InSAR時(shí)序監(jiān)測(cè)及應(yīng)用中的質(zhì)量控制研究
發(fā)布時(shí)間：2019-04-30 08:20
【摘要】：近年來(lái)，隨著新型高分辨率SAR衛(wèi)星的相繼發(fā)射以及時(shí)序InSAR技術(shù)的不斷進(jìn)步和發(fā)展，InSAR技術(shù)以其難以抵擋的迅猛優(yōu)勢(shì)被廣泛的應(yīng)用在地震、地面沉降、滑坡、泥石流等地質(zhì)災(zāi)害的調(diào)查監(jiān)測(cè)研究中，從而為地球物理以及大地測(cè)量學(xué)等研究領(lǐng)域提供了一種全新的動(dòng)態(tài)研究途徑，是一種極具潛力和優(yōu)勢(shì)的空間對(duì)地觀測(cè)新技術(shù)。 然而，由于InSAR數(shù)據(jù)常會(huì)受到大氣、DEM、軌道以及失相干噪聲等多項(xiàng)誤差的影響，且這些誤差往往具有多源性、復(fù)雜性、交叉性以及各種的不確定性等，使得InSAR監(jiān)測(cè)數(shù)據(jù)中的部分誤差難以消除，嚴(yán)重的影響了InSAR形變監(jiān)測(cè)結(jié)果的精度和可靠性，從而使其無(wú)法達(dá)到地表分辨單元毫米級(jí)微小形變的理論監(jiān)測(cè)精度，嚴(yán)重的制約了InSAR技術(shù)的進(jìn)一步推廣和應(yīng)用。因此如何對(duì)InSAR數(shù)據(jù)處理中的多種誤差進(jìn)行質(zhì)量控制，即對(duì)InSAR數(shù)據(jù)中的異常數(shù)據(jù)、粗差、缺失或過(guò)多的冗余信息進(jìn)行分析，是獲取高精度和高可靠性的最優(yōu)InSAR監(jiān)測(cè)結(jié)果的一個(gè)迫切需求，，是進(jìn)行InSAR監(jiān)測(cè)結(jié)果后處理和形變機(jī)理反演和預(yù)警的重要前提�；诖�，本文在對(duì)InSAR數(shù)據(jù)處理中的各種誤差特性進(jìn)行統(tǒng)計(jì)分析的基礎(chǔ)上，重點(diǎn)針對(duì)InSAR監(jiān)測(cè)數(shù)據(jù)中存在的各種誤差問(wèn)題，研究了其相應(yīng)的誤差消除方法；并從大地測(cè)量學(xué)理論出發(fā)，通過(guò)建立數(shù)學(xué)模型，運(yùn)用合理的算法來(lái)消除InSAR數(shù)據(jù)中的誤差項(xiàng)，從而保證了高精度高可靠性的InSAR監(jiān)測(cè)數(shù)據(jù)獲取。 通過(guò)研究，本文取得了以下主要?jiǎng)?chuàng)新性成果： 1）在研究InSAR相位解纏方法的基礎(chǔ)上，針對(duì)InSAR數(shù)據(jù)中存在的解纏誤差，提出了一種基于移動(dòng)開(kāi)窗多面函數(shù)法的InSAR解纏相位重構(gòu)模型：多面函數(shù)法保證了解纏相位的連續(xù)性，移動(dòng)開(kāi)窗法則保持了相位的局部細(xì)節(jié)信息。并在構(gòu)建模型時(shí)給出了顧及相干性約束和特征相位的InSAR解纏相位擬合節(jié)點(diǎn)確定方法；最后利用F統(tǒng)計(jì)對(duì)重構(gòu)模型進(jìn)行了顯著性檢驗(yàn)。 2）針對(duì)InSAR數(shù)據(jù)中存在的軌道誤差，在線性擬合估計(jì)方法的基礎(chǔ)上，提出了一種基于小波分解的抗差最小二乘方法對(duì)軌道殘余干涉條紋進(jìn)行擬合剔除。小波分解可在頻率域內(nèi)將軌道誤差與形變、大氣等其它誤差項(xiàng)分離，而具有抗差性的迭代加權(quán)最小二乘則使得多項(xiàng)式擬合模型的結(jié)果更可靠。分別采用模擬數(shù)據(jù)和西安地區(qū)的EnvisatASAR實(shí)際數(shù)據(jù)分析驗(yàn)證了算法的精度和可靠性。 3）在研究短基線集（Small BAseline Subset，SBAS）時(shí)間序列算法和小波多尺度分解（Multiscale InSAR Time Series，MInTS）算法的基礎(chǔ)上，針對(duì)InSAR時(shí)間序列處理技術(shù)中存在的相關(guān)問(wèn)題即協(xié)方差計(jì)算問(wèn)題，給出了一種融合MInTS和SBAS的綜合InSAR時(shí)間序列處理算法—MInTS-SBAS算法，該算法既可有效的解決InSAR干涉數(shù)據(jù)量，以及顧及InSAR數(shù)據(jù)的相關(guān)性的協(xié)方差問(wèn)題，還可對(duì)InSAR時(shí)間序列處理中的地形、大氣等各種誤差進(jìn)行分離。通過(guò)西安地區(qū)的實(shí)際數(shù)據(jù)，研究表明，本文給出的MInTS-SBAS算法能有效的提高InSAR時(shí)序監(jiān)測(cè)結(jié)果的精度，與GPS、水準(zhǔn)等比較具有更好的一致性和可靠性。 4）針對(duì)InSAR時(shí)間序列處理中存在的大量時(shí)域失相干噪聲，給出了一種基于Kalman濾波的InSAR時(shí)間序列誤差分析方法。研究表明，Kalman濾波算法不僅能對(duì)InSAR時(shí)序形變中的時(shí)域噪聲進(jìn)行有效消除，還可獲取優(yōu)化的線性形變速率值。 5）針對(duì)InSAR數(shù)據(jù)中存在的大量冗余數(shù)據(jù)，以及強(qiáng)噪聲和偽信號(hào)等，提出了一種顧及InSAR數(shù)據(jù)物理空間相關(guān)特性設(shè)立協(xié)方差函數(shù)的自適應(yīng)四叉樹(shù)分解InSAR數(shù)據(jù)壓縮算法。該算法能夠在形變變化明顯處進(jìn)行密集采樣，在形變變化緩慢處進(jìn)行稀疏采樣，從而能夠在較好的保留InSAR數(shù)據(jù)的形變細(xì)節(jié)信息的條件下，達(dá)到有效壓縮InSAR數(shù)據(jù)量和消除噪聲的目的。 6）以地理信息系統(tǒng)GIS為工具，在對(duì)運(yùn)城市地裂縫災(zāi)害形成機(jī)理分析的基礎(chǔ)上，分別研究了基于層次決策分析法的地裂縫敏感性分析方法和基于BP神經(jīng)網(wǎng)絡(luò)模型的地裂縫活動(dòng)強(qiáng)度預(yù)測(cè)方法，為運(yùn)城區(qū)域的城市建設(shè)和發(fā)展提供了必要的地裂縫災(zāi)害預(yù)警。
[Abstract]:In recent years, with the continuous emission of the new high-resolution SAR satellite and the progress and development of the time-series InSAR technology, InSAR technology has been widely used in the investigation and study of the geological disasters such as earthquake, land subsidence, landslide and debris flow. So as to provide a brand-new dynamic research approach for the fields of geophysical and geodesy and the like, and is a space-to-earth observation new technology with great potential and advantages. However, since the InSAR data is often affected by many errors such as the atmosphere, the DEM, the orbit, and the noise of the distortion, these errors often have the characteristics of multi-source, complexity, cross-cutting and various uncertainties, so that some of the errors in the InSAR monitoring data are difficult to eliminate. In addition, the accuracy and reliability of the InSAR deformation monitoring results are seriously affected, so that the theoretical monitoring precision of the millimeter-level micro-deformation of the surface-resolved unit can not be achieved, the