scholarly journals Geological hazard interpretation and hazard evaluation of open pit mines based on Unmanned Aerial Vehicle images

2021 ◽  
Vol 2006 (1) ◽  
pp. 012072
Author(s):  
Jianxiong Ma ◽  
Jiwen Liu ◽  
Jing Ming ◽  
Xuejia Sang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Open Pit ◽  
Hazard Evaluation ◽  
Geological Hazard ◽  
Open Pit Mines ◽  
Aerial Vehicle

Prediction of Blast-Induced Ground Vibration Intensity in Open-Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System

2019 ◽  
Vol 29 (2) ◽  
pp. 771-790 ◽  
Author(s):  
Xuan-Nam Bui ◽  
Yosoon Choi ◽  
Victor Atrushkevich ◽  
Hoang Nguyen ◽  
Quang-Hieu Tran ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ground Vibration ◽  
Open Pit ◽  
Open Pit Mines ◽  
Vibration Intensity ◽  
Intelligence System ◽  
Aerial Vehicle

scholarly journals Continuous Monitoring and Improvement of the Blasting Process in Open Pit Mines Using Unmanned Aerial Vehicle Techniques

2020 ◽  
Vol 12 (17) ◽  
pp. 2801
Author(s):  
Thomas Bamford ◽  
Filip Medinac ◽  
Kamran Esmaeili
Keyword(s):  
Unmanned Aerial Vehicle ◽  
High Speed ◽  
Field Experiments ◽  
Block Size ◽  
Structural Data ◽  
Open Pit ◽  
Open Pit Mines ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Technical Manpower

The current techniques used for monitoring the blasting process in open pit mines are manual, intermittent and inefficient and can expose technical manpower to hazardous conditions. This study presents the application of unmanned aerial vehicle (UAV) systems for monitoring and improving the blasting process in open pit mines. Field experiments were conducted in different open pit mines to assess rock fragmentation, blast-induced damage on final pit walls, blast dynamics and the accuracy of blastholes including production and pre-split holes. The UAV-based monitoring was done in three different stages, including pre-blasting, blasting and post-blasting. In the pre-blasting stage, pit walls were mapped to collect structural data to predict in situ block size distribution and to develop as-built pit wall digital elevation models (DEM) to assess blast-induced damage. This was followed by mapping the production blasthole patterns implemented in the mine to investigate drillhole alignment. To monitor the blasting process, a high-speed camera was mounted on the UAV to investigate blast initiation, sequencing, misfired holes and stemming ejection. In the post-blast stage, the blasted rock pile (muck pile) was monitored to estimate fragmentation and assess muck pile configuration, heave and throw. The collected aerial data provide detailed information and high spatial and temporal resolution on the quality of the blasting process and significant opportunities for process improvement. The current challenges with regards to the application of UAVs for blasting process monitoring are discussed, and recommendations for obtaining the most value out of an UAV application are provided.

scholarly journals Experimental Investigation on the Performance of DJI Phantom 4 RTK in the PPK Mode for 3D Mapping Open-Pit Mines

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Le VAN CANH ◽  
Cao XUAN CUONG ◽  
Nguyen QUOC LONG ◽  
Le THI THU HA ◽  
Tran TRUNG ANH ◽  
...  
Keyword(s):  
Coal Mines ◽  
Daily Basis ◽  
Open Pit ◽  
Surface Model ◽  
Test Field ◽  
Open Pit Mines ◽  
Ground Control Points ◽  
Aerial Vehicle ◽  
Topographic Surveys

Open-pit coal mines’ terrain is often complex and quickly and frequently changes. Therefore, topographic surveys of open-pit mines are undertaken on a daily basis. While these tasks are very time-consuming and costly with traditional methods such as total station and GNSS, the unmanned aerial vehicle (UAV) based method can be more efficient. This method is a combination of the “Structure from motion” (SfM) photogrammetry technique and UAV photogrammetry which has been widely used in topographic surveying. With an increasing popularity of RTK-enabled drones, it is becoming even more powerful method. While the important role of ground control points (GCP) in the accuracy of digital surface model (DSM) generated from images acquired by “traditional” UAVs (not RTK-enabled drones) has been proved in many previous studies, it is not clear in the case of RTK-enabled drones, especially for complex terrain in open-pit coal mines. In this study, we experimentally investigated the influence of GCP regarding its numbers and distribution on the accuracy of DSM generation from images acquired by RTK-enabled drones in open-pit coal mines. In addition, the Post Processing Kinematic (PPK) mode was executed over a test field with the same flight altitude. DSM generation was performed with several block control configurations: PPK only, PPK with one GCP, and PPK with two GCPs. Several positions of GCPs were also examined to test the optimal locations for placing GCPs to achieve accurate DSMs. The results show that the horizontal and vertical accuracy given by PPK only were 9.3 and 84.4 cm, respectively. However, when adding at least one GCP, the accuracy was significantly improved in both horizontal and vertical components, with RMSE for XY and Z ranging between 3.8 and 9.8 cm (with one GCP) and between 3.0 and 5.7 cm (with two GCPs), respectively. Also, the GCPs placed in the deep areas of the open-pit mine could ensure the cm-level accuracy.

