Activities of Musan Mine observed by Sentinel-1 Coherence Imagery

2020 ◽  
Author(s):  
Jihyun Moon ◽  
Heejeong Seo ◽  
Hoonyol Lee
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
European Space Agency ◽  
Open Pit Mine ◽  
Mining Activity ◽  
Open Pit ◽  
Open Pit Mining ◽  
Series Data ◽  
Mining Activities ◽  
Mining Site

<p>Musan mine in North Korea is the largest open-pit iron mine in Asia with the proved reserves of about 2.06 billion tons and more than 9 square kilometers. Open-pit mining is one of the surface mining technique extracting minerals from the surface. Vegetation is rarely distributed at the mining site because the topsoil is removed and the ore is mined directly from the surface. Therefore, it is effective to observe surface displacement at the mining site using Interferometric Synthetic Aperture Radar (InSAR) technology. InSAR coherence detects random surface change that measures the activity or stability of the interferometric phase of InSAR data. High coherence will be maintained on the surface where there is no movement and only surface scattering. On the other hand, the surface where there is a lot of movement and volumetric scattering has low coherence value. Therefore, using 12-days InSAR coherence images from Sentinel-1 satellites, for example, it is possible to analyze how active the open-pit mine is during the 12 days. Sentinel-1A satellite images were acquired from June 11, 2015 to May 24, 2016, followed by Sentine-1B satellite images from September 27, 2016 to April 21, 2019. A total of 102 SAR images were downloaded from European Space Agency (ESA) portal. There is a gap between May 24 and September 27, 2016 due to the transition of the data acquisition plan. Over 100 12-days coherence data were obtained by applying InSAR. Stable spots and target spots were selected through average and standard deviation of the entire coherence time series data. Coherence values include not only the mining activity but also the effects of perpendicular baseline, temporal baseline, and weather. Therefore, NDAI (Normalized Difference Activity Index) was newly defined to remove the noise and only the coherence value due to the influence of the mining activity was extracted. The degree of activities can be observed by the time series coherence and NDAI images. This study needs other references related to mining activities in order to analyze the mining activities in more detail. This method can be applied to other open-pit mine.</p>

scholarly journals GROUND DEFORMATION EXTRACTION USING VISIBLE IMAGES AND LIDAR DATA IN MINING AREA

2016 ◽  
Vol XLI-B7 ◽  
pp. 505-512 ◽  
Author(s):  
Wenmin Hu ◽  
Lixin Wu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Ground Deformation ◽  
Mining Area ◽  
Open Pit Mine ◽  
Space Distribution ◽  
Open Pit ◽  
Mining Activities ◽  
Study Results ◽  
The Mean

Recognition and extraction of mining ground deformation can help us understand the deformation process and space distribution, and estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combining with high resolution visible stereo imagery, LiDAR observation point cloud data and historical data. The DEM in large mining area is generated using high-resolution satellite stereo images, and ground deformation is obtained through time series analysis combined with historical DEM data. Ground deformation caused by mining activities are detected and analyzed to explain the link between the regional ground deformation and local deformation. A district of covering 200 km<sup>2</sup> around the West Open Pit Mine in Fushun of Liaoning province, a city located in the Northeast China is chosen as the test area for example. Regional and local ground deformation from 2010 to 2015 time series are detected and extracted with DEMs derived from ZY-3 images and LiDAR point DEMs in the case study. Results show that the mean regional deformation is 7.1 m of rising elevation with RMS 9.6 m. Deformation of rising elevation and deformation of declining elevation couple together in local area. The area of higher elevation variation is 16.3 km<sup>2</sup> and the mean rising value is 35.8 m with RMS 15.7 m, while the deformation area of lower elevation variation is 6.8 km<sup>2</sup> and the mean declining value is 17.6 m with RMS 9.3 m. Moreover, local large deformation and regional slow deformation couple together, the deformation in local mining activities has expanded to the surrounding area, a large ground fracture with declining elevation has been detected and extracted in the south of West Open Pit Mine, the mean declining elevation of which is 23.1 m and covering about 2.3 km<sup>2</sup> till 2015. The results in this paper are preliminary currently; we are making efforts to improve more precision results with invariant ground control data for validation.

