Functional Mining Research Articles

Identification of buried historical mineshaft using ground-penetrating radar

Buried historical mineshafts constitute significant sinkhole hazards and sources of geotechnical risks. The locations of buried mineshafts on old mining maps are prone to inaccuracies. An efficient way to limit these inaccuracies is to use geophysical methods. However, their effectiveness strongly depends on the local survey conditions.The aim of the study was to identify the buried 14th century Wielki mineshaft in Bochnia Salt Mine, a UNESCO Worldwide Heritage Site, Poland. The need to locate this shaft was due to the following reasons. Firstly, the Bochnia Salt Mine had difficulties with water flowing into the workings from the supposed location of the Wielki mineshaft. The shaft area was one of the most waterlogged in the salt mine's terrain. An urgent task was to determine the shaft's exact location to implement a waterproof barrier to protect the historic mine workings. Secondly, the modernisation of the market square in Bochnia required clarification of the location of the Wielki mineshaft and checking as to whether it posed a sinkhole hazard to the terrain surface. Another important reason for locating the medieval shaft was its archaeological value.A ground surface and borehole ground-penetrating radar (GPR) study and geological-engineering recognition using boreholes were employed to indicate the location of the Wielki shaft. The anthropogenic layer of heterogeneous material and the urbanised site resulted in challenges with regard to detecting evident georadar anomalies. Several dozen intersecting GPR profiles were gathered in the research area using 250 MHz and 100 MHz antennae. The initial surface GPR results, indicative of the GPR anomaly, were consolidated by cored borehole investigation and a borehole GPR study. In particular, the presence of a void, loosened zones, and wooden fragments of the shaft lining was confirmed in the inclined control borehole. Following our classification of typical causes of the occurrence of sinkholes over buried mineshafts, the consolidation of backfill material in a mineshaft was determined as the cause of the sinkhole originated above the Wielki shaft (type B sinkhole). Destruction of the shaft's wooden cap may result in the occurrence of a sinkhole on the ground surface. Therefore, the void was backfilled and a hydraulic barrier was consequently made to stop the seepage of water into the Bochnia Salt Mine's historic workings.This case study demonstrates that surface and borehole GPR surveying integrated with borehole data offers the possibility of obtaining the high-resolution data needed to accurately locate an old buried mineshaft in an urbanised area. The GPR anomaly was indirectly detected on the basis of the deflection of the boundary between the natural ground and an anthropogenic layer with sufficient contrast in their electromagnetic properties. This approach can be useful wherever the georadar penetration range is limited by high attenuation and dispersiveness of the anthropogenic layer.

Artificial intelligent based smart system for safe mining during foggy weather

AbstractOpencast mining operations at hilly areas are usually affected during foggy weather due to the inability of drivers to operate heavy earth‐moving machinery in low visibility conditions. This article deals with an intelligent vision enhancement system for continuing opencast mining operations during foggy weather. The system integrates hardware and software to provide multistage safety features that make it unique from existing systems. The system includes hardware like thermal cameras, high definition cameras, proximity radar, wireless devices, GNSS module, graphical processing unit, display unit, and so forth, and image processing software, namely real‐time image stitching, image enhancement, and object detection using convolutional neural networks. The integrated system and algorithms display a 180° panorama field view of the vehicle's front using real‐time video stitching. The front view after image processing, rear camera view, object detection through proximity radar, and real‐time location of the vehicles on a 3D geo‐tagged mine map by GNSS modules are displayed in four splitter windows on a touch screen fitted on the dashboard in front of the driver's seat. The driver can drive the vehicle by seeing the display screen during foggy weather. The output image of the developed image‐processing algorithm has less distortion, better quality, and better depth perception than existing methods. Overall, there are significant improvements in the persistence of the color elements by 39.65%, contrast by 4.62%, and the corresponding entropy by 7.11% concerning the similar existing methods. The final system has been successfully tested in an opencast mine.

