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Quantifying relationships between subsidence and longwall face advance using DInSAR 被引量:4

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摘要 Surface subsidence that results from longwall mining can be large magnitude and can affect significant areas.Conventional methods for subsidence monitoring include leveling,global positioning system(GPS),and photogrammetric surveys.Remote sensing techniques including,aerial LiDAR,terrestrial laser scanning,and satellite-based Differential Interferometric Synthetic Aperture Radar(DInSAR),are also used to measure deformation associated with subsidence.DInSAR data are different than data from conventional subsidence surveys.Images capture data over large areas(hundreds of kilometers),and each pixel(data point)in an image quantifies the average displacement over an area of square meters.DInSAR data can have fairly high time resolution;imaging periods typically range from weeks to months.DInSAR data can be useful to monitor subsidence sequentially over short periods.Regularly monitoring subsidence may help define if caving is progressing normally and can establish relationships between surface deformation and longwall face advance,which has potential to help quantify possible risks to mine stability.In this study,subsidence at a longwall trona mine is monitored over short periods,typically 12 days,as the longwall face is advanced through a panel.C-band interferometric wide swath synthetic aperture radar(SAR)images from the sentinel satellites are used to quantify the subsidence.The onset of subsidence occurs close in time to the beginning of the longwall face advance,and overall,the development of subsidence closely follows the longwall face advance.
出处 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2021年第1期91-94,共4页 矿业科学技术学报(英文版)
基金 Funding for this research was provided by the National Institute for Occupational Health and Safety(NIOSH).The conclusions of this work are those of the authors and do not represent the opinions or policies of NIOSH.SAR images used in the study are from Copernicus Sentinel data from 2017.Raw data were processed by the European Space Agency(ESA) SAR images were retrieved from ASF DAAC in 2019.The contributions of these organizations are gratefully acknowledged.
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