Intelligent Identification for Rock-Mineral Microscopic Images Using Ensemble Machine Learning Algorithms

It is significant to identify rock-mineral microscopic images in geological engineering. The task of microscopic mineral image identification, which is often conducted in the lab, is tedious and time-consuming. Deep learning and convolutional neural networks (CNNs) provide a method to analyze mineral microscopic images efficiently and smartly. In this research, the transfer learning model of mineral microscopic images is established based on Inception-v3 architecture. The four mineral image features, including K-feldspar (Kf), perthite (Pe), plagioclase (Pl), and quartz (Qz or Q), are extracted using Inception-v3. Based on the features, the machine learning methods, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), multilayer perceptron (MLP), and gaussian naive Bayes (GNB), are adopted to establish the identification models. The results are evaluated using 10-fold cross-validation. LR, SVM, and MLP have a significant performance among all the models, with accuracy of about 90.0%. The evaluation result shows LR, SVM, and MLP are the outstanding single models in high-dimensional feature analysis. The three models are also selected as the base models in model stacking. The LR model is also set as the meta classifier in the final prediction. The stacking model can achieve 90.9% accuracy, which is higher than all the single models. The result also shows that model stacking effectively improves model performance

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

Reservoir Characterisation of Gas Shale through Sedimentary, Mineralogical, Petrophysical and Statistical Rock Types Evaluation

The successful exploration and production of the gas shale reservoirs can help to face the current energy crisis. However, shale is a fine-grained heterogeneous rock, so its exploration and development are challenging. This research has provided an integrated method for analysis, evaluation, and synthesis of potential gas shale formations in the Canning Basin, Western Australia. The results form a valuable case study that is applicable to many other sedimentary basins throughout the world

Petrographic analysis with deep convolutional neural networks

Petrographic analysis is based on the microscopic description and classification of rocks and is a crucial technique for sedimentary and diagenetic studies. When compared to hand specimens, thin sections of rocks provide better and more accurate means for analysis of mineral distribution and percentage, pore space analysis, and cement composition. Because of the rich information they contain, thin section data are commonly used not only by the mining and petroleum industry, but by the academic community as well. Most petrographic analysis relies on visual inspection of rock thin sections under a microscope, a task that is laborious even for experienced geologists. Large projects with a tight time frame requiring the analysis of a large amount of thin sections may require multiple petrographers, thereby risking the introduction of inconsistency in the analysis. To address this challenge, we explore the use of deep convolutional neural networks (CNN) as a tool that can allow the petrographer to analyze and classify more samples in a consistent manner. Unlike previous studies using deep learning models trained on large volumes of thin section data, we make use of transfer learning based on robust and reliable CNN models trained with a large amount of non-geological images. With a much smaller number of labeled thin sections used in training followed by “fine-tuning” we are able to construct convolutional neural networks that achieve low error levels (<5% when images of same quality are used for training and testing) in thin section classification. While becoming widely accepted as a useful tool in the biological and manufacturing disciplines, CNN is currently underutilized in the geoscience community; we foresee an increase of use of such techniques to help accelerate and quantify a wide variety of geological tasks

A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.</jats:p

A GIS-based method for archival and visualization of microstructural data from drill core samples.

Core samples obtained from scientific drilling could provide large volumes of direct microstructural and compositional data, but generating results via the traditional treatment of such data is often time-consuming and inefficient. Unifying microstructural data within a spatially referenced Geographic Information System (GIS) environment provides an opportunity to readily locate, visualize, correlate, and explore the available microstructural data. Using 26 core billet samples from the San Andreas Fault Observatory at Depth (SAFOD), this study developed procedures for: 1. A GIS-based approach for spatially referenced visualization and storage of microstructural data from drill core billet samples; and 2. Producing 3D models of sample billets and thin section positions within each billet, which serve as a digital record after irreversible material loss and fragmentation of physical billets. This approach permits spatial registration of 2D thin section ‘base maps’ within the core sample billets, where each billet is represented by 3D solid surface (produced via SFM photogrammetry) and internal structure models (acquired with micro-CT scans) created prior to sectioning. The spatial positions of the base maps were established within locally defined coordinate systems in each core billet’s solid surface model. The GIS database structure provided interactive linkage to the results of various analyses performed throughout the map at a wide range of scales (e.g. SEM and CL images as well as text and numerical data) within each thin section. The viability of the proposed framework was demonstrated via display of integrated microstructural data, creation of vector point information associated with features of interest in CL imagery, and development of a model for extraction and unsupervised classification of a multi-generation calcite vein network from the CL imagery. The results indicate that a GIS can facilitate the spatial treatment of 2D and 3D data even at centimeter to nanometer scales, building upon existing work which is predominantly limited to the 2D space of single thin sections. Conversely, the research effort also revealed several challenges, particularly involving intensive 3D representations and complex matrix transformations required to create geographically translated forms of the within-billet coordinate systems, which are suggested for consideration in future studies

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Marble is one of the semi-precious stones that has been used in decorating building façade and making decorative things. This stone is present in the nature in the form of rock or layered stone. Examining the kind of stone, extent of impurity and different streaks in white marble is a widely confronted subject by those who are involved in this industry. Obtaining the extent of impurity of white marble using methods of detecting and analyzing material is expensive and time-consuming. In this research carried out on while marbles of Arc Mine in Birjand, it has been attempted to present very fast method using Image Processing Techniques so that while preserving identity and appearance of stone and without any damage to it, we compute the impurity level and different streaks on white marble surface. The proposed method includes two stages; in the first stage applying image processing functions, it is attempted to segment the present impurities and streaks on marble surface from the stone background and in the second stage, the area of these impurities and streaks is computed. Results obtained in this paper (97.8%) in comparison with other researches and experimental methods indicate acceptability of this algorithm

Automatic Gemstone Classification Using Computer Vision

This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas