Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

A review of artificial intelligence technologies in mineral identification : classification and visualization

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.The National Natural Science Foundation of China.https://www.mdpi.com/journal/jsanElectrical, Electronic and Computer Engineerin

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Mineral grains recognition using computer vision and machine learning

Identifying and counting individual mineral grainsc composing sand is an important component of many studies in environment, engineering, mineral exploration, ore processing and the foundation of geometallurgy. Typically, silt (32–128 μm) and sand (128–1000 μm) sized grains will be characterized under an optical microscope or a scanning electron microscope. In both cases, it is a tedious and costly process. Therefore, in this paper, we introduce an original computational approach in order to automate mineral grains recognition from numerical images obtained with a simple optical microscope. To the best of our knowledge, it is the first time that the current computer vision based on machine learning algorithms is tested for the automated recognition of such mineral grains. In more details, this work uses the simple linear iterative clustering segmentation to generate superpixels and many of them allow isolating sand grains, which is not possible with classical segmentation methods. Also, the approach has been tested using convolutional neural networks (CNNs). However, CNNs did not give as good results as the superpixels method. The superpixels are also exploited to extract features related to a sand grain. These image characteristics form the raw dataset. Prior to proceed with the classification, a data cleaning stage is necessary to get a usable dataset for machine learning algorithms. In addition, we present a comparison of performances of several algorithms. The overall obtained results are approximately 90% and demonstrate the concept of mineral recognition from a sample of sand grains provided by a numerical image

Hyperspectral drill-core scanning in geometallurgy

Driven by the need to use mineral resources more sustainably, and the increasing complexity of ore deposits still available for commercial exploitation, the acquisition of quantitative data on mineralogy and microfabric has become an important need in the execution of exploration and geometallurgical test programmes. Hyperspectral drill-core scanning has the potential to be an excellent tool for providing such data in a fast, non- destructive and reproducible manner. However, there is a distinct lack of integrated methodologies to make use of these data through-out the exploration and mining chain. This thesis presents a first framework for the use of hyperspectral drill-core scanning as a pillar in exploration and geometallurgical programmes. This is achieved through the development of methods for (1) the automated mapping of alteration minerals and assemblages, (2) the extraction of quantitative mineralogical data with high resolution over the drill-cores, (3) the evaluation of the suitability of hyperspectral sensors for the pre-concentration of ores and (4) the use of hyperspectral drill- core imaging as a basis for geometallurgical domain definition and the population of these domains with mineralogical and microfabric information.:Introduction Materials and methods Assessment of alteration mineralogy and vein types using hyperspectral data Hyperspectral imaging for quasi-quantitative mineralogical studies Hyperspectral sensors for ore beneficiation 3D integration of hyperspectral data for deposit modelling Concluding remarks Reference

Chemometrics approach to FT-IR hyperspectral imaging analysis of degradation products in artwork cross-section

Ascertain the distribution of materials and that of their degradation products in historical artifacts is crucial to understand their conservation status. Among the different analytical techniques that can be used, FT-IR imaging supplies information on the molecular composition of the material on a micrometric-scale in a nondestructive way (i.e. respecting the physical integrity of the material/object and without inducing visible damage to the object. This is possible by limiting the sampling to very small amounts.) (K.H.A. Janssens, R. van Grieken, Non-destructive microanalysis of cultural heritage materials, Elsevier, 2004). When thin sections of the material are not exploitable for transmission, and when ATR imaging mode is not suitable due to possible damages on the sample surface, FT-IR imaging is performed in reflection mode on thick polished, matrix embedded samples. Even if many efforts have been done in the optimization of the sample preparation, the material's surface quality is a critical issue that can hinder the achievement of good infrared images. Moreover, spectral artifacts due to volume and surface interactions can yield uncertain results in standard data treatment. In this paper we address a multivariate statistical analysis as an alternative and complementary approach to obtain high contrast FT-IR large images from hyperspectral data obtained by reflection μ-FTIR analysis. While applications of Principal Component Analysis (PCA) for chemical mapping is well established, no clustering unsupervised method applied to μ-FTIR data have been reported so far in the field of analytical chemistry for cultural heritage. In order to obtain certain chemical distribution of the stratigraphy materials, in this work the use of Hierarchical Cluster Analysis (HCA), validated with a supervised Principal Component based k-Nearest Neighbor (PCA-kNN) Analysis, has been successfully used for the re-construction of the μ-FTIR image, extracting useful information from the complex data set. A case study (a patina from the Arch of Septimius Severus in the Roman Forum) is presented to validate the model and to show new perspectives for FT-IR imaging in art conservation

