本文選題：礦山邊坡　切入點(diǎn)：變形監(jiān)測　出處：《太原理工大學(xué)》2016年博士論文

【摘要】：礦產(chǎn)資源開采引發(fā)的地表塌陷、崩塌、地裂縫和地面沉降等礦山災(zāi)害給人類的生命財產(chǎn)安全造成嚴(yán)重的威脅,集成多傳感器的自動化、智能化監(jiān)測系統(tǒng)是礦山地面災(zāi)害監(jiān)測的發(fā)展方向。以中煤平朔井工二礦邊坡(簡稱為二號井邊坡)自動化監(jiān)測系統(tǒng)作為研究案例,應(yīng)用高斯過程(Gaussian Process,簡稱GP)理論研究變形數(shù)據(jù)智能分析方法和預(yù)測模型,據(jù)之對礦山地面災(zāi)害進(jìn)行防治提供科學(xué)依據(jù)。將這種研究成果結(jié)合自動化、智能化監(jiān)測技術(shù)應(yīng)用于礦山地面災(zāi)害監(jiān)測具有廣闊的應(yīng)用前景。監(jiān)測數(shù)據(jù)的可靠性是變形監(jiān)測分析和預(yù)測的基礎(chǔ),針對原始觀測數(shù)據(jù)可能存在的異常值,提出的完整搜索估計法(Full Search Estimation,簡稱FSE)能夠?qū)崿F(xiàn)多維異常數(shù)據(jù)定位、估值和修正:根據(jù)異常數(shù)據(jù)影響驗(yàn)后方差這一基本思想,設(shè)計了多維異常數(shù)據(jù)定位搜索算法,在算法執(zhí)行的過程中能夠自動生成包含異常數(shù)據(jù)位置的定位矩陣,同時給出了動態(tài)閾值計算公式用于判斷搜索是否結(jié)束;應(yīng)用可靠性理論結(jié)合最小二乘方法推證了異常數(shù)據(jù)的估值和修正方程。分別以測量機(jī)器人異常數(shù)據(jù)探測和礦山坐標(biāo)轉(zhuǎn)換參數(shù)可靠性求解為例對FSE進(jìn)行實(shí)證分析,結(jié)果表明FSE具有較好的抗差能力。邊坡變監(jiān)測過程中受外界環(huán)境及施工作業(yè)等因素的影響有時造成數(shù)據(jù)缺失,需要應(yīng)用時空插值方法對缺失數(shù)據(jù)加以插補(bǔ)形成完整的時空序列數(shù)據(jù)。通過研究高斯過程回歸(Gaussian Process Regression,簡稱GPR)在時間域上插值的樣本數(shù)量,給出GPR在時間域上的一維插值方法和步驟,實(shí)驗(yàn)證明GPR在時間域上可以適應(yīng)線性和非線性插值;按照空間插值樣本數(shù)據(jù)選擇的一般原則,進(jìn)一步研究了基于gpr的空間插值方法;顧及監(jiān)測數(shù)據(jù)的時空關(guān)聯(lián)性,利用gpr在時間域和空間域插值輸出的驗(yàn)后方差作為定權(quán)因子,給出了基于gpr的時空插值的計算公式,并用交叉驗(yàn)證法證明了gpr時空插值的可行性。對變形區(qū)域進(jìn)行時空位移特征分析是變形數(shù)據(jù)分析的一項主要內(nèi)容,就描述監(jiān)測點(diǎn)三維位移特征常用的絕對指標(biāo)進(jìn)行了論述,但僅使用絕對指標(biāo)凸顯不出監(jiān)測點(diǎn)相對穩(wěn)定狀態(tài)。將短期位移速率和累積位移速率的比值定義為累積位移速率比作為一種相對指標(biāo),使用累積位移速率比的大小和符號可以簡單直觀的分析監(jiān)測點(diǎn)的相對穩(wěn)定狀態(tài)。通過計算分析一段時間內(nèi)的累積位移速率比,依據(jù)3σ準(zhǔn)則將監(jiān)測點(diǎn)的穩(wěn)定狀態(tài)分為四個級別,即穩(wěn)定、較穩(wěn)定、不穩(wěn)定和極不穩(wěn)定。單獨(dú)分析監(jiān)測點(diǎn)的變形特征難以從整體上掌握變形區(qū)域的時空演化趨勢和變形規(guī)律。為此,在gpr時空插值的基礎(chǔ)上研究了基于gpr變形趨勢面模型建模方法和流程,以三維累積位移量作為分析對象,構(gòu)建了二號井邊坡的變形趨勢面模型,以此來分析其變形的時空演化過程;應(yīng)用fse提取累積位移速率比的離群值,并將提取結(jié)果賦予高斯過程分類(gaussianprocessclassification,簡稱gpc)標(biāo)志,進(jìn)而給出基于gpc變形區(qū)域局部穩(wěn)定性分析方法和流程,以累積位移速率比作為分析對象,對二號井邊坡的局部穩(wěn)定性進(jìn)行整體分析。監(jiān)測點(diǎn)在發(fā)生變形的過程中經(jīng)常表現(xiàn)出明顯的非線性特征,利用gpr超參數(shù)自適應(yīng)求解、輸出結(jié)果具有概率意義的優(yōu)點(diǎn)研究了變形智能預(yù)測模型。鑒于gpr的核函數(shù)對預(yù)測性能有很大影響,應(yīng)用核函數(shù)相加方式得到與變形曲線特點(diǎn)相吻合的組合式核函數(shù)“matern32+se”;考慮到監(jiān)測數(shù)據(jù)的不斷更新和累積,為保持超參數(shù)與訓(xùn)練樣本集的一致性,研究了“遞進(jìn)~截尾式”超參數(shù)動態(tài)更新模式和gpr最佳訓(xùn)練樣本數(shù)量確定方法;在此基礎(chǔ)上建立了以時間作為輸入項的GPR監(jiān)測點(diǎn)時間驅(qū)動智能預(yù)測模型(GPR-TIPM)和以歷史數(shù)據(jù)作為輸入項的GPR監(jiān)測點(diǎn)數(shù)據(jù)驅(qū)動智能預(yù)測模型(GPR-DIPM)。分別將兩種模型應(yīng)用于二號井邊坡進(jìn)行中短期變形預(yù)測,實(shí)驗(yàn)結(jié)果表明兩種預(yù)測模型均取得了較為理想的效果,GPR-TIPM的預(yù)測性能總體上優(yōu)于GPR-DIPM。通過GPR-TIPM模型與經(jīng)典的AR(p)和GM(1,1)模型的實(shí)驗(yàn)對比分析,結(jié)果表明GPR-TIPM的預(yù)測精度明顯提高。最后部分設(shè)計了GP變形監(jiān)測數(shù)據(jù)處理軟件原型系統(tǒng)架構(gòu),并以此架構(gòu)為導(dǎo)向,根據(jù)文中提到的模型和算法用Matlab和C#語言分別實(shí)現(xiàn)了服務(wù)端近實(shí)時數(shù)據(jù)處理系統(tǒng)和GIS客戶端可視化在線分析系統(tǒng)。
