Mineral identification using artificial neural networks and the rotating polarizer stage
TL;DR: In this article, an artificial neural network is used for the classification of minerals using thin sections acquired using the rotating polarizing microscope stage, which extracts a basic set of seven primary images during each sampling.
About: This article is published in Computers & Geosciences.The article was published on 2001-11-01. It has received 86 citations till now.
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21 Aug 2006TL;DR: In this paper, the authors present a review of three dimensional analytical methods for quantitative textural analysis, including general analytical methods, surface and section analytical methods and a detailed analysis of theoretical parameter distributions.
Abstract: Acknowledgements 1. Introduction 1.1 Petrological methods 1.2 Qualitative versus quantitative data 1.3 What do I mean by texture? 1.4 Information density and data sources 1.5 Structure of this book 1.6 Software applications for quantitative textural studies 2. General analytical methods 2.1 Introduction 2.2 Complete three dimensional analytical methods 2.3 Extraction of grain parameters from data volumes 2.4 Destructive partial analytical methods 2.5 Surface and section analytical methods 2.6 Extraction of textural parameters from images 2.7 Calculation of three dimensional data from two dimensional observations 2.8 Verification of theoretical parameter distributions 2.9 Summary 3. Grain and crystal sizes 3.1 Introduction 3.2 Review of theory 3.3 Analytical methods 3.4 Typical applications 4. Grain shape 4.1 Introduction 4.2 Brief review of theory 4.3 Methodology 4.4 Typical applications 5. Grain orientations - rock fabric 5.1 Introduction 5.2 Brief review of theory 5.3 Introduction to fabric methodology 5.4 Determination of shape preferred orientations 5.5 Determination of lattice preferred orientations 5.6 3D bulk fabric methods - combined SPO and LPO 5.7 Extraction of grain orientation data and parameters 5.8 Typical applications 6. Grain spatial distributions and relations 6.1 Introduction 6.2 Brief review of theory 6.3 Methodology 6.4 Typical applications 7. Textures of fluid-filled pores 7.1 Introduction 7.2 Brief review of theory 7.3 Methodology 7.4 Parameter values and display 7.5 Typical applications 8. Appendix. Computer programs for use in quantitative textural analysis - freeware, shareware and commercial 8.1 Abbreviations 8.2 General analytical methods 8.3 Grain and crystal sizes 8.4 Grain shape 8.5 Grain orientations - rock fabric 8.6 Grain spatial distributions and relations Figure captions References.
234 citations
Cites background or methods from "Mineral identification using artifi..."
...So far only the intensity component of the image has been used to evaluate four standard textural parameters called contrast, entropy, energy and homogeneity (Thompson et al., 2001)....
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...All these parameters are evaluated for each grain and the results classified using neural networks (Thompson et al., 2001) and genetic programming (Ross et al....
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TL;DR: In this paper, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated.
Abstract: Research on processing geochemical data and identifying geochemical anomalies has made important progress in recent decades Fractal/multi-fractal models, compositional data analysis, and machine learning (ML) are three widely used techniques in the field of geochemical data processing In recent years, ML has been applied to model the complex and unknown multivariate geochemical distribution and extract meaningful elemental associations related to mineralization or environmental pollution It is expected that ML will have a more significant role in geochemical mapping with the development of big data science and artificial intelligence in the near future In this study, state-of-the-art applications of ML in identifying geochemical anomalies were reviewed, and the advantages and disadvantages of ML for geochemical prospecting were investigated More applications are needed to demonstrate the advantage of ML in solving complex problems in the geosciences
122 citations
Cites methods from "Mineral identification using artifi..."
...One of the most commonly used ML methods, ANN, has proved to be a powerful tool for the classification and identification of the minerals (Thompson et al. 2001; Baykan and Yılmaz 2010)....
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TL;DR: In this paper, the authors present a special view on the quantification of these properties by classical and newly developed fractal-geometry methods, discusses advantages and disadvantages of special methods and outlines the correlations between structure quantifications and rock properties and structure-forming processes, presented in the literature.
96 citations
TL;DR: An artificial neural network with k-fold cross validation is trained with manually classified mineral samples based on their pixel values to classify 5 different minerals, namely, quartz, muscovite, biotite, chlorite, and opaque.
75 citations
TL;DR: In this article, the authors used Factor Analysis (FA) to explore statistically the data regarding geochemical patterns and to assist the identification and interpretation of element associations in the Ningqiang district of China.
66 citations
References
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01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
17,604 citations
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03 Jan 1986
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
13,579 citations
Book•
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TL;DR: This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time, and basic concepts are introduced with only essential mathematical elements.
Abstract: This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.
2,365 citations
Book•
01 Jan 1991TL;DR: This work combines neural networks and fuzzy systems, presenting neural networks as trainable dynamical systems and developing mechanisms and principles of adaption, self-organization, convergence and global stability.
Abstract: This work combines neural networks and fuzzy systems, presenting neural networks as trainable dynamical systems and developing mechanisms and principles of adaption, self-organization, convergence and global stability. It includes the new geometric theory of fuzzy sets, systems and associated memories, and shows how to apply fuzzy set theory to adaptive control and how to generate structured fuzzy systems with unsupervised neural techniques.
2,356 citations
Book•
17 Dec 1995
TL;DR: Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework and provides an intuitive explanation of each method for each network paradigm.
Abstract: From the Publisher:
The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks.
Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective.
The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary.
C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method.
The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
284 citations