scholarly journals GROUND DEFORMATION EXTRACTION USING VISIBLE IMAGES AND LIDAR DATA IN MINING AREA

2016 ◽  
Vol XLI-B7 ◽  
pp. 505-512 ◽  
Author(s):  
Wenmin Hu ◽  
Lixin Wu
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Ground Deformation ◽  
Mining Area ◽  
Open Pit Mine ◽  
Space Distribution ◽  
Open Pit ◽  
Mining Activities ◽  
Study Results ◽  
The Mean

Recognition and extraction of mining ground deformation can help us understand the deformation process and space distribution, and estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combining with high resolution visible stereo imagery, LiDAR observation point cloud data and historical data. The DEM in large mining area is generated using high-resolution satellite stereo images, and ground deformation is obtained through time series analysis combined with historical DEM data. Ground deformation caused by mining activities are detected and analyzed to explain the link between the regional ground deformation and local deformation. A district of covering 200&thinsp;km<sup>2</sup> around the West Open Pit Mine in Fushun of Liaoning province, a city located in the Northeast China is chosen as the test area for example. Regional and local ground deformation from 2010 to 2015 time series are detected and extracted with DEMs derived from ZY-3 images and LiDAR point DEMs in the case study. Results show that the mean regional deformation is 7.1&thinsp;m of rising elevation with RMS 9.6&thinsp;m. Deformation of rising elevation and deformation of declining elevation couple together in local area. The area of higher elevation variation is 16.3&thinsp;km<sup>2</sup> and the mean rising value is 35.8&thinsp;m with RMS 15.7&thinsp;m, while the deformation area of lower elevation variation is 6.8&thinsp;km<sup>2</sup> and the mean declining value is 17.6&thinsp;m with RMS 9.3&thinsp;m. Moreover, local large deformation and regional slow deformation couple together, the deformation in local mining activities has expanded to the surrounding area, a large ground fracture with declining elevation has been detected and extracted in the south of West Open Pit Mine, the mean declining elevation of which is 23.1&thinsp;m and covering about 2.3&thinsp;km<sup>2</sup> till 2015. The results in this paper are preliminary currently; we are making efforts to improve more precision results with invariant ground control data for validation.

scholarly journals Interruptions: Affective futures and uncanny presences at Giemaš, Finnmark

Polar Record ◽  
2021 ◽  
Vol 57 ◽  
Author(s):  
Marianne Elisabeth Lien
Keyword(s):  
Open Pit ◽  
Open Pit Mining ◽  
Mining Company ◽  
Mining Site ◽  
Wide Range ◽  
Migratory Movement ◽  
A Site ◽  
Affective Relations ◽  
North Norway

Abstract This paper concerns affective relations and unexpected interruptions as the planned expansion of an extractive open-pit mining site gathers momentum. The site is a mountain in Varanger, North Norway, criss-crossed by a sand-coloured meshwork of roads that are part of the current infrastructure of a quartzite quarry. Recently purchased by Chinese investors, the mining company Elkem plans a massive expansion of the operations, which will interrupt a wide range of practices and projects, including the migratory movement of reindeer, as well as their grazing patterns. Known as Giemaš amongst Sámi speakers, the mountain is also alluded to as a site of other powers, manifesting as unexpected accidents. In this article, I explore how the planned expansion evokes this contested site as more than a singular mountain, and how divergent epistemic formations interrupt the making of extractive resources in multiple ways.

scholarly journals Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G

2021 ◽  
Vol 12 (5) ◽  
pp. 827-835
Author(s):  
Mohanad F Jwaid, Husam K Salih Juboori
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Vehicles ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Transport Costs ◽  
Time Data ◽  
Nsga Ii ◽  
Scheduling System ◽  
Unmanned Vehicle

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).