Spatiotemporal Interactions between Surface Coal Mining and Land Cover and Use Changes

Long-term surface mining and land cover and use changes have been evidenced to have a critical relationship. This study conducts trend and correlation analysis by statistical tools to quantitatively evaluate this relationship in the Ptolemais (Northern Greece) coal mining area for the period 1990-2018. Firstly, based on Corine data and satellite images, a relative spatial indicator (RSI) was adopted to describe the mineral land areas. Secondly, land cover and use changes were spatially defined using Corine data and ArcGIS tools. The active mining area was then distinguished by dumping area, using Landsat satellite imagery and mining maps, and finally, mine operation parameters were statistically analyzed. The study revealed that “mineral extraction sites” present a strong correlation with “non irrigated arable land” and “transitional woodland”. The results have shown that between 1990 and 2018 the total forest area was increased by three times, mainly as a result of the dumping sites geometry. Additionally, the mine operation parameters are well correlated with the active mining area.

Multi-scale, multi-sensor data integration for automated 3-D geological mapping

Enhanced digital outcrop models attributed with hyperspectral reflectance data, or hyperclouds, provide a flexible, three-dimensional medium for data-driven mapping of geological exposures, mine faces or cliffs. This approach facilitates the collection of spatially contiguous information on exposed mineralogy, and so helps to quantify mineralising processes, interpret 1-D drillhole data, and optimise mineral extraction. In this contribution we present an open-source python workflow, hylite, for creating hyperclouds by seamlessly fusing geometric information with data from a variety of hyperspectral imaging sensors and applying necessary atmospheric and illumination corrections. These rich datasets can be analysed using a variety of techniques, including minimum wavelength mapping and spectral indices, to accurately map geological objects from a distance. Reference spectra from spectral libraries, ground or laboratory measurements can also be included to derive supervised classifications using machine learning techniques. We demonstrate the potential of the hypercloud approach by integrating hyperspectral data from laboratory, tripod and unmanned aerial vehicle acquisitions to automatically map relevant lithologies and alterations associated with volcanic hosted massive sulphide (VHMS) mineralisation in the Corta Atalaya open-pit, Spain. These analyses allow quantitative and objective mineral mapping at the outcrop and open-pit scale, facilitating quantitative research and smart-mining approaches. Our results highlight the seamless sensor integration made possible with hylite and the power of data-driven mapping methods applied to hyperclouds. Significantly, we also show that random forests (RF) trained only on laboratory data from labelled hand-samples can be used to map appropriately corrected outcrop scale data.

A Methodology for the Definition of Geotechnical Mine Sectors Based on Multivariate Cluster Analysis

This paper offers a new method for the definition of geotechnical sectors in open pit mines based on multivariate cluster analysis. A geological-geotechnical data set of a manganese open pit mine was used to demonstrate the methodology. The data set consists of a survey of geological and geotechnical parameters of the rock mass, measured directly in several points of the mine, structured initially in twenty-eight variables. After the preprocessing of the data set, the clustering technique was applied using the k-Prototype algorithm. The squared Euclidean distance was used to quantify the proximity between numerical variables, and the Jaccard's coefficient of similarity was used to quantify the proximity between the nominal variables. The different cluster results obtained were validated by the multivariate analysis of variance. The identification of cluster structures was achieved by plotting them on the mine map for spatial visualization and definition of geotechnical sectors. These sectors are spatially contiguous and relatively homogeneous regarding their geological–geotechnical properties, indicated by a high density of points of the same group. It was possible to observe a great adherence of the proposed sectors to the mine geology, demonstrating the practical representativeness of the clustering results and the proposed sectors.

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.

Analysis of Mining Surveying and Mapping Geographic Information Service in the Period of Big Data

Analysis of Mining Surveying and Mapping Geographic Information Service in the Period of Big Data

Protecting miners from coal bursts during development above historic mine workings in Harlan County, KY