further popularization and the application of the InSAR technology are seriously restricted, Therefore, it is an urgent need to obtain the optimal InSAR monitoring result with high precision and high reliability by the quality control of multiple errors in the InSAR data processing, that is, the analysis of the abnormal data, the coarse difference, the missing or too many redundant information in the InSAR data. It is important to carry out the inversion and early warning of the post-treatment and deformation mechanism of InSAR monitoring. In this paper, based on the statistical analysis of various error characteristics in the InSAR data processing, this paper focuses on the various error problems existing in the InSAR monitoring data, and studies the corresponding error elimination method; and based on the geodesy theory, the paper establishes the mathematical model. The error term in the InSAR data is eliminated by using a reasonable algorithm, so that the high-precision and high-reliability InSAR monitoring data is guaranteed. The following main innovations have been made in this paper through the research The results are as follows:1) Based on the research of the InSAR phase unwrapping method, an InSAR unwrapping phase based on the multi-face function method of the mobile window is proposed for the unwrapping error existing in the InSAR data. Bit-reconstruction model: the multi-surface function method ensures the continuity of the winding phase, and the moving window rule keeps the phase position. In this paper, an InSAR unwrapping phase fitting node determination method, which takes into account the coherence constraint and the characteristic phase, is given in the construction of the model, and the reconstruction model is finally carried out by using the F statistic. Significance test.2) On the basis of the linear fitting estimation method, an anti-difference least square method based on wavelet decomposition is proposed for the residual interference of the track on the basis of the linear fitting estimation method. The wavelet decomposition can separate the orbit error from other error items such as deformation, atmosphere and other errors in the frequency domain, and the iterative weighted least square with the resistance to the difference makes the polynomial fit the model The results of the model are more reliable. The simulation data and the EnvisatAMSAR real data analysis in Xi 'an area are used to validate the algorithm. 3) On the basis of studying the short baseline set (SBAS) time series algorithm and the wavelet multi-scale decomposition (MInTS) algorithm, the relevant questions in the processing technology of the InSAR time series In this paper, an integrated InSAR time series processing algorithm (MInTS-SBAS) based on the MInTS and the SBAS is presented, which can effectively solve the inSAR interference data and the covariance of the correlation between the InSAR data and the terrain and the atmosphere in the processing of the InSAR time series. The results show that the MInTS-SBAS algorithm presented in this paper can effectively improve the accuracy of the InSAR timing monitoring results, compared with the GPS, the level, and so on. good consistency and reliability.4) An InSA based on Kalman filtering is presented for the large amount of time-domain distortion noise present in the processing of the InSAR time series The R-time series error analysis method shows that the Kalman filtering algorithm can not only effectively eliminate the time-domain noise in the time-series deformation of the InSAR, but also can be obtained An adaptive quadtree, which takes into account the physical space-related characteristics of the InSAR data, is proposed to set up the covariance function for the large number of redundant data in the InSAR data, as well as the strong noise and the pseudo-signal. An InSAR data compression algorithm is decomposed. The algorithm can be used for dense sampling at the obvious deformation change, and the sparse sampling is performed