scholarly journals Terrain Point Cloud Assisted GB-InSAR Slope and Pavement Deformation Differentiate Method in an Open-Pit Mine

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2337 ◽  
Author(s):  
Xiangtian Zheng ◽  
Xiufeng He ◽  
Xiaolin Yang ◽  
Haitao Ma ◽  
Zhengxing Yu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Surface Deformation ◽  
Interpolation Method ◽  
Geometric Model ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Open Pit ◽  
Slope Surface ◽  
Terrain Model

Ground-based synthetic aperture radar interferometry (GB-InSAR) is a valuable tool for deformation monitoring. The 2D interferograms obtained by GB-InSAR can be integrated with a 3D terrain model to visually and accurately locate deformed areas. The process has been preliminarily realized by geometric mapping assisted by terrestrial laser scanning (TLS). However, due to the line-of-sight (LOS) deformation monitoring, shadow and layover often occur in topographically rugged areas, which makes it difficult to distinguish the deformed points on the slope between the ones on the pavement. The extant resampling and interpolation method, which is designed for solving the scale difference between the point cloud and radar pixels, does not consider the local scattering characteristics difference of slope. The scattering difference information of road surface and slope surface in the terrain model is deeply weakened. We propose a differentiated method with integrated GB-InSAR and terrain surface point cloud. Local geometric and scattering characteristics of the slope were extracted, which account for pavement and slope differentiating. The geometric model is based on a GB-InSAR system with linear repeated-pass and the topographic point cloud relative observation geometry. The scattering model is based on k-nearest neighbor (KNN) points in small patches varies as radar micro-wave incident angle changes. Simulation and a field experiment were conducted in an open-pit mine. The results show that the proposed method effectively distinguishes pavement and slope surface deformation and the abnormal area boundary is partially relieved.

scholarly journals Reduction Method for Mobile Laser Scanning Data

2018 ◽  
Vol 7 (7) ◽  
pp. 285 ◽  
Author(s):  
Wioleta Błaszczak-Bąk ◽  
Zoltan Koppanyi ◽  
Charles Toth
Keyword(s):  
Data Reduction ◽  
Point Cloud ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
New Approach ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Short Time ◽  
Mls Method

Mobile Laser Scanning (MLS) technology acquires a huge volume of data in a very short time. In many cases, it is reasonable to reduce the size of the dataset with eliminating points in such a way that the datasets, after reduction, meet specific optimization criteria. Various methods exist to decrease the size of point cloud, such as raw data reduction, Digital Terrain Model (DTM) generalization or generation of regular grid. These methods have been successfully applied on data captured from Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS), however, they have not been fully analyzed on data captured by an MLS system. The paper presents our new approach, called the Optimum Single MLS Dataset method (OptD-single-MLS), which is an algorithm for MLS data reduction. The tests were carried out in two variants: (1) for raw sensory measurements and (2) for a georeferenced 3D point cloud. We found that the OptD-single-MLS method provides a good solution in both variants; therefore, the choice of the reduction variant depends only on the user.

Local deformation monitoring using GPS in an open pit mine: initial study

GPS Solutions ◽  
2003 ◽  
Vol 7 (3) ◽  
pp. 176-185 ◽  
Author(s):  
Donghyun (Don) Kim ◽  
Richard B. Langley ◽  
Jason Bond ◽  
Adam Chrzanowski
Keyword(s):  
Initial Study ◽  
Local Deformation ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Open Pit

scholarly journals A NEW METHOD FOR DETERMINING THE DEFORMATION MONITORABLE INDICATOR OF POINT CLOUD

2016 ◽  
Vol III-7 ◽  
pp. 157-164
Author(s):  
W. Xuan ◽  
X. H. Hua ◽  
W. N. Qiu ◽  
J. G. Zou ◽  
X. J. Chen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Sampling Interval ◽  
Deformation Monitoring ◽  
Error Space ◽  
Set Up ◽  
The Relationship ◽  
Error Ellipsoid ◽  
Actual Point

With the continuous development of the terrestrial laser scanning (TLS) technique, the precision of the laser scanning has been improved which makes it possible that TLS could be used for high-precision deformation monitoring. A deformation monitorable indicator (DMI) should be determined to distinguish the deformation from the error of point cloud and plays an important role in the deformation monitoring using TLS. After the DMI determined, a scheme of the deformation monitoring case could be planned to choose a suitable instrument, set up a suitable distance and sampling interval. In this paper, the point error space and the point cloud error space are modelled firstly based on the point error ellipsoid. Secondly, the actual point error is derived by the relationship between the actual point cloud error space and the point error space. Then, the DMI is determined using the actual point error. Finally, two sets of experiments is carried out and the feasibility of the DMI is proved.

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

scholarly journals Application of Laser Scanning for Creating Geological Documentation

2018 ◽  
Vol 35 ◽  
pp. 04001
Author(s):  
Michał Buczek ◽  
Martyna Paszek ◽  
Anna Szafarczyk
Keyword(s):  
Cross Section ◽  
Point Cloud ◽  
Laser Scanning ◽  
Geophysical Methods ◽  
Open Pit ◽  
Coal Bed ◽  
Hard Coal ◽  
Test Field ◽  
Geological Cross Section ◽  
Single Station

A geological documentation is based on the analyses obtained from boreholes, geological exposures, and geophysical methods. It consists of text and graphic documents, containing drilling sections, vertical crosssections through the deposit and various types of maps. The surveying methods (such as LIDAR) can be applied in measurements of exposed rock layers, presented in appendices to the geological documentation. The laser scanning allows obtaining a complete profile of exposed surfaces in a short time and with a millimeter accuracy. The possibility of verifying the existing geological cross-section with laser scanning was tested on the example of the AGH experimental mine. The test field is built of different lithological rocks. Scans were taken from a single station, under favorable measuring conditions. The analysis of the signal intensity allowed to divide point cloud into separate geological layers. The results were compared with the geological profiles of the measured object. The same approach was applied to the data from the Vietnamese hard coal open pit mine Coc Sau. The thickness of exposed coal bed deposits and gangue layers were determined from the obtained data (point cloud) in combination with the photographs. The results were compared with the geological cross-section.

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