scholarly journals Detecting drainage pitfalls in open-pit mines and haul roads using UAV-photogrammetry

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 190-195
Author(s):  
Felipe Dille Benevenuti ◽  
Rodrigo De Lemos Peroni
Keyword(s):  
Three Dimensional ◽  
Open Pit ◽  
Road Maintenance ◽  
Open Pit Mines ◽  
Cycle Times ◽  
Course Material ◽  
Aerial Vehicle ◽  
Haul Road ◽  
Elevation Model

Open-pit mines generally have operational problems such as puddling and inappropriate water flow over haul roads, particularly if located in areas with high rainfall indices. These situations increase truck cycle times, promote rapid deterioration of haul-road wearing-course material, reduce productivity due to downtime and increase road maintenance. In addition, operational costs are raised as the frequency of truck maintenance and tire failures also increase. The use of a high-resolution three-dimensional elevation model, created based on Unmanned Aerial Vehicle (UAV) photogrammetry, has been shown to be an effective technique to detect anomalies in a fast and precise way. With the proposed approach, it is possible to diagnose haul-road conditions after rainfall or to anticipate the potential occurrence of such anomalies before they become a greater problem. This diagnosis can then be used to prioritize maintenance activities in open-pit mines. To describe the methodology, a case study is presented demonstrating and validating the results obtained.

scholarly journals Coupling Satellite Images and Unmanned Aerial Vehicle Data to Monitor the Exploitation of Open-Pit Mine

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Tong Si Son ◽  
Quang Toan LE ◽  
Thi-Huyen-Ai TONG ◽  
Vu Giang NGUYEN ◽  
Phan Long VU ◽  
...  
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Satellite Images ◽  
Mining Industry ◽  
Open Pit Mine ◽  
Open Pit ◽  
Remote Sensing Technique ◽  
Vertical Range ◽  
High Resolution Satellite Images ◽  
Aerial Vehicle

Discovering the variation of an open-pit mine in vertical, horizontal, and temporal dimensions as well to characterize the stages and the trends of the exploitation are indispensable tasks which provide information supporting decision making and planning for sustainable development of the mining industry. Remote sensing technique with the advantages of multi-spatial, multi-spectral, multi-temporal resolution is a promising solution to meet the  information requirement. This study proposes an approach of coupling the high-resolution satellite images and Unmanned Aerial Vehicle (UAV) data to observe the variation of Tan An open rocky mine during its lifetime. Five satellite images with the resolution of 0.5 m acquired in 2006, 2012, 2014, 2016, 2018, and two ortho-images with 0.034 m resolution constructed from UAV photos captured in 2019, 2020 are used to make land cover maps. The analysis of land cover changes discovers 3 stages of open-pit mine exploitation consisting of unprompted exploitation, exploiting outbreak and stable exploitation corresponding to the changes in the mine. Besides, two Digital Surface Models (DSM) constructed by UAV photos are compared to calculate the elevation and volume changes. The assessment of the correlation between elevation change and land cover change indicates that the mineral exploitation is in the vertical range from 645 m to 660 m, and the exploitation trend is following the horizontal expansion rather than the deep excavation. Additionally, this experiment results in 79,422 m3 of mineral taken from the mine, and 34,022 m3 of soil used for the restoration within a year from June 2019 to June 2020.

scholarly journals Building DEM for deep open-pit coal mines using DJI Inspire 2

2020 ◽  
Vol 61 (1) ◽  
pp. 1-10
Author(s):  
Nguyen Viet Nghia ◽  
Keyword(s):  
3D Model ◽  
Coal Mines ◽  
Image Data ◽  
Maximum Error ◽  
3D Models ◽  
Reference Points ◽  
Open Pit ◽  
Open Pit Mines ◽  
Aerial Vehicle ◽  
Uav Image

Using photo data of unmanned aerial vehicle (UAV) for building 3D models has been widely used in recent years. However, building a 3D model for deep open - pit coal mines with the mean height difference between surface and bottom of mines to over 500 m, there has not been researched mentioned. The paper deals with the assessment possibility of developing 3D models for deep open - pit mines from UAV image data. To accomplish this goal, DJI's Inspire 2 flying device is used to take the photo at Coc Sau coal mine. The flying area is 4 km2, the flight altitude compared to the takeoff point on the mine surface is 250 m, the overlaying coverage is both horizontal and vertical is 70%. The average errors of the horizontal and height elements of the reference points photo correlates are 0.011 m, 0.017 m, 0.016 m, 0.049 m, and 0.051 m. The maximum error on the X-axis is - 0,025 m, and the Y-axis is 0.028 m, the maximum horizontal error is 0.034 m, the maximum error on the Z-axis is 0.095 m, and the position error is 0.095 m. These results show that the 3D model established from photographic data by Inspire 2 device has satisfied the requirements of the accuracy of establishing the mining terrain map 1: 1000 scale.

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