[Abstract]:The surface subsidence caused by exploitation of mineral resources, collapse, ground fissure and ground subsidence of mine disaster caused a serious threat to human life and property safety, multi sensor integrated automation, intelligent monitoring system is the development direction of mining ground disaster monitoring. The slope coal underground mine two Ping Shuo (referred to as No. two well slope the automatic monitoring system) as a case study, the application of Gauss (Gaussian Process, referred to as GP) intelligent data analysis method and prediction model of deformation theory, according to the control of mining ground disaster to provide a scientific basis. The research achievements of the combination of automation, intelligent monitoring technology application in mining ground disaster monitoring has broad application prospects. The reliability of the monitoring data is the basis of the analysis and forecasting of deformation, the abnormal value of raw data may exist, proposed a complete search. Meter method (Full Search Estimation, referred to as FSE) can realize multidimensional anomaly positioning, estimation and correction: according to the basic idea of abnormal data influence posterior variance, design a multidimensional anomaly positioning search algorithm, can automatically generate the location matrix including abnormal data in the process of implementation of the location algorithm, and dynamic threshold formula to determine whether the end of the search is given; the application of the reliability theory combined with the least squares method to deduce abnormal data estimation and correction equation. By measuring robot abnormal data detection and mine coordinate conversion parameters reliability for the empirical analysis of FSE, the results show that FSE has good robust ability. The influence of outside environment and construction work the factors such as slope deformation monitoring in the process sometimes resulting in missing data, requires the application of spatio-temporal interpolation method for missing data to be inserted Fill space complete sequence data. Through the study of Gauss (Gaussian Process Regression, the regression process referred to as GPR) the number of sample interpolation in time domain, GPR is given in the time domain of the one-dimensional interpolation method and steps, experiments show that GPR can adapt to the linear and nonlinear interpolation in time domain; according to the general principles of spatial interpolation samples data selection, further study of the spatial interpolation method based on GPR; take into account the spatio-temporal correlation of monitoring data, the use of GPR in time domain and space domain interpolation output posterior variance as weighting factor, calculation formula of spatio-temporal interpolation based on GPR is proposed, and the feasibility of GPR temporal interpolation is proved by cross validation method analysis of characteristics of time and space. The displacement deformation area is a major content of deformation data analysis, describe the absolute index of three-dimensional displacement monitoring points of common features were discussed , but only use the absolute index not highlight the relatively stable state monitoring points. The short-term displacement rate and cumulative displacement rate is defined as the ratio of cumulative displacement ratio as a relative index, use the sign and magnitude of the cumulative displacement