本文選題：GPS　切入點(diǎn)：高程時(shí)間序列　出處：《中國地震局地震研究所》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：二十世紀(jì)90年代初,IGS(International GPS Service for Geodynamics)的建立標(biāo)志著GPS全球定位系統(tǒng)應(yīng)用于地球物理研究工作的開始。世界上各個(gè)國家的地學(xué)研究機(jī)構(gòu)開始在不同的國家、不同的地區(qū)建立起GPS連續(xù)跟蹤站。隨著GPS連續(xù)跟蹤站的大量涌現(xiàn),積累了大量的數(shù)據(jù),使得GPS對(duì)于地球物理研究的可行性與可靠性大幅提高。 UNAVCO Facility(美國衛(wèi)星導(dǎo)航系統(tǒng)與地殼形變觀測研究大學(xué)聯(lián)合體)在全世界范圍內(nèi)建立了大量GPS連續(xù)跟蹤站,本論文利用該機(jī)構(gòu)在天山地區(qū)的若干連續(xù)站觀測數(shù)據(jù)進(jìn)行研究。 本文前面部分介紹了時(shí)間序列分析的基本理論,各種時(shí)間序列模型；介紹了時(shí)間序列分析常用的各種方法,例如最小二乘線性擬合、功率譜密度的計(jì)算以及小波多分辨率分析等。 在本文的后半部分,先篩選了數(shù)據(jù)連續(xù)性較好,缺值情況較輕的11個(gè)連續(xù)跟蹤站。通過高精度GNSS數(shù)據(jù)處理軟件-Bernese解算得到了這些連續(xù)站十年左右的高程方向的原始坐標(biāo)時(shí)間序列。然后利用最小二乘線性擬合的方法,從原始坐標(biāo)時(shí)間序列中提取出線性運(yùn)動(dòng)速度,并將線性運(yùn)動(dòng)趨勢從原始坐標(biāo)時(shí)間序列中予以剔除,獲得了無線性趨勢的殘差時(shí)間序列。通過計(jì)算發(fā)現(xiàn),唯一一個(gè)位于天山地區(qū)以外的作為參考而選擇的KRTV站在十余年間站線性運(yùn)動(dòng)的趨勢不明顯,其他10個(gè)連續(xù)觀測站的線性擬合的斜率均為正值,并且在大小上呈現(xiàn)出良好的區(qū)域一致性,說明該地區(qū)在過去十余年間存在整體的隆升趨勢。 隨后,對(duì)殘差時(shí)間序列進(jìn)行了譜分析,利用Welch方法計(jì)算功率譜的譜指數(shù)。分析結(jié)果表明,除少數(shù)站(3個(gè))的噪聲模型可以表示為白噪聲+閃爍噪聲外,其他站均需要用白噪聲+閃爍噪聲+隨機(jī)漫步噪聲的模型描述(即其譜指數(shù)均小于-1)。 最后對(duì)時(shí)間序列進(jìn)行了小波多分辨率分析,利用Coiflets小波濾波器,提取出各個(gè)跟蹤站的年周期項(xiàng)和半年周期項(xiàng)。并統(tǒng)計(jì)了各個(gè)測站周年項(xiàng)與半周年項(xiàng)的振幅以及最大值出現(xiàn)的月份。通過分析發(fā)現(xiàn),各個(gè)跟蹤站均具有明顯的季節(jié)性變化項(xiàng),并且這種變化表現(xiàn)出一定的區(qū)域性。這種季節(jié)性的變化趨勢是由各種復(fù)雜的地球物理因素引起的。因此在進(jìn)行高精度的地殼形變監(jiān)測時(shí),應(yīng)當(dāng)選擇相同季節(jié)的進(jìn)行GPS重復(fù)觀測,以排除季節(jié)性誤差的影響。
[Abstract]:In 1990s, the establishment of International GPS Service for Geodynamic marked the beginning of the application of the GPS Global Positioning system to geophysical research. GPS continuous tracking stations have been established in different regions. With the emergence of GPS continuous tracking stations a large number of data have been accumulated and the feasibility and reliability of GPS for geophysical research have been greatly improved. A large number of GPS continuous tracking stations have been established in the United States Satellite Navigation system (UNAVCO) and crustal deformation observation Research University (USSCS). In this paper, the data of several continuous stations in Tianshan region are used to study the data. In the first part of this paper, the basic theory of time series analysis and various time series models are introduced, and various methods of time series analysis, such as least square linear fitting, are introduced. Power spectral density calculation and wavelet multi-resolution analysis. In the second half of this paper, the data continuity is better. The original coordinate time series of the elevation direction of these continuous stations about ten years are obtained by using the high-precision GNSS data processing software-Bernese, and then the least square linear fitting method is used. The linear motion velocity is extracted from the original coordinate time series, and the linear motion trend is removed from the original coordinate time series. The trend of linear motion of the only KRTV station selected as a reference outside Tianshan area is not obvious in more than a decade, and the slope of linear fitting for the other 10 continuous observation stations is all positive. It also shows good regional consistency in size, which indicates that there is an overall uplift trend in this area in the past ten years. Then, the spectral analysis of the residual time series is carried out, and the spectral exponent of the power spectrum is calculated by using the Welch method. The results show that the noise model of a few stations (3) can be expressed as white noise flicker noise. The other stations need to be described by the model of white noise flicker noise random walk noise (that is its spectral index is less than -1). Finally, the wavelet multi-resolution analysis of time series is carried out, and the Coiflets wavelet filter is used. The annual and semi-annual periodic items of each tracking station are extracted, and the amplitude and the month of the maximum value of the annual and semi-annual items of each station are counted. Through analysis, it is found that each tracking station has obvious seasonal variation term. The seasonal variation is caused by a variety of complex geophysical factors. Therefore, GPS repeated observation should be carried out in the same season for high precision crustal deformation monitoring. To rule out seasonal errors.
【學(xué)位授予單位】：中國地震局地震研究所
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P228.4

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