Third ERTS Symposium: Abstracts

Abstracts are provided for the 112 papers presented at the Earth Resources Program Symposium held at Washington, D.C., 10-14 December, 1973

Utilization of photogrammetry during establishment of virtual rock collection at Aalto University

In recent years, we can observe increasing popularity of the term industry 4.0 which is defined as a new level of organization and control over the entire value chain of the life cycle of products. Experts distinguished nine different technologies, which are essential for the development of industry 4.0. One of them is virtual reality, which is used during processes of data visualisation and digitization. These processes can also include geological collections. Due to limited access to different geological spots, the popularity of destructive techniques during rock testing and high complexity of the process of learning geosciences, geologists are looking for new methods of digitization of different samples of rocks and minerals. The aim of this master thesis was to create a virtual collection of selected rocks and minerals using photogrammetry and virtual reality (VR) technology and develop new tool and interactive learning platform for study mineralogy and petrography. To accomplish these aims and create 3D models of specimens, the author built professional photo studio and used photogrammetric techniques to digitize the samples. The main output of this research is a virtual 3D collection of rocks and minerals that consisting of 107 samples, and which is available via two different channels: Sketchfab portal online model repository and VR environment built in Unity game engine. The virtual collection will be utilised to teach students how to identify rocks and minerals at Aalto University

Unsupervised Machine Learning Application for the Identification of Kimberlite Ore Facie using Convolutional Neural Networks and Deep Embedded Clustering

Mining is a key economic contributor to many regions globally - especially those in developing nations. The design and operation of the processing plants associated with each of these mines is highly dependant on the composition of the feed material. The aim of this research is to demonstrate the viability of implementing a computer vision solution to provide online information of the composition of material entering the plant, thus allowing the plant operators to adjust equipment settings and process parameters accordingly. Data is collected in the form of high resolution images captured every couple of seconds of material on the main feed conveyor belt into the Kao Diamond Mine processing plant. The modelling phase of the research is implemented in two stages. The first stage involves the implementation of a Mask Region-based Convolutional Neural Network (Mask R-CNN) model with a ResNet 101 CNN backbone for instance segmentation of individual rocks from each image. These individual rock images are extracted and used for the second phase of the modelling pipeline - utilizing an unsupervised clustering method known as Convolutional Deep Embedded Clustering with Data Augmentation (ConvDEC-DA). The clustering phase of this research provides a method to group feed material rocks into their respective types or facie using features developed from the auto-encoder portion of the ConvDEC-DA modelling. While this research focuses on the clustering of Kimberlite rocks according to their respective facie, similar implementations are possible for a wide range of mining and rock types

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

Global and local characterization of rock classification by Gabor and DCT filters with a color texture descriptor

In the automatic classification of colored natural textures, the idea of proposing methods that reflect human perception arouses the enthusiasm of researchers in the field of image processing and computer vision. Therefore, the color space and the methods of analysis of color and texture, must be discriminating to correspond to the human vision. Rock images are a typical example of natural images and their analysis is of major importance in the rock industry. In this paper, we combine the statistical (Local Binary Pattern (LBP) with Hue Saturation Value (HSV) and Red Green Blue (RGB) color spaces fusion) and frequency (Gabor filter and Discrete Cosine Transform (DCT)) descriptors named respectively Gabor Adjacent Local Binary Pattern Color Space Fusion (G-ALBPCSF) and DCT Adjacent Local Binary Pattern Color Space Fusion (D-ALBPCSF) for the extraction of visual textural and colorimetric features from direct view images of rocks. The textural images from the two G-ALBPCSF and D-ALBPCSF approaches are evaluated through similarity metrics such as Chi2 and the intersection of histograms that we have adapted to color histograms. The results obtained allowed us to highlight the discrimination of the rock classes. The proposed extraction method provides better classification results for various direct view rock texture images. Then it is validated by a confusion matrix giving a low error rate of 0.8% of classification

Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

British Geological Survey Annual Science Review 2012-13

The British Geological Survey (BGS) is part of the Natural Environment Research Council and is its principal supplier of national capability in geoscience. We advance understanding of the structure, properties and processes of the solid Earth system through interdisciplinary surveys, monitoring, modelling and research for the benefit of society. We are the UK’s premier provider of objective and authoritative geoscientific data, information and knowledge for creating wealth, using natural resources sustainably, reducing risk and living with the impacts of environmental change. Our vision To be the world’s leading centre for geoscience impact