In order to reach a large, untapped reserve of high-quality coal, D8 Cloverlick Mine proposed to mine a corridor nearly 600 m deep beneath the Benham Spur of Black Mountain, Kentucky’s highest peak. D8 Cloverlick Mine was extracting the Owl seam, but the corridor’s route lay approximately 20 m above century-old mine workings in the C–(Darby) seam. Adding to the concern, three serious coal bursts had recently occurred in nearby Owl seam workings. Maps of the old workings seemed to indicate that the underlying C–seam had been fully extracted. However, two of the coal bursts had occurred above areas where the C–Seam was also shown as mined out. Mine Safety and Health Administration (MSHA) Technical Support therefore investigated the records of past mining to better understand the old mine maps. Underground conditions observed in current Owl seam workings were also compared with the maps of the old C–seam workings. The study concluded that the presence of hazardous underlying remnants could not be ruled out. To mitigate the burst risk, D8 Cloverlick Mine adopted a strategy of stress probe drilling. A self-propelled coal drill was used to auger 11.5-m-long, small diameter holes in advance of mining. As each hole was drilled, the cuttings were measured to detect the presence of highly stressed coal. Ultimately the crossing was successfully completed without incident.

Object-Oriented Open-Pit Mine Mapping Using Gaofen-2 Satellite Image and Convolutional Neural Network, for the Yuzhou City, China

Our society’s growing need for mineral resources brings with it the associated risk of degrading our natural environment as well as impacting on neighboring communities. To better manage this risk, especially for open-pit mine (OM) operations, new earth observation tools are required for more accurate baseline mapping and subsequent monitoring. The purpose of this paper is to propose an object-oriented open-pit mine mapping (OOMM) framework from Gaofen-2 (GF-2) high-spatial resolution satellite image (HSRSI), based on convolutional neural networks (CNNs). To better present the different land use categories (LUCs) in the OM area, a minimum heterogeneity criterion-based multi-scale segmentation method was used, while a mean area ratio method was applied to optimize the segmentation scale of each LUC. After image segmentation, three object-feature domains were obtained based on the GF-2 HSRSI: spectral, texture, and geometric features. Then, the gradient boosting decision tree and Pearson correlation coefficient were used as an object feature information reduction (FIR) method to recognize the distinguishing feature that describe open-pit mines (OMs). Finally, the CNN was used by combing the significant features to map the OM. In total, 105 OM sites were extracted from the interpretation of GF-2 HSRSIs and the boundary of each OM was validated by field work and used as inputs to evaluate the open-pit mine mapping (OMM) accuracy. The results revealed that: (1) the FIR tool made a positive impact on effective OMM; (2) by splitting the segmented objects into two groups, training and testing sets which are composed of 70% of the objects, and validation sets which are formed by the remaining 30% of the objects, then combing the selected feature subsets for training to achieve an overall accuracy (OA) of 90.13% and a Kappa coefficient (KC) of 0.88 of the whole datasets; (3) comparing the results of the state-of-the-art method, support vector machine (SVM), in OMM, the proposed framework outperformed SVM by more than 7.28% in OA, 8.64% in KC, 6.15% in producer accuracy of OM and by 9.31% in user accuracy of OM. To the best of our knowledge, it is the first time that OM information has been used through the integration of multiscale segmentation of HSRSI with the CNN to get OMM results. The proposed framework can not only provide reliable technical support for the scientific management and environmental monitoring of open pit mining areas, but also be of wide generality and be applicable to other kinds of land use mapping in mining areas using HSR images.

Application of Remote Sensing in Investigation of Geological Environment of Iron Mine

Remote sensing technology plays an important role in mine production and management, and can provide comprehensive, accurate, rich and reliable information sources for geological prospecting, regional environmental monitoring, mine mapping and other basic work[1]. In particular, remote sensing technology has the advantages of macroscopic, rapid and dynamic technology, which can meet the requirements of mine geological environment survey and monitoring. With the improvement of satellite remote sensing in spatial resolution, temporal resolution and spectral resolution, remote sensing technology has developed rapidly in recent years; Remote sensing plays a more and more important role in mine geological environment investigation[2].

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a diverse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers; while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic); while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.

Monitoring and assessment of underground climatic conditions using sensors and GIS tools

Monitoring and assessment of underground climatic conditions are necessary to identify potential hazards and initiate remedial measures in advance that otherwise would lead to disastrous conditions. This paper discusses the concept of real-time monitoring and assessment of climatic conditions in a typical underground mine using sensors and GIS tools by utilizing a laboratory scale model. Typical ventilation parameters including temperature, humidity, and gas concentrations were monitored using sensors in a laboratory setting and various ratios and indices proposed by previous researchers for interpreting fire gases and spontaneous combustion conditions are predicted from the monitored data. GIS tools were used to display this information in real-time on a mine map that would help in creating a safe and comfortable working environment for personnel and equipment working underground.

