Mineral Deposit Modeling with Pseudo-genetically Constructed Training Images PDF Download
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Author: Jeffery Brian Boisvert
Publisher:
ISBN: 9780494299388
Category : Facies (Geology)
Languages : en
Pages : 72
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Book Description
The selection of a training image for use in multiple point statistics can be difficult. Often, there is little objective evidence to select a specific training image. A methodology to rank training images based on a multiple point statistical comparison to exploration data is presented as objective support for selecting an appropriate training image.
Author: Jeffery Brian Boisvert
Publisher:
ISBN: 9780494299388
Category : Facies (Geology)
Languages : en
Pages : 72
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Book Description
The selection of a training image for use in multiple point statistics can be difficult. Often, there is little objective evidence to select a specific training image. A methodology to rank training images based on a multiple point statistical comparison to exploration data is presented as objective support for selecting an appropriate training image.
Author: Allen L. Clark
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
Author: R. V. Kirkham
Publisher: St. John's, Nfld. : Geological Association of Canada
ISBN:
Category : Nature
Languages : en
Pages : 816
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Book Description
Author: Daniel A. Silva Maureira
Publisher:
ISBN:
Category : Entropy (Information theory)
Languages : en
Pages : 222
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Book Description
Deterministic geologic modeling methods accurately characterize large-scale continuous features of geological phenomena, but often fail in reproducing their inherent short-scale variability. The opposite is the case with stochastic methods that lack large-scale continuity yet contain reasonable short-scale variability. Both methods are limited in their ability to account for a balanced amount of geologic variability. Integrating both large and short scale geologic elements properly improves the prediction of mineral resources and reserves. This thesis develops a methodology that improves the characterization of the geologic variability in mineral deposits. The central idea is to combine deterministic and stochastic geologic interpretations and transfer the essential geological features into geostatistical models. The multiple point statistics (MPS) simulation method is suitable for this task. This technique utilizes training images for extracting and then reproducing complicated geomorphological features in the models. The method has been adapted to integrate information from different images. Generally, training images are designed based on conceptual models of the geologic phenomena; in this work, deterministic and stochastic geologic representations are used as data-driven training images, one comes from modeling the categories by an implicit geologic approach, and another comes from the application of conventional sequential indicator simulation (SIS) method. Such data-driven training images permit a robust inference of spatial structure from reasonably spaced drillhole data. This work establishes the principles to integrate multiple training images through a scheme of data integration for conditional probabilities known as a linear opinion pool. A methodology for calibrating the contribution of each training image is developed based on the variability at the available drillholes. A measure of multipoint entropy along the drillholes is matched by the combination of the two training images. The resulting calibrated models integrate geologic features from both training images, reproducing the correct underlying continuity and variability of the deposit, and reducing misclassified ore/waste material. Practical implementation of the methodology shows improvement in the predicted profit relative to classical geostatistical approaches.
Author: D. B. Stoeser
Publisher:
ISBN: 9780607949209
Category : Mines and mineral resources
Languages : en
Pages :
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Book Description
A compilation of previously published works of USGS covering the general subject of mineral deposit modeling and numerous individual mineral deposit models.
Author: B.S. Daya Sagar
Publisher: Springer
ISBN: 3319789996
Category : Science
Languages : en
Pages : 911
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Book Description
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.
Author: Karen D. Kelley
Publisher:
ISBN: 9781629499291
Category : Prospecting
Languages : en
Pages :
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Book Description
Earth's near-surface mineralogy has diversified over more than 4.5 b.y. from no more than a dozen preplanetary refractory mineral species (what have been referred to as "ur-minerals" by Hazen et al., 2008) to around 5,000 species (based on the list of minerals approved by the International Mineralogical Association; http://rruff.info/ima). This dramatic diversification is a consequence of three principal physical, chemical, and biological processes: element selection and concentration (primarily through planetary differentiation and fluidrock interactions); an expanded range of mineral-forming environments (including temperature, pressure, redox, and activities of volatile species); and the influence of the biosphere.
Author: Roger Marjoribanks
Publisher: Springer Science & Business Media
ISBN: 9401158223
Category : Science
Languages : en
Pages : 123
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Book Description
This book is written as a practical field manual to effective. Each geolOgist has to develop his/her be used by geologists engaged in mineral explo own techniques and will ultimately be judged on ration. It is also hoped that it will serve as a text results, not the process by which these results and reference for students in Applied Geology were reached. In mineral exploration, the only courses of universities and colleges. The book 'right' way of doing anything is the way that aims to outline some of the practical skills that locates ore in the quickest and most cost-effective turn the graduate geologist into an explo manner. It is preferable, however, for an individ rationist:. It is intended as a practical 'how to' ual to develop his/her own method of operation book, rather than as a text on geological or ore after having tried, and become aware of, those deposit theory. procedures which experience has shown to work An explorationist is a professional who search well and which are generally accepted in indus try as good exploration practice. es for ore bodies in a scientific and structured way. Although an awkward and artificial term, The chapters of the book approximately fol this is the only available word to describe the low the steps which a typical exploration pro totality of the skills which are needed to locate gramme would go through. In Chapter 1, the and define economic mineralization.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author: Frank Hutter
Publisher: Springer
ISBN: 3030053180
Category : Computers
Languages : en
Pages : 223
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Book Description
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
