoENV VI     Geostatistics For Environmental Applications
Author: Amílcar Soares
Publisher: Springer Science & Business Media
Release Date: 2008-03-12
Pages: 511
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This volume contains 40 selected full-text contributions from the Sixth European Conference on Geostatistics for Environmental Applications, geoENV IV, held in Rhodes, Greece, October 25-26, 2006. The objective of the editors was to compile a set of papers from which the reader could perceive how geostatistics is applied within the environmental sciences. A few selected theoretical contributions are also included.

The Development Of Comparative Information Yield Curves For Application To Subsurface Characterization
Author: Felipe Pereira Jorge De Barros
Publisher:
Release Date: 2009
Pages: 338
ISBN:
Available Language: English, Spanish, And French
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Future Directions For The U S  Geological Survey S Energy Resources Program
Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
Release Date: 2018-09-04
Pages: 168
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Reliable, affordable, and technically recoverable energy is central to the nation's economic and social vitality. The United States is both a major consumer of geologically based energy resources from around the world and - increasingly of late - a developer of its own energy resources. Understanding the national and global availability of those resources as well as the environmental impacts of their development is essential for strategic decision making related to the nation's energy mix. The U.S. Geological Survey Energy Resources Program is charged with providing unbiased and publicly available national- and regional-scale assessments of the location, quantity, and quality of geologically based energy resources and with undertaking research related to their development. At the request of the Energy Resources Program (ERP), this publication considers the nation's geologically based energy resource challenges in the context of current national and international energy outlooks. Future Directions for the U.S. Geological Survey's Energy Resources Program examines how ERP activities and products address those challenges and align with the needs federal and nonfederal consumers of ERP products. This study contains recommendations to develop ERP products over the next 10-15 years that will most effectively inform both USGS energy research priorities and the energy needs and priorities of the U.S. government.

Soft Computing For Reservoir Characterization And Modeling
Author: Patrick Wong
Publisher: Springer Science & Business Media
Release Date: 2001-12-04
Pages: 586
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

In the middle of the 20th century, Genrich Altshuller, a Russian engineer, analysed hundreds of thousands of patents and scientific publications. From this analysis, he developed TRIZ (G. Altshuller, "40 Principles: TRIZ Keys to Technical Innovation. TRIZ Tools," Volume 1, First Edition, Technical Innovation Center, Inc. , Worcester, MA, January 1998; Y. Salamatov, "TRIZ: The Right Solution at the Right Time. A Guide to Innovative Problem Solving. " Insytec B. V. , 1999), the theory of inventive problem solving, together with a series of practical tools for helping engineers solving technical problems. Among these tools and theories, the substance-field theory gives a structured way of representing problems, the patterns of evolution show the lifecycle of technical systems, the contradiction matrix tells you how to resolve technical contradictions, using the forty principles that describe common ways of improving technical systems. For example, if you want to increase the strength of a device, without adding too much extra weight to it, the contradiction matrix tells you that you can use "Principle 1: Segmentation," or "Principle 8: Counterweight," or "Principle 15: Dynamicity," or "Principle 40: Composite Materials. " I really like two particular ones: "Principle 1: Segmentation," and Principle 15: Dynamicity. " "Segmentation" shows how systems evolve from an initial monolithic form into a set of independent parts, then eventually increasing the number of parts until each part becomes small enough that it cannot be identified anymore.

A Machine Learning And Computer Vision Framework For Damage Characterization And Structural Behavior Prediction
Author: Rouzbeh Davoudi
Publisher:
Release Date: 2019
Pages: 158
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This work focuses on using computer vision to relate surface (and limited subsurface) damage observations to quantitative damage and load levels in structural components. In particular, image processing and machine learning regression techniques have been used to build predictive models capable of estimating internal loads (e.g., shear and moment) and damage states in RC beams, slabs, and panels based on surface crack pattern images. The predictive models have been generated and tested using image data sets obtained from earlier published studies, which together provide about 1,500 crack pattern images captured from about 170 individual RC beam, slab, and panel tests across a range of load and damage levels. Working with these existing image data sets, various textural and geometric attributes of surface crack patterns have been defined and evaluated with respect to their efectiveness in building useful estimation models. Relatively simple crack representations have been used, consistent with the varying nature of the images available in the earlier studies, but also with an eye toward potential field applications in which image capture and segmentation quality could be limited. The fundamental state quantification tasks range from the relatively simple (e.g., given an image showing damage, generate from that image a predicted load level in terms of percentage of capacity) to relatively advanced (e.g., estimating principal stresses in a panel). In addition to investigating the basic feasibility of the approach, these studies also identify and evaluate a range of strategies, algorithms, and parameters that affect the accuracy of the estimations, and these are discussed, as well. In general terms, the results show that the predictive models based on surface crack image data can work well across a wide range of geometries, loadings, concrete strengths, and reinforcement details. Size effects can be accounted for by including specimen physical dimensions in the feature sets used for model training, and fundamental design relations can be used to develop useful non-dimensional prediction parameters.