Geomorphic variability and sediment dynamics in small catchments of dryland environments: Application into sand mining

2021 ◽  
Author(s):  
Abhishek kumar Singh ◽  
Nishith Bhatt
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Mining Activity ◽  
Series Data ◽  
Western India ◽  
Natural Sand ◽  
Small Catchment ◽  
Sediment Routing ◽  
Source To Sink ◽  
Small Catchments

&lt;p&gt;The understanding of the sediment routing system and source-to-sink dynamics in a catchment is vital as it helps to assess areas undergoing erosion and deposition. This is significant in catchments which undergo active mining activities especially natural sand materials. The role of climate and natural erosional processes is vital in this as mining of sand is also affected by natural replenishment. In present study, we take a case study of a small catchment of 30km length ~ Chharri, situated in arid landscape of Kachchh of western India. Using geomorphic assemblage mapped using remote sensing and field investigation, we identified natural sub-sinks (depocenters) in the Chharri river valley. The investigation was validated by studying sediment profiles of the depocentral landforms in seasonal time series (pre-monsoon and post monsoon sessions). The changes in morphology, sediments accumulations were integrated to assess the natural sand replenishment in areas which had been undergoing mining activity. Based on time series data it was deduced that the small catchments in dry-land environments, the sand production and dynamics is modulated by type of vegetation, pattern in precipitation and human intervention. The results of such source-to-sink study have long-term implications on sand replenishment, mining activity and landscape evolution of such river basins.&lt;/p&gt;

Digital twins and modeling of the transporting-technological processes for on-line dispatch control in open pit mining

2020 ◽  
pp. 55-58
Author(s):  
I. O. Temkin ◽  
◽  
A. V. Myaskov ◽  
S. A. Deryabin ◽  
U. A. Rzazade ◽  
...  
Keyword(s):  
3D Models ◽  
Transport Processes ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Russian Science ◽  
Digital Platform ◽  
Technological Processes ◽  
Digital Twins ◽  
On Line

This article discusses modern modeling technologies which open up new capabilities for creating a digital platform for open pit mining management. The specific details of the construction of an intelligent digital platform for the management of transport processes during mineral mining are discussed. A brief overview of the methods and tools for modeling technological processes in open pit mining is given. The stages to be overcome on the path of digital transformation of mines using dynamic 3D models are presented. It is proposed to use software environments of the gaming industry platforms and virtual reality systems as tools for the dynamic 3D modeling of objects. The classes of agents are introduced for the convenience of structuring the tasks to be solved. The basic functional and instrumental elements of the intelligent platform being developed at the present time are given, and also a simplified structure of the technological process control system in an open pit mine, including the prediction module, is presented. The principles of work are described, and the advantages of the specific tool for creating digital 3D models are also discussed. The results obtained in modeling a stage of a transport cycle in an open pit mine are reported. The research was supported by the Russian Science Foundation, Grant No. 19-17-00184.

scholarly journals Evaluating the Feasibility of Illegal Open-Pit Mining Identification Using Insar Coherence

2020 ◽  
Vol 12 (3) ◽  
pp. 367 ◽  
Author(s):  
Shunyao Wang ◽  
Xiaoping Lu ◽  
Zhenwei Chen ◽  
Guo Zhang ◽  
Taofeng Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Field Research ◽  
Open Pit ◽  
Open Pit Mining ◽  
Environmental Harm ◽  
Optical Remote Sensing ◽  
Mining Activities ◽  
Mining Sites ◽  
Illegal Mining ◽  
Adverse Weather

Illegal open-pit mining causes environmental harm and undermines sustainable development. Conventional monitoring approaches such as field research and unmanned aerial vehicle (UAV) imagery are time-consuming and labor-intensive, making large-scale monitoring difficult. In comparison, optical remote sensing imagery can cover large areas but is vulnerable to adverse weather conditions and is not sensitive to vertical ground changes. As open-pit excavation causes sudden changes in the scattering properties of ground objects along with dramatic vertical deformation, we evaluated the feasibility of using interferometric synthetic aperture radar (InSAR) coherence to identify illegal mining activities. Our method extracts the coherence coefficient from two SAR images taken on different dates, applies thresholding and filtering to extract a decorrelation map, and then overlays this with legal mining boundaries and optical satellite images to identify illegal mining activities. For three test cases in southwestern Inner Mongolia, China, 49 legal mining sites were correctly detected (with an accuracy of 90.74%) as well as six illegal mining sites. Ground truthing confirmed the presence of ongoing activity at one of these sites. Our study shows that InSAR coherence is suitable for the identification of mining activities, and our method provides a new approach for the detection and monitoring of illegal open-pit mining.

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