A time-lapse study using self-potential and electrical resistivity tomography methods for mapping of old mine working across railway-tracks in a part of Raniganj coalfield, India

The first coal mining in India was started in the seventeenth century with an unplanned way in Raniganj coalfield, the coal capital of India. The coalfield was possessed by several small private companies which were nationalized in 1973. A part plan of Chanch/Victoria Area, Victoria West Colliery, BCCL, Raniganj Coalfield, India, indicates an underground coal mine gallery/goaf in the Begunia Coal-seam that connects to a Light Casting Factory. It was established as “Barakar Iron Works” in the year 1881 by the government at that time. It is understood that the approaching gallery was used for coal supply to the “Light Casting Factory” by the mining of “Begunia Coal-Seam”. Mostly, these are uncharted and very poorly documented with scarce mine plans. So, the present study attempts to explore the location, depth, extension, and condition of the old working gallery through time-lapse monitoring using a combined study comprising of Self-Potential (SP) and Electrical Resistivity Tomography (ERT) techniques. Four SP profiles data along a line and thirteen ERT profiles data in four different lines were collected across the expected site with different station/electrode spacing covering different profile length in four seasons, viz., Summer (May 2016), Monsoon (August 2016), Post-Monsoon (October 2016) and Summer (April 2017). SP data were analysed using simulated annealing (SA) optimization technique for the evaluation of model parameters. The ERT data were acquired using Wenner, Dipole–Dipole and Schlumberger arrays and inversion of the combined data set was performed using the 2.5D ZZRESINV inversion software. Prominent negative SP signature with equivalent low resistivity anomaly have been delineated that possibly indicate the presence of the old mine working/mine gallery. However, the overall results of time-lapse study inferred that the ground is stable. All results are corroborated with available lithology and field photographs.

Just‐in‐time learning for the prediction of oil sands ore characteristics using GPS data in mining applications

AbstractFor the mining based oilsands industry, it is desirable to determine the quality of the ore delivered to the extraction processes in real‐time to make optimal operational decisions such as optimal ore blending to achieve maximal bitumen recovery. Currently, the industry determines the real‐time ore characteristics for any given shovel Global Positioning System (GPS) location by first determining the shovel elevation from the topological mine map and then using the determined geological coordinates in the 3D geological block model. It should be noted that the block model is built based on the widely spaced core hole samples, and it is updated only on a yearly basis due to high cost of narrower core hole sampling. Thus, the block model predictions are often inaccurate in between the core hole spacing. On the other hand, mining operations data are available that contain accurate ore characteristics information in the already mined area. Therefore, in this work, we present a just‐in‐time based data‐driven modelling strategy that utilizes the recently available mining operations data to obtain reliable ore characteristics given the GPS data. The prediction capability of ore characteristics using the proposed modelling strategy is validated at core hole locations. Further, the prediction of ore characteristics at non‐core hole points demonstrate promising results.

A geodatabase for supporting planning and management of mining activities: the case of Piedmont Region

Mining activities can contribute to sustainable development, focusing on economic, environmental, and community aspects. To this purpose, it appears essential to investigate the spatial dimension of mining operations to have a clear picture of the full range of impacts in terms of both opportunities and constrains. The paper illustrates the construction of a geodatabase for supporting planning and management of mining activities in Piedmont Region (Northern Italy). In the geodatabase, all the quarries located in the area are represented at a regional scale, distinguishing by active and inactive sites that have been further divided according to the main categories of extracted materials, namely aggregates, industrial minerals, and ornamental stones. The article discusses how the regional mining mapping can offer an integrated and interdisciplinary representation of the territory under analysis, thus supporting the decision-making processes in the context of the activities related to the Strategic Environmental Assessment of the planning of mining operations.

