Fragmentation calculation method for blast muck piles in open-pit copper mines based on three-dimensional laser point cloud data

Wang, Yongzhi; Tu, Wenlong; Li, Hui

doi:10.1016/j.jag.2021.102338

Cited by 14 publications

(11 citation statements)

References 20 publications

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“…To determine the lumps form and obtain their distribution in the rock mass, we propose to use the theory of neural networks and the recognition algorithm [34,35] shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

2021

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Mining enterprises are widely introducing digital technologies and automation is one of such tools. Granularity monitoring, namely, the size determination of rock mass pieces is a common operational component of the processes that extract minerals by open-pit mining. The article proposes an approach that, in addition to the lump size distribution, makes it possible to estimate the lump form distribution as well. To investigate the effectiveness of monitoring the form of blasted rock mass lumps, the authors conducted experiments in four stages related to the rock condition. They include geological occurrence, explosive crushing, trommelling, and mill crushing. The relationship between these stages is presented and the change in the lumps fragment form is traced. The present article proposes an informational and analytical model of the processes at mining enterprises, extracting minerals by open-pit mining, as well as an algorithm for determining the lumps form and obtaining their distribution in the rock mass.

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“…To determine the lumps form and obtain their distribution in the rock mass, we propose to use the theory of neural networks and the recognition algorithm [34,35] shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the lumps form and obtain their distribution in the rock mass, we propose to use the theory of neural networks and the recognition algorithm [34,35] shown in Figure 3. It should be noted that such an algorithm can be applied to image the entire rock mass.…”

Section: Methodsmentioning

confidence: 99%

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

2021

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“…Engin et al 22 used a 3D laser scanner to obtain a 3D view of a rock piles of about 13 cm and used morphological methods to determine the position of the rock block and to correct the surface of the rock block, and finally used nonlinear order statistical filtering and histogram analysis to determine the blasted block size distribution of the rock piles, and by comparing the results of image analysis, the results obtained using this method were proved to be sufficiently reliable and accurate. Wang et al 23 used 3D laser scanning technology to obtain blasted rock piles point cloud data, and used the Voxel Cloud Connectivity Segmentation algorithm improved by discrete features to solve the influence of point clouds on the surface of small particles of ore on block recognition, and used the Locally Convex Connected Patches algorithm improved by plane fitting to solve the problem of over-segmentation of large rock blocks, and verified the generality and accuracy of the method by comparing the number of rock blocks. The methods for rock segmentation measurement of 3D point cloud data are mainly clustering analysis or converting point cloud data into similar 2D images and then using 2D image segmentation methods 21 , 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al 23 used 3D laser scanning technology to obtain blasted rock piles point cloud data, and used the Voxel Cloud Connectivity Segmentation algorithm improved by discrete features to solve the influence of point clouds on the surface of small particles of ore on block recognition, and used the Locally Convex Connected Patches algorithm improved by plane fitting to solve the problem of over-segmentation of large rock blocks, and verified the generality and accuracy of the method by comparing the number of rock blocks. The methods for rock segmentation measurement of 3D point cloud data are mainly clustering analysis or converting point cloud data into similar 2D images and then using 2D image segmentation methods 21 , 23 , 24 . Compared with the 2D image rock segmentation measurement method, its main advantage is the high accuracy of the obtained point cloud data, but due to the expensive 3D laser scanner and the need for professional software for pre-processing, it cannot be widely used.…”

Section: Introductionmentioning

confidence: 99%

A method of blasted rock image segmentation based on improved watershed algorithm

Guo

Wang

Shi-jiao

et al. 2022

Sci Rep

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It is of great theoretical significance and practical value to establish a fast and accurate detection method for particle size of rock fragmentation. This study introduces the Phansalkar binarization method, proposes the watershed seed point marking method based on the solidity of rock block contour, and forms an adaptive watershed segmentation algorithm for blasted rock piles images based on rock block shape, which is to better solve the problem of incorrect segmentation caused by adhesion, stacking and blurred edges in blasted rock images. The algorithm first obtains the binary image after image pre-processing and performs distance transformation; then by selecting the appropriate gray threshold, the adherent part of the distance transformation image, i.e., the adherent rock blocks in the blasted rock image, is segmented and the seed points are marked based on the solidity of the contour calculated by contour detection; finally, the watershed algorithm is used to segment. The area cumulative distribution curve of the segmentation result is highly consistent with the manual segmentation, and the segmentation accuracy was above 95.65% for both limestone and granite for rock blocks with area over 100 cm2, indicating that the algorithm can accurately perform seed point marking and watershed segmentation for blasted rock image, and effectively reduce the possibility of incorrect segmentation. The method provides a new idea for particle segmentation in other fields, which has good application and promotion value.

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“…The current open-pit mine describes the spatial information of the open-pit mine and the mining status of the open pit through the step-feature line. In recent years, UAV 3D point-cloud-data acquisition and processing technology [ 1 , 2 , 3 ] has been widely used in the field of 3D modeling, and in the spatial analysis of open-pit mines after continuous and rapid development [ 4 , 5 , 6 , 7 ]. Although UAV point-cloud technology has made great contributions to mine automation, step-feature-line drawing is still mainly realized by manual visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

Mao

Wang

et al. 2022

Sensors

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Step-feature lines are one of the important geometrical elements for drawing the status quo maps of open-pit mines, and the efficient and accurate automatic extraction and updating of step-feature lines is of great significance for open-pit-mine stripping planning and analysis. In this study, an automatic extraction method of step-feature lines in an open-pit mine based on unmanned-aerial-vehicle (UAV) point-cloud data is proposed. The method is mainly used to solve the key problems, such as low accuracy, local-feature-line loss, and the discontinuity of the step-feature-line extraction method. The method first performs the regular raster resampling of the open-pit-mine cloud based on the MLS algorithm, then extracts the step-feature point set by detecting the elevation-gradient change in the resampled point cloud, further traces the step-feature control nodes by the seed-growth tracking algorithm, and finally generates smooth step-feature lines by fitting the space curve to the step-feature control nodes. The results show that the method effectively improves the accuracy of step-feature-line extraction and solves the problems of local-feature-line loss and discontinuity.

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Fragmentation calculation method for blast muck piles in open-pit copper mines based on three-dimensional laser point cloud data

Cited by 14 publications

References 20 publications

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

Digitalization in Open-Pit Mining: A New Approach in Monitoring and Control of Rock Fragmentation

A method of blasted rock image segmentation based on improved watershed algorithm

Extraction of Step-Feature Lines in Open-Pit Mines Based on UAV Point-Cloud Data

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