Subsurface Hydrology
Author: David W. Hyndman
Publisher: American Geophysical Union
Release Date: 2007-01-09
Pages: 253
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 171. Groundwater is a critical resource and the PrinciPal source of drinking water for over 1.5 billion people. In 2001, the National Research Council cited as a "grand challenge" our need to understand the processes that control water movement in the subsurface. This volume faces that challenge in terms of data integration between complex, multi-scale hydrologie processes, and their links to other physical, chemical, and biological processes at multiple scales. Subsurface Hydrology: Data Integration for Properties and Processes presents the current state of the science in four aspects: Approaches to hydrologie data integration Data integration for characterization of hydrologie properties Data integration for understanding hydrologie processes Meta-analysis of current interpretations Scientists and researchers in the field, the laboratory, and the classroom will find this work an important resource in advancing our understanding of subsurface water movement.

Dissertation Abstracts International
Author:
Publisher:
Release Date: 2006
Pages:
ISBN:
Available Language: English, Spanish, And French
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Study Of AI Based Methods For Characterization Of Geotechnical Site Investigation Data
Author: Hui Wang
Publisher:
Release Date: 2020
Pages: 51
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Due to the inadequate knowledge of the soil forming histories and/or human activities, the subsurface soil layers are difficult to ascertain. Subsurface uncertainty and its influence on geotechnical design have long been a challenge facing practitioners. Recently, the ASCE Geo-institute has developed the Data Interchange for Geotechnical and Geoenvironmental Specialists (DIGGS), which is a standard schema for transferring geotechnical data between multiple organizations. It paves the way of sharing and unifying datasets and forms a structural database for further data-driven modeling and analysis. The Office of Geotechnical Engineering at ODOT (OGE) is taking a national leading role in supporting the development efforts of DIGGS and hence make this project possible. In this study, site investigation data in DIGGS format and archived format are jointly processed. An innovative technique developed by the research team has been further improved for better application in real-world projects. Bayesian machine learning is integrated with Markov random field models to infer and simulate subsurface models and geospatial data with quantified uncertainty. Spatial heterogeneity and statistical characteristics are modeled in terms of statistical and spatial patterns. These patterns serve as a basis to provide a synthesized interpretation of the soil profiles with uncertainty quantified. Four (4) validation projects have been performed in this report and the results are well documented. Summary and recommendations for future work are also provided. A short introduction of the key concepts behind this technique, and pathway for converting the existing program into a ready for implementation web-based program for potential ODOT usages are provided in the appendices.

Government Reports Annual Index
Author:
Publisher:
Release Date: 1994
Pages:
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Sections 1-2. Keyword Index.--Section 3. Personal author index.--Section 4. Corporate author index.-- Section 5. Contract/grant number index, NTIS order/report number index 1-E.--Section 6. NTIS order/report number index F-Z.

Government Reports Annual Index  Personal Author
Author:
Publisher:
Release Date: 1993
Pages:
ISBN:
Available Language: English, Spanish, And French
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Government Reports Announcements   Index
Author:
Publisher:
Release Date: 1996
Pages:
ISBN:
Available Language: English, Spanish, And French
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Computational Science     ICCS 2019
Author: João M. F. Rodrigues
Publisher: Springer
Release Date: 2019-06-07
Pages: 663
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems Part III: Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Classifier Learning from Difficult Data; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems Part IV: Track of Data-Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Marine Computing in the Interconnected World for the Benefit of the Society; Track of Multiscale Modelling and Simulation; Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation Part V: Track of Smart Systems: Computer Vision, Sensor Networks and Machine Learning; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Track ICCS 2019 Chapter “Comparing Domain-decomposition Methods for the Parallelization of Distributed Land Surface Models” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Environmental Modeling And Management
Author:
Publisher:
Release Date: 2002
Pages: 276
ISBN:
Available Language: English, Spanish, And French
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Peterson S Graduate Programs In Engineering   Applied Sciences 2007
Author: Peterson's (Firm : 2006- )
Publisher: Petersons
Release Date: 2006-11
Pages: 1135
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Provides information about admission, financial aid, programs and institutions, and research specialties within the fields of engineering and applied sciences, including civil engineering, information technology, and bioengineering.

The Robotics Institute       Annual Research Review
Author: Carnegie-Mellon University. Robotics Institute
Publisher:
Release Date: 1992
Pages:
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Annual Research Review
Author: Carnegie-Mellon University. Robotics Institute
Publisher:
Release Date: 1991
Pages:
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS: