The most fundamental characteristic of coal's run-of-mine (ROM) distinguishes between coal rocks and the accompanying gangue. Pu et al. used the VGG16 CNN for the classification of images presenting …
Using image recognition technology to realize coal gangue recognition is one of the development directions of intelligent fully mechanized caving mining. Aiming at the problem of low accuracy of ...
INTRODUCTION. This method of classification is in conformity with the provisions of the Joint Geological Survey-Bureau of Mines Resource Classification Agreement of November 21, 1973, covering all mineral resources and will be used in future resource/reserve studies on coal conducted by agencies of the Department of the Interior.
During the extraction and processing of coal, a large amount of solid waste, collectively known as gangue, is produced. This gangue has a low carbon content but a high ash content, accounting for approximately 15 to 20% of the total coal yield. Before coal is used, coal and gangue must be effectively separated to reduce the gangue …
Distinguish open-pit coal mine and underground coal mine pixels in Landsat images. • A new open-pit mine mapping processing flow. • Using open data and a free …
Research Highlights We investigated SVM classification use for change detection due to surface mining. Classification accuracy for Ikonos image was higher than that for Quickbird image. We have found out that mine and dump area decreased by 192.5 ha from 2004 to 2008. Forest area increased by 6.73% from 854 ha to 911 ha from 2004 …
Detection, classification and monitoring of surface mining region from satellite images have vast challenges. Surface mining industry has huge effect on social, economical, ecological, and environmental welfare of a country. Machine learning techniques are used to capture the unique characteristics of surface mining region from …
Demirel et al. [82] monitored changes in the geographic size of coal mines over four years at a spatial resolution of 1 m over an area of 5000 km 2 .
However, taking Res Net 50 model with the highest classification accuracy as an example, its classification accuracy in the mixed coal sample image dataset (83.42%) is lower than that in the anthracite image data set (90.19%), which is due to the relatively weak ability to classify coke coal and gas coal images in the mixed coal …
Fig. 1 compares the surface state differences of coal and gangue in various situations based on the proposed model. In the ideal laboratory environment, the light …
Coal mining has brought a series of environmental problems. Local government departments have issued relevant governance policies, but the premise of scientific prevention and control is to correctly grasp the actual distribution of various ground objects in the mining area. Using classification methods to extract ground object …
llustration of the spatial structure of SCMAs, consisting of the EAs, SAs, and DAs. (a) An example of a SCMA in the Gaofen-1 image (R: Band 1, G: Band 2, and B: Band 3).
DOI: 10.1016/J.MINENG.2021.107126 Corpus ID: 238688243; Performance evaluation of a deep learning based wet coal image classification @article{Liu2021PerformanceEO, title={Performance evaluation of a deep learning based wet coal image classification}, author={Yang Liu and Zelin Zhang and Xiang Liu and Wang Lei and Xia Xuhui}, …
Fig. 7 (b) is the Saraji coal mining area, the original image was collected on Feb. 1, 2018. Comparing the two images with the original images and the actual data, …
Coal mining, particularly surface mining, inevitably disturbs land. According to Landsat images acquired over Xilingol grassland in 2005, 2009 and 2015, land uses were divided into seven classes ...
can be seen in Fig. 1.2, the Aachen coal mining area, the South Limburg coal mining area, and the Campine coal mining area were all located in what is in fact one and the same coalfield.
This volume of the "Atlas of Coal Geology" provides 393 images on various subject matters related to coal deposits and coal resource utilization. The supporting text provides an introductory overview of coal exploration, mining, and coalbed methane (CBM) development, followed by discussions on various megascopic aspects of coal geology ...
Detection, classification and monitoring of surface mining region from satellite images have vast challenges. Surface mining industry has huge effect on …
Keywords Rock classication · Image processing · Patch · Machine learning · SVM · Random forest 1 Introduction The failure of coal pillar ribs is a major hazard in under …
At the same time, with the rapid development of image processing and pattern recognition, coal and gangue recognition technology based on image processing has attracted increasingly more attention in the coal mining field. 12 Based on this, Liang et al. extracted critical features from a grayscale histogram for differentiation between …
A time-series data enhancement combination process is proposed for the actual poor microseismic data, where a hybrid FCN-LSTM network structure was built, the optimal network parameters were obtained by experiments, and finally a reasonable microseISMic data classifier was obtained. Efficient and accurate classification of the …
The stages of physical and chemical changes that proceed from peat through coal are called coalification, and are classified and described by rank.In general, rank categories are defined based on recognizable changes in coal parameters.
Bituminous coal contains 45%–86% carbon. Bituminous coal in the United States is between 100 million and 300 million years old. Bituminous coal is the most abundant rank of coal found in the United States, and it accounted for about 46% of total U.S. coal production in 2022.
Coal is defined as having more than 50 percent by weight (or 70 percent by volume) carbonaceous matter produced by the compaction and hardening of altered plant remains—namely, peat deposits. Different varieties of coal arise because of differences in the kinds of plant material (coal type), degree of coalification (coal rank), and range of …
Efficient and accurate classification of the microseismic data obtained in coal mine production is of great significance for the guidance of coal mine production safety, disaster prevention and early warning. In the early stage, the classification of microseismic events relies on human experiences, which is not only inefficient but also …
This is achieved using Convolutional Neural Networks (CNN) that has proven to be capable of complex land use/land cover classification tasks. With a list of …
A fourth coal type classification is directed towards coals of lignite rank which are sometimes referred to as 'brown' coal. At very low ranks, coal types manifest themselves as color variations, ranging from dark brown to a 'pale' brown hue (George, 1982) ().In part, these color variations are the result of the ratio of preserved plant material to resinous …
Distinguish open-pit coal mine and underground coal mine pixels in Landsat images. • A new open-pit mine mapping processing flow. • Using open data and a free cloud computing platform for the whole experiment. • Shanxi's open-pit mines encroach 734.5 km 2 of land and 100.5 km 2 of cropland.
Classification of rock and coal is one preliminary problem for fully automated or intelligent mining. It assists for the automated rib stability analysis and enables the shearer to adjust the ...
Shaanxi is an important coal-producing province in China, with areas such as Yan'an, Jingbian, and Yulin being the main coal mining regions 23. 300 coal samples and 300 gangue samples were ...
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نشانی: شماره 1688، جاده شرقی گاوک، منطقه جدید پودونگ، شانگهای، چین.
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