Petrophysical rock typing in Uinta Basin using models powered by machine learning algorithms

Petrophysical characterization is key to identifying different rock types for hydrocarbon production optimization. Rock-typing, a petrophysical characterization technique, can be performed using wireline measurements, such as triple combo and special logs; however, this identification needs to be verified using laboratory characterization to enhance the accuracy of rock-typing prediction models. In this work, we implement an integrated characterization workflow for 600 ft of the core from the Uinta Basin, including total organic carbon, source rock analysis, elemental (X-ray Fluorescence) and mineral (Fourier-transform Infrared Spectroscopy) composition, total porosity (High-pressure pycnometer, Nuclear Magnetic Resonance), pore throat size distribution (Mercury Injection Capillary Pressure), and microstructure (Scanning Electron Microscopy). Wireline measurements include the triple combo and the sonic logs. Principal Component Analysis and K-means (as an unsupervised machine learning algorithm) were applied to both datasets (core and log) to cluster and classify different rock types. In parallel, the petrophysical systematic for each rock type was evaluated. The Uinta group is vastly diverse, having a wide range of porosity (2-18%) and TOC (0.5-10%). Three main rock types were identified type 1-siliceous rich, type 2-calcite rich, and type 3-dolomite rich. The relative contribution of types 1, 2, and 3 is 37, 42, and 21 %, respectively. The top section of the analyzed core is dominated by rock type 1, which generally has the highest porosity and relatively higher TOC. Most of the bottom section is carbonate-rich rock types, in which calcite-rich and dolomite-rich layers are interbedded. SEM analyses suggest that a fraction of the porosity is associated with organic matter. Between rock types 3 and 2, further studies indicate that the high dolomite rock type and high total porosity tend to have larger pore size, and better-sorted grains, while the high calcite rock type has lower porosity and small pore size. There is a fair agreement in rock type identification between using core-derived and log-derived models. The Uinta basin leads the hydrocarbon production in Utah. The study provides a comprehensive core analysis dataset highlighting the vertical complexity of the Uinta group. The agreement in rock-typing using core and wireline inputs suggests that log-derived rock-typing can be utilized to identify sweet zones

Automated Segmentation of Olivine Phenocrysts in a Volcanic Rock Thin Section Using a Fully Convolutional Neural Network

