本文關(guān)鍵詞：基于改進(jìn)奇異譜分析方法提取GNSS坐標(biāo)時間序列趨勢項及季節(jié)項信息　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： GNSS坐標(biāo)時間序列 改進(jìn)奇異譜分析方法 小波分析 相移現(xiàn)象 功率譜分析

【摘要】：第一個GNSS連續(xù)觀測站在1991年1月20日建立以后,世界各國陸續(xù)建立更多的GNSS連續(xù)觀測站。并且隨著GNSS測量技術(shù)的進(jìn)步及數(shù)據(jù)處理中模型精度不斷提高,在全球范圍內(nèi)已經(jīng)累積了 20多年高精度的GNSS連續(xù)觀測數(shù)據(jù)。為研究不同時空尺度的地球物理現(xiàn)象如地球自轉(zhuǎn)、區(qū)域形變、地震形變監(jiān)測、冰后回彈、斷層滑動及全球板塊構(gòu)造運(yùn)動提供重要的數(shù)據(jù)支持。數(shù)據(jù)預(yù)處理是GNSS站點(diǎn)坐標(biāo)時間序列分析的第一步工作,其主要內(nèi)容包括三部分:粗差探測與剔除、缺損數(shù)據(jù)插值及階躍項改正。在采用小波分析和功率譜分析對GNSS站點(diǎn)坐標(biāo)時間序列進(jìn)行分析時,需要GNSS站點(diǎn)坐標(biāo)時間序列去除線性趨勢并為零均值。首先,在時域上采用小波分析從GNSS站點(diǎn)坐標(biāo)時間序列中提取的季節(jié)項進(jìn)行分析。其次,采用功率譜分析對GNSS觀測數(shù)據(jù)進(jìn)行分析時,結(jié)果發(fā)現(xiàn)在低頻處的譜能量較大且頻譜呈傾斜(斜率趨近于-1),這說明噪聲項中包含著閃爍噪聲;但隨著頻率的增加,在高頻處譜能量逐漸降低且頻譜趨于平緩(斜率趨近于0),這說明噪聲項中包含著白噪聲。結(jié)果得出的閃爍噪聲和白噪聲模型組合也與GNSS站點(diǎn)坐標(biāo)時間序列噪聲的最佳模型相符合。同時功率譜分析結(jié)果表明GNSS坐標(biāo)時間序列中含有頻率(cpy)接近1.0和2.0的季節(jié)項。在與GNSS觀測技術(shù)相關(guān)的系統(tǒng)誤差及各種地球物理效應(yīng)共同的影響下,GNSS站點(diǎn)時間序列中可能包含非長期趨勢項、階躍項、噪聲項以及振幅隨時間變化的季節(jié)項。如何將上述信息甚至一些原因不明確的其他信息從GNSS站點(diǎn)時間序列中分離出來是現(xiàn)在時間序列研究的熱點(diǎn)。使用傳統(tǒng)參數(shù)模型去解決這些復(fù)雜的問題時具有局限性。作為一種從數(shù)據(jù)自身出發(fā)的無參自適應(yīng)的奇異譜分析方法可以在沒有使用原始數(shù)據(jù)中任何地球物理現(xiàn)象先驗信息的情況下將有用信息從從受到噪聲干擾的GNSS站點(diǎn)時間序列中提取出來。為改正傳統(tǒng)的奇異譜分析方法(SSA)具有相移現(xiàn)象缺點(diǎn),本文提出一個改進(jìn)奇異譜分析方法(SSA-PD)用于擬合GNSS站點(diǎn)時間序列。通過模擬數(shù)據(jù)計算表明模擬信號和改進(jìn)奇異譜分析方法重構(gòu)信號殘差的均方根小于1.8 mm,并且改進(jìn)奇異譜分析方法擬合精度顯著優(yōu)于傳統(tǒng)奇異譜分析方法。采用IGS站點(diǎn)坐標(biāo)時間序列將小波分析方法與改進(jìn)奇異譜分析方法進(jìn)行比較,結(jié)果表明改進(jìn)奇異譜分析方法在提取年以及半年季節(jié)項要優(yōu)于小波分析方法。最后對奇異譜分析方法提取GNSS坐標(biāo)時間序列中的趨勢項和季節(jié)項進(jìn)行分析,結(jié)果表明GNSS站點(diǎn)時間序列時頻特性呈現(xiàn)出了顯著的區(qū)域性。并對形成的因素進(jìn)行定性分析。
[Abstract]:The first GNSS continuous observation stations in January 20, 1991 after the establishment of the world began to build more continuous GNSS stations. The accuracy of the model and with the continual improvement and data processing of GNSS measurement technology in the worldwide has accumulated 20 years of continuous observation data of high precision GNSS. To study the effect of different temporal and spatial scales of geophysical phenomena as the earth's rotation, regional deformation, earthquake deformation monitoring, post glacial rebound, fault slip and global plate tectonic motion to provide important data support. Data preprocessing is the first step in the job analysis coordinate time series of GNSS site, its main contents include three parts: detection and elimination of outliers, missing data interpolation and step correction in the wavelet analysis and power spectrum analysis of coordinate time series of GNSS site, GNSS site to coordinate time series and linear trend removal Zero mean. First of all, in the time domain by using wavelet analysis to extract the seasonal item from the GNSS site coordinates in time series analysis. Secondly, by analyzing the power spectrum analysis of GNSS data, the results found in the spectrum of more energy and spectrum at low frequency is inclined (slope closer to -1), indicating that noise contains a flicker noise; but with the increase of frequency, the high frequency spectrum energy decreases gradually and tends to smooth spectrum (slope near 0), indicating that the noise contained in white noise. The best result of the model is a combination of flicker noise and white noise model and the coordinate time series of GNSS site noise is consistent. At the same time, the power spectrum analysis results show that the frequency with GNSS coordinate time series in (CPY) close to 1 and 2 of the season. In correlation with GNSS observation technique and system error of various geophysical effects together under the influence of GN May contain non SS site long-term trends in time series, step, noise and the amplitude changes with time. How will the season information or other information for some reason is not clear from the GNSS site in the time series is now separated from the time series of research hot spots. With the limitations of the traditional model parameters to use to solve these complex problems. The extracted GNSS site time series as a singular spectrum analysis method non parametric adaptive starting from the data itself can not use the original data of any geophysical phenomena under the condition of prior information will be useful information from the subject to noise. In order to correct the traditional singular spectrum analysis method (SSA) with phase shift defects, this paper presents an improved method of singular spectrum analysis (SSA-PD) is used for fitting the GNSS site time series. Through the simulation data calculation table The analog signal and improve the singular spectrum analysis method to reconstruct the signal RMS residuals of less than 1.8 mm, and improved the singular spectrum analysis method fitting accuracy is significantly better than the traditional singular spectrum analysis method. Using the IGS site coordinate time series wavelet analysis method and improved singular spectrum analysis method, the results show that the improved singular spectrum analysis method in the extraction of years the first half of season and items to be superior to the wavelet analysis method. Finally the singular spectrum extraction method of GNSS coordinate time series in the trend and seasonal item analysis, the results show that the GNSS site time series time-frequency characteristics showing a significant region. And the factors for the formation of qualitative analysis.

【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P228.4

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