本文選題：機載LiDAR + 差值DEM��； 參考：《河南理工大學》2016年碩士論文

【摘要】：礦區(qū)沉陷監(jiān)測對于保障礦產資源安全開采、保護礦區(qū)安全具有重要意義,已有的地面監(jiān)測方法勞動強度大、效率低下、覆蓋范圍有限。機載LiDAR系統(tǒng)能夠快速獲取大面積地表高分辨率、高精度的三維點云數(shù)據(jù)。使用機載LiDAR技術對開采塌陷引起的地表形變進行監(jiān)測,可以較快地獲得整個區(qū)域的空間地形變化信息,從而確定礦區(qū)的沉陷分布與地表移動下沉情況。論文基于2009、2012年兩期機載LiDAR點云數(shù)據(jù),構建了研究區(qū)沉陷信息提取的技術流程。在對兩期DEM差值信息不確定性分析的基礎上,去除了非開采沉陷導致的地形變化信息,進而提取到研究區(qū)沉陷盆地的空間分布與沉陷量估算值。論文主要研究內容可概括為以下三個方面:(1)分析了機載LiDAR點云數(shù)據(jù)的特點,在采用漸進三角網算法對機載LiDAR點云作濾波處理的基礎上,基于反距離加權插值法建立了兩期高分辨率DEM數(shù)據(jù);(2)針對機載LiDAR數(shù)據(jù)的特點,在對DEM精度與不確定性分析的基礎上,利用模糊推理方法建立了基于坡度、點云密度和地表粗糙度的誤差相關表面,用于差值DEM不確定性的量化與DEM最小變化閾值的探測,進一步采用基于權重濾波窗口的貝葉斯估計判定與修正,較精確地獲取了研究區(qū)地表形變信息;(3)針對上述處理中存在地表侵蝕等導致的地形變化信息,論文在坡度相關性分析的基礎上,通過掩膜去除了非開采沉陷導致的地形變化信息,并利用多項式擬合方法對剖面數(shù)據(jù)進行處理,獲取了較為精確的沉陷盆地信息。通過實驗研究我們發(fā)現(xiàn),該方法適用于大面積開采沉陷監(jiān)測,能夠快速確定沉陷區(qū)位置,較準確獲取沉陷區(qū)的分布范圍、面積及體積等信息。但是,若地表植被覆蓋度增加,提取的DEM誤差將增大,高程差誤差也隨之增大�；跈C載LiDAR數(shù)據(jù),我們能夠較準確地提取沉陷區(qū)的位置、分布、沉陷深度以及沉陷量等信息,這為開采沉陷損害評價及開采沉陷預測提供了一種新的技術手段。
[Abstract]:Mining subsidence monitoring is of great significance for ensuring the safe mining of mineral resources and protecting the safety of mining areas. The existing surface monitoring methods are of great labor intensity, low efficiency and limited coverage.Airborne LiDAR system can quickly obtain large area surface high resolution, high accuracy 3D point cloud data.Using airborne LiDAR technology to monitor the ground deformation caused by mining collapse, the spatial topographic change information of the whole area can be obtained quickly, and the subsidence distribution and subsidence of the mining area can be determined.Based on the two issues of airborne LiDAR point cloud data in 2009 and 2012, the technical process of extracting subsidence information in the study area is constructed in this paper.Based on the uncertainty analysis of DEM difference information in two periods, the topographic variation information caused by non-mining subsidence is removed, and then the spatial distribution and subsidence estimation value of subsidence basin in the study area are extracted.In this paper, the main research contents can be summarized as follows: 1) the characteristics of airborne LiDAR point cloud data are analyzed. Based on the progressive triangular network algorithm, the airborne LiDAR point cloud is filtered.Based on the inverse distance weighted interpolation method, two periods of high resolution DEM data are established. According to the characteristics of airborne LiDAR data, based on the analysis of the accuracy and uncertainty of DEM, the slope based on fuzzy reasoning method is established.The error correlation surface of point cloud density and surface roughness is used for quantization of difference DEM uncertainty and detection of DEM minimum change threshold. Furthermore, Bayesian estimation and correction based on weight filtering window are used.In view of the topographic change information caused by the surface erosion in the above processing, the paper analyzes the correlation of slope degree.The terrain change information caused by non-mining subsidence is removed by mask, and the profile data are processed by polynomial fitting method, and more accurate information of subsidence basin is obtained.Through experimental study, we find that this method is suitable for monitoring subsidence in large area mining, and can quickly determine the location of subsidence area, and obtain the information of distribution, area and volume of subsidence area accurately.However, if the vegetation coverage is increased, the extracted DEM error will increase, and the elevation error will also increase.Based on airborne LiDAR data, we can accurately extract the location, distribution, depth and amount of subsidence, which provides a new technical means for mining subsidence damage evaluation and mining subsidence prediction.
【學位授予單位】：河南理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TD327

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