An example of automated characterization and interpretation of the textural and compositional characteristics of solids phases in thin sections using machine learning (ML) is presented. In our study, we focus on the characterization of olivine in volcanic rocks, which is a phase that is often chemically zoned with variable Mg/(Mg + Fe) ratios, so-called magnesian number or mg#. As the olivine crystals represent only less than 10 vol% of the volcanic rock, a pre-processing step is necessary to automatically detect the phases of interest in the images on a pixel level, which is achieved using Deep Learning. A major contribution of the presented approach is to use backscattered electron (BSE) images to: 1) automatically segment all olivine crystals present in the thin section; 2) determine quantitatively their mg#; and 3) identify different populations depending on zoning type (e.g., normal vs reversal zoning) and textural characteristics (e.g., microlites vs phenocrysts). The segmentation of the olivine crystals is implemented with a pretrained fully convolutional neural network model with DeepLabV3 architecture. The model is trained to identify olivine crystals in backscattered electron images using automatically generated training data. The training data are generated automatically from images which can easily be created from X-Ray element maps. Once the olivines are identified in the BSE images, the relationship between BSE intensity value and mg# is determined using a simple regression based on a set of microprobe measurements. This learned functional relationship can then be applied to all olivine pixels of the thin section. If the highest possible map resolution (1 micron per 1 pixel) is selected for the data acquisition, the full processing time of an entire thin section of (Formula presented.) containing more than 1,500 phenocrysts and 20.000 microliths required 140 h of data acquisition (BSE + X-Ray element maps), 8 h of training and 16 h of segmentation and classification. Our further tests demonstrated that the 140 h of data acquisition can be reduced at least by a factor of 4 since only a part of the thin section area (25% or even less) needs to be used for training. The characterization of each additional thin section would only require the BSE data acquisition time (less than 48 h for a whole thin section), without an additional training step. The paper describes the training and processing in detail, shows analytical results and outlines the potential of this Deep Learning approach for petrological applications, resulting in the automatic characterization and interpretation of mineral textures and compositions with an unprecedented high resolution. Copyright © 2022 Leichter, Almeev, Wittich, Beckmann, Rottensteiner, Holtz and Sester

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

DEEP LEARNING FOR SHALE SEM IMAGE ANALYSIS

The microstructure of unconventional reservoir rocks not only controls the storage and transport of hydrocarbons but also controls the mechanical properties of the shale. Scanning Electron Microscopy (SEM) has been valuable in understanding the microstructure of reservoir rocks. However, quantitative image analysis has been proven to be difficult. There are many limitations to image analysis that produce significant errors in determining areal porosity and organic matter content within shales. Current methods in building a suitable database for statistical analysis is time intensive, requires a trained technician, and cannot deal with the thousands of images already collected. This research evaluates the application of machine learning, more specifically Deep Learning, to reduce the time required to analyze SEM images from days, for a single large-area high-resolution MAPS area to a matter of seconds for a single image. The objective of the initial work presented was to determine if there were significant microstructural differences between different formations that could be captured by computer software. In order to avoid acquiring large amounts of data required for training a network from scratch, the technique called transfer learning was applied to the pretrained convolutional neural network (CNN) AlexNet (Krizhevsky et al. 2017). This technique allows a user to re-teach the pre-trained network to focus on a new dataset than it was originally trained on. The dataset used comprised of 27,000 images (each 512x512 pixels) from 18 different formations spanning range of maturities. Results from this study generated probabilities of classification in association with different formations. Images with higher probability to other formations other than the intended label suggests there are microstructural similarities between formations. This work proved that convolutional neural networks can learn to identify features from the shale microstructure with an accuracy of 92%. As a result, this method was applied for classifying image quality with reasonable accuracy of 95% accuracy. In addition to classification, CNNs can be applied to individual pixels within an image for classification. This is known as image segmentation. The focus in this topic is the identification and quantification of discrete objects such as pores, grains, organics, etc. applied directly to SEM images. When the model was applied to a large-area, high resolution maps with a large enough representative area (REA), it can provide representative and accurate results of area porosity and organic matter content (OM), consistent with lab measured porosity and TOC values. Accuracy of segmentation range from 92-99% for intersection over union metric (IoU) when classifying pore, OM and mineral content. Direct inspection of the images when compared to data generated using the Ilastik software proved to surpass the random forest method by more accurately defining boundaries between labels. The model was trained using Woodford images but was able to be successfully applied to images from other formations such as the Marcellus, Vaca Muerta, and Eagle Ford shales in addition to the Osage formation in the STACK play. This method was then expanded to identify carbonates, silicates, and other heavy minerals in addition to pore and organics. A sensitivity study was done in order to determine the best model. The sensitivity study was done to determine whether deeper or shallower models performed better with the data, more or less convolutional layers in the model, or a narrower or wider model performed better with the data, more or less filers per convolutional layer. This research shows that applications of CNNs to shales can quickly and accurately provide results in identifying similar formations in addition to features of interest