at the slow deformation change, so that the effective compression of the InS can be achieved under the condition of better preserving the deformation detail information of the InSAR data. Based on the analysis of the formation mechanism of the crack disaster in the city of Yuncheng, based on the analysis of the formation mechanism of the crack in the city of Yuncheng, the sensitivity analysis method of the ground crack and the BP neural network based on the hierarchical decision-making method are respectively studied on the basis of the analysis of the formation mechanism of the crack in the city. The prediction method of the ground crack activity strength of the complex model, which is the city construction of the Yuncheng area.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：P225.1;P208
本文編號(hào)：2468632
[Abstract]:In recent years, with the continuous emission of the new high-resolution SAR satellite and the progress and development of the time-series InSAR technology, InSAR technology has been widely used in the investigation and study of the geological disasters such as earthquake, land subsidence, landslide and debris flow. So as to provide a brand-new dynamic research approach for the fields of geophysical and geodesy and the like, and is a space-to-earth observation new technology with great potential and advantages. However, since the InSAR data is often affected by many errors such as the atmosphere, the DEM, the orbit, and the noise of the distortion, these errors often have the characteristics of multi-source, complexity, cross-cutting and various uncertainties, so that some of the errors in the InSAR monitoring data are difficult to eliminate. In addition, the accuracy and reliability of the InSAR deformation monitoring results are seriously affected, so that the theoretical monitoring precision of the millimeter-level micro-deformation of the surface-resolved unit can not be achieved, the further popularization and the application of the InSAR technology are seriously restricted, Therefore, it is an urgent need to obtain the optimal InSAR monitoring result with high precision and high reliability by the quality control of multiple errors in the InSAR data processing, that is, the analysis of the abnormal data, the coarse difference, the missing or too many redundant information in the InSAR data. It is important to carry out the inversion and early warning of the post-treatment and deformation mechanism of InSAR monitoring. In this paper, based on the statistical analysis of various error characteristics in the InSAR data processing, this paper focuses on the various error problems existing in the InSAR monitoring data, and studies the corresponding error elimination method; and based on the geodesy theory, the paper establishes the mathematical model. The error term in the InSAR data is eliminated by using a reasonable algorithm, so that the high-precision and high-reliability InSAR monitoring data is guaranteed. The following main innovations have been made in this paper through the research The results are as follows:1) Based on the research of the InSAR phase unwrapping method, an InSAR unwrapping phase based on the multi-face function method of the mobile window is proposed for the unwrapping error existing in the InSAR data. Bit-reconstruction model: the multi-surface function method ensures the continuity of the winding phase, and the moving window rule keeps the phase position. In this paper, an InSAR unwrapping phase fitting node determination method, which takes into account the coherence constraint and the characteristic phase, is given in the construction of the model, and the reconstruction model is finally carried out by using the F statistic. Significance test.2) On the basis of the linear fitting estimation method, an anti-difference least square method based on wavelet decomposition is proposed for the residual interference of the track on the basis of the linear fitting estimation method. The wavelet decomposition can separate the orbit error from other error items such as deformation, atmosphere and other errors in the frequency domain, and the iterative weighted least square with the resistance to the difference makes the polynomial fit the model The results of the model are more reliable. The simulation data and the EnvisatAMSAR real data analysis in Xi 'an area are used to validate the algorithm. 