Performance analysis of optical and X-Ray transmitter sensors for limestone classification in the South of Brazil

The present study aims to evaluate the use of sensor-basedin the classification of marble ore. These dolomite marbles are quite particular due toa series of felsic and mafic intrusions and syngenetic metapelitic intercalation. In the present study, all the variants of ore and sterile that exists in the deposit were quantified using 3D mapping of mine’s surface. Three particles sizes were tested with the sorting equipment in the laboratory: −25+9.5mm, −50+25mm, and −70+50mm. We use indices of recovery, in terms of Calcium Carbonate Equivalent and mass balance, but also performance indices as accuracy, sensitivity, and specificity for interpretation. Recovery values were between 35 and 85% in the lowest sieve range and between 91 and 98% in the larger range in the sorting tests withan optical sensor. Accuracy is between 43 and 85% in the lowest range and 97 and 98% in the largest. Same results occurred with the sensitivity and specificity, which had better separations while increasing the particle size. It was concluded that the X-ray sensor did not perform well and was considered not applicable to the case under analysis. The optical sensor was shown to be applicable in all tested situations, with recoveries between 35% and 98%, depending on the condition and particle size of the feed. The grain size was the primary factor influencing the separation performance, implying variations of up to 30% in the accuracy indexes.

Fault interpretation using a support vector machine: A study based on 3D seismic mapping of the Zhaozhuang coal mine in the Qinshui Basin, China

The identification of small faults is of great significance to safe and efficient coal mining. In this study, a support vector machine (SVM) algorithm was applied to fault recognition problems using multi-attributes acquired from the Zhaozhuang coal mine in Shanxi Province, China. Using a fault model with varying throws, we analyzed the response characteristics of eight seismic attributes in the coalfield. Correlation and cluster analyses were used to select five attributes—variance, curvature of orientation, dip deviation, dip continuity, and maximum amplitude—as training data. The fault model revealed that small faults respond weakly to these attributes. Consequently, we developed an improved method to enhance the response of small-throw faults. A particle swarm algorithm was used to find the optimal parameters for an SVM model to identify faults in the Zhaozhuang mining region. Subsequently, experimental results showed that the SVM model produced accurate fault recognition results.

A method for real-time dynamic fleet mission planning for autonomous mining

This paper introduces a method for dynamic fleet mission planning for autonomous mining (in loop-free maps), in which a dynamic fleet mission is defined as a sequence of static fleet missions, each generated using a modified genetic algorithm. For the case of static fleet mission planning (where each vehicle completes just one mission), the proposed method is able to reliably generate, within a short optimization time, feasible fleet missions with short total duration and as few stops as possible. For the dynamic case, in simulations involving a realistic mine map, the proposed method is able to generate efficient dynamic plans such that the number of completed missions per vehicle is only slightly reduced as the number of vehicles is increased, demonstrating the favorable scaling properties of the method as well as its applicability in real-world cases.

Tremors and rock bursts hazards characteristics using the digital model of the deposit in KWK ROW Site Marcel

The article presents data arranged for the purpose of scientific and research development prepared for protective pillar stability assessment including located in it main haulage transportation, and ventilation workings in the part “C” of KWK ROW Site Marcel and the “Z” KWK ROW site Jankowice in relation to the planned and completed exploitation in this area. The article contains: characteristics of geological and mining conditions for the analyzed area, description of digital model creation of the deposit and development. The digital model of the deposit was created based on the materials obtained from KWK ROW: geological borehole profiles, digital files with plots saved in the EDN-OPN format, development and levels mining maps, and tremor record files for analyzed area in years 2015-2018. The Geolisp software operates in a CAD graphic environment and allows for automation of the most frequently performed works in the field of mining map preparation.

Procedure of Macroecological Rough-Drought Mapping of Mining and Processing Industry Zones in Russia

The methodical basis is given for the macroecological rough-draught mapping of mining and processing industry zones at a local and regional scale by the key indices and numerical values of geoecological indicators reflecting the real biota response from the results of remote sensing of vegetation cover, with description of contamination sources and production infrastructure. Judged from the functional purpose of maps, the rough-draught maps provide an assessment of current state and quality of the natural environment using the relevant standards and ratings with regard to a human being and the biota as a whole as the principal ecological subject.