rate ratio of the monitoring points can be analyzed a simple relatively stable state. Through the analysis of cumulative displacement rate the time ratio calculation, on the basis of the 3 Sigma standards monitoring points in the steady state is divided into four levels, namely, stable, stable, unstable and very unstable. A separate analysis of deformation characteristics of monitoring points to the overall evolution of regional spatial and temporal trends of the palm grip and deformation deformation. Therefore, based on GPR the spatio-temporal interpolation of GPR deformation trend surface modeling method and process based on three-dimensional cumulative displacement as the analysis object, construct the deformation trend of No. two well slope surface model, which is used to divide Analysis of the spatial and temporal evolution of the deformation; FSE is used to extract the outlier cumulative displacement rate ratio of the value, and the result of the extraction process to Gauss classification (gaussianprocessclassification, referred to as GPC), and then gives the method and process of analysis of local stability of regional GPC deformation based on the cumulative displacement speed ratio as the research object, the local stability of slope No. two well the overall analysis. Monitoring points during a deformation process often exhibit obvious nonlinear characteristics, super parameter adaptive solution by GPR, the output and the probabilistic prediction model of intelligent deformation. In view of the kernel function of GPR has great influence on the prediction performance, application of nuclear function additive combined nuclear way function "is consistent with the characteristics of deformation curve of matern32+se; taking into account the monitoring data update and accumulation, in order to maintain the hyper parameters and training The consistency of training sample set, on the "progressive censoring ~" super dynamic parameter update mode and GPR optimal method to determine the number of training samples; set up on the basis of the GPR monitoring point in time as the time of entry driven intelligent prediction model (GPR-TIPM) and GPR based on historical data as input data driven monitoring points intelligent prediction model (GPR-DIPM) respectively. The two models applied to No. two well in the short term the slope deformation prediction, the experimental results show that the two models have achieved satisfactory effect, the prediction of GPR-TIPM overall performance is better than that of GPR-DIPM. through the GPR-TIPM model and the classic AR (P) and GM (1,1) analysis experiment comparison of model, results show that the prediction accuracy of GPR-TIPM is obviously improved. Finally a prototype software system architecture of data processing of deformation monitoring of GP, and this architecture oriented, according to the mentioned in this paper The model and algorithm use Matlab and C# language to implement the server near real-time data processing system and the GIS client visual online analysis system.

【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TD325.4

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