3) On the basis of studying the short baseline set (SBAS) time series algorithm and the wavelet multi-scale decomposition (MInTS) algorithm, the relevant questions in the processing technology of the InSAR time series In this paper, an integrated InSAR time series processing algorithm (MInTS-SBAS) based on the MInTS and the SBAS is presented, which can effectively solve the inSAR interference data and the covariance of the correlation between the InSAR data and the terrain and the atmosphere in the processing of the InSAR time series. The results show that the MInTS-SBAS algorithm presented in this paper can effectively improve the accuracy of the InSAR timing monitoring results, compared with the GPS, the level, and so on. good consistency and reliability.4) An InSA based on Kalman filtering is presented for the large amount of time-domain distortion noise present in the processing of the InSAR time series The R-time series error analysis method shows that the Kalman filtering algorithm can not only effectively eliminate the time-domain noise in the time-series deformation of the InSAR, but also can be obtained An adaptive quadtree, which takes into account the physical space-related characteristics of the InSAR data, is proposed to set up the covariance function for the large number of redundant data in the InSAR data, as well as the strong noise and the pseudo-signal. An InSAR data compression algorithm is decomposed. The algorithm can be used for dense sampling at the obvious deformation change, and the sparse sampling is performed at the slow deformation change, so that the effective compression of the InS can be achieved under the condition of better preserving the deformation detail information of the InSAR data. Based on the analysis of the formation mechanism of the crack disaster in the city of Yuncheng, based on the analysis of the formation mechanism of the crack in the city of Yuncheng, the sensitivity analysis method of the ground crack and the BP neural network based on the hierarchical decision-making method are respectively studied on the basis of the analysis of the formation mechanism of the crack in the city. The prediction method of the ground crack activity strength of the complex model, which is the city construction of the Yuncheng area.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：P225.1;P208
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 趙爭(zhēng),張繼賢,張過(guò);遺傳算法在InSAR相位解纏中的應(yīng)用[J];測(cè)繪科學(xué);2002年03期
2 獨(dú)知行;陽(yáng)凡林;劉國(guó)林;溫興水;;GPS與InSAR數(shù)據(jù)融合在礦山開(kāi)采沉陷形變監(jiān)測(cè)中的應(yīng)用探討[J];測(cè)繪科學(xué);2007年01期
3 陳基偉;;PS-InSAR技術(shù)地面沉降研究與展望[J];測(cè)繪科學(xué);2008年05期
4 胡俊;丁曉利;朱建軍;李志偉;張長(zhǎng)書(shū);王興旺;;基于CRInSAR和kalman濾波的監(jiān)測(cè)地表三維形變的研究[J];測(cè)繪科學(xué);2009年02期
5 季靈運(yùn);王慶良;崔篤信;楊成生;;SAR衛(wèi)星軌道數(shù)據(jù)精度對(duì)Doris獲取DEM精度的影響研究[J];測(cè)繪科學(xué);2009年05期
6 周金國(guó);崔書(shū)珍;彭軍還;;GPS對(duì)流層延遲改正模型及其InSAR應(yīng)用研究[J];測(cè)繪通報(bào);2009年11期
7 曾琪明,解學(xué)通;基于譜運(yùn)算的復(fù)相關(guān)函數(shù)法在干涉復(fù)圖像配準(zhǔn)中的應(yīng)用[J];測(cè)繪學(xué)報(bào);2004年02期
8 羅海濱;何秀鳳;劉焱雄;;利用DInSAR和GPS綜合方法估計(jì)地表3維形變速率[J];測(cè)繪學(xué)報(bào);2008年02期
9 張菊清;劉平芝;;抗差趨勢(shì)面與正交多面函數(shù)結(jié)合擬合DEM數(shù)據(jù)[J];測(cè)繪學(xué)報(bào);2008年04期
10 孫倩;朱建軍;李志偉;尹宏杰;胡波;蔣彌;;基于信噪比的InSAR干涉圖自適應(yīng)濾波[J];測(cè)繪學(xué)報(bào);2009年05期
相關(guān)博士學(xué)位論文 前3條
1 王青松;星載干涉合成孔徑雷達(dá)高效高精度處理技術(shù)研究[D];國(guó)防科學(xué)技術(shù)大學(xué);2011年
2 趙超英;差分干涉雷達(dá)技術(shù)用于不連續(xù)形變的監(jiān)測(cè)研究[D];長(zhǎng)安大學(xué);2009年
3 楊成生;差分干涉雷達(dá)測(cè)量技術(shù)中水汽延遲改正方法研究[D];長(zhǎng)安大學(xué);2011年
本文編號(hào)：2468632
本文鏈接：http://sikaile.net/kejilunwen/dizhicehuilunwen/2468632.